code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a_ ( ) -> tuple[list[int], int]:
"""simple docstring"""
lowerCamelCase_ =[randint(-1000 , 1000 ) for i in range(10 )]
lowerCamelCase_ =randint(-5000 , 5000 )
return (arr, r)
a_ : Dict = make_dataset()
def a_ ( __snake_case : list[int] , __snake_case : int ) -> tuple[int, ...]:
"""simple docstring"""
for triplet in permutations(__snake_case , 3 ):
if sum(__snake_case ) == target:
return tuple(sorted(__snake_case ) )
return (0, 0, 0)
def a_ ( __snake_case : list[int] , __snake_case : int ) -> tuple[int, int, int]:
"""simple docstring"""
arr.sort()
lowerCamelCase_ =len(__snake_case )
for i in range(n - 1 ):
lowerCamelCase_, lowerCamelCase_ =i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a_ ( ) -> tuple[float, float]:
"""simple docstring"""
lowerCamelCase_ ='''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
lowerCamelCase_ ='''
triplet_sum1(*dataset)
'''
lowerCamelCase_ ='''
triplet_sum2(*dataset)
'''
lowerCamelCase_ =repeat(setup=__snake_case , stmt=__snake_case , repeat=5 , number=1_0000 )
lowerCamelCase_ =repeat(setup=__snake_case , stmt=__snake_case , repeat=5 , number=1_0000 )
return (min(__snake_case ), min(__snake_case ))
if __name__ == "__main__":
from doctest import testmod
testmod()
a_ : List[str] = solution_times()
print(F"""The time for naive implementation is {times[0]}.""")
print(F"""The time for optimized implementation is {times[1]}.""")
| 75 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =StableDiffusionInstructPixaPixPipeline
lowercase : List[Any] =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
lowercase : Optional[Any] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase : Union[str, Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase : List[Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=8, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=32, )
lowerCamelCase_ =PNDMScheduler(skip_prk_steps=lowerCAmelCase )
torch.manual_seed(0 )
lowerCamelCase_ =AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
torch.manual_seed(0 )
lowerCamelCase_ =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, )
lowerCamelCase_ =CLIPTextModel(lowerCAmelCase )
lowerCamelCase_ =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCamelCase_ ={
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowerCamelCase_ =image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase_ =Image.fromarray(np.uinta(lowerCAmelCase ) ).convert('''RGB''' )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ ='''french fries'''
lowerCamelCase_ =sd_pipe(**lowerCAmelCase, negative_prompt=lowerCAmelCase )
lowerCamelCase_ =output.images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =[inputs['''prompt''']] * 2
lowerCamelCase_ =np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
lowerCamelCase_ =torch.from_numpy(lowerCAmelCase ).unsqueeze(0 ).to(lowerCAmelCase )
lowerCamelCase_ =image / 2 + 0.5
lowerCamelCase_ =image.permute(0, 3, 1, 2 )
lowerCamelCase_ =image.repeat(2, 1, 1, 1 )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
lowerCamelCase_ =np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='''scaled_linear''' )
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
lowerCamelCase_ =[round(lowerCAmelCase, 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(lowerCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =VaeImageProcessor(do_resize=lowerCAmelCase, do_normalize=lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' ) )[0]
lowerCamelCase_ =components['''vae''']
lowerCamelCase_ =self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
lowerCamelCase_ =vae.encode(inputs[image_param] ).latent_dist.mode()
lowerCamelCase_ =pipe(**lowerCAmelCase )[0]
lowerCamelCase_ =np.abs(out - out_latents_inputs ).max()
self.assertLess(lowerCAmelCase, 1e-4, '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
lowerCamelCase_ ={
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0
def callback_fn(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ) -> None:
lowerCamelCase_ =True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowerCamelCase_ =False
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
pipe(**lowerCAmelCase, callback=lowerCAmelCase, callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowercase__ ( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCamelCase_ =inputs['''image'''].resize((504, 504) )
lowerCamelCase_ ='''timbrooks/instruct-pix2pix'''
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
lowerCAmelCase, safety_checker=lowerCAmelCase, )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =output.images[0]
lowerCamelCase_ =image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
lowerCamelCase_ =np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 75 | 1 |
'''simple docstring'''
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
lowerCamelCase_ =0
# if input_string is "aba" than new_input_string become "a|b|a"
lowerCamelCase_ =''''''
lowerCamelCase_ =''''''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__snake_case ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
lowerCamelCase_, lowerCamelCase_ =0, 0
# length[i] shows the length of palindromic substring with center i
lowerCamelCase_ =[1 for i in range(len(__snake_case ) )]
# for each character in new_string find corresponding palindromic string
lowerCamelCase_ =0
for j in range(len(__snake_case ) ):
lowerCamelCase_ =1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__snake_case )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
lowerCamelCase_ =2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
lowerCamelCase_ =j - k + 1 # noqa: E741
lowerCamelCase_ =j + k - 1
# update max_length and start position
if max_length < length[j]:
lowerCamelCase_ =length[j]
lowerCamelCase_ =j
# create that string
lowerCamelCase_ =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __UpperCamelCase :
lowercase : Union[str, Any] =XGLMConfig
lowercase : Optional[Any] ={}
lowercase : Optional[int] ='gelu'
def __init__( self, lowerCAmelCase, lowerCAmelCase=14, lowerCAmelCase=7, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=99, lowerCAmelCase=32, lowerCAmelCase=2, lowerCAmelCase=4, lowerCAmelCase=37, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=512, lowerCAmelCase=0.0_2, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =is_training
lowerCamelCase_ =use_input_mask
lowerCamelCase_ =use_labels
lowerCamelCase_ =vocab_size
lowerCamelCase_ =d_model
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =ffn_dim
lowerCamelCase_ =activation_function
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =initializer_range
lowerCamelCase_ =None
lowerCamelCase_ =0
lowerCamelCase_ =2
lowerCamelCase_ =1
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length], self.vocab_size ), clip_value_min=0, clip_value_max=3 )
lowerCamelCase_ =None
if self.use_input_mask:
lowerCamelCase_ =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ =self.get_config()
lowerCamelCase_ =floats_tensor([self.num_hidden_layers, self.num_attention_heads], 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size, d_model=self.hidden_size, num_layers=self.num_hidden_layers, attention_heads=self.num_attention_heads, ffn_dim=self.ffn_dim, activation_function=self.activation_function, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=lowerCAmelCase, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =config_and_inputs
lowerCamelCase_ ={
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : int =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowercase : Optional[Any] =(TFXGLMForCausalLM,) if is_tf_available() else ()
lowercase : Tuple =(
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowercase : Optional[Any] =False
lowercase : Optional[Any] =False
lowercase : Optional[int] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, n_embd=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =TFXGLMModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
def lowercase__ ( self ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def lowercase__ ( self, lowerCAmelCase=True ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =tf.convert_to_tensor([[2, 268, 9_865]], dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
lowerCamelCase_ =[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
# fmt: on
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist(), lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
tf.random.set_seed(0 )
lowerCamelCase_ =tokenizer('''Today is a nice day and''', return_tensors='''tf''' )
lowerCamelCase_ =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(''':/CPU:0''' ):
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, seed=[7, 0] )
lowerCamelCase_ =tokenizer.decode(output_ids[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =(
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ ='''left'''
# use different length sentences to test batching
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
lowerCamelCase_ =tokenizer(lowerCAmelCase, return_tensors='''tf''', padding=lowerCAmelCase )
lowerCamelCase_ =inputs['''input_ids''']
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, attention_mask=inputs['''attention_mask'''], max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[0], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[1], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer.batch_decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_non_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [non_padded_sentence, padded_sentence] )
| 75 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : List[str] = """▁"""
a_ : int = {"""vocab_file""": """sentencepiece.bpe.model"""}
a_ : Union[str, Any] = {
"""vocab_file""": {
"""xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
"""xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
"""xlm-roberta-large-finetuned-conll02-dutch""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll02-spanish""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll03-english""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
),
"""xlm-roberta-large-finetuned-conll03-german""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
),
}
}
a_ : int = {
"""xlm-roberta-base""": 5_12,
"""xlm-roberta-large""": 5_12,
"""xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
"""xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
"""xlm-roberta-large-finetuned-conll03-english""": 5_12,
"""xlm-roberta-large-finetuned-conll03-german""": 5_12,
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[int] =VOCAB_FILES_NAMES
lowercase : Tuple =PRETRAINED_VOCAB_FILES_MAP
lowercase : int =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : List[str] =['input_ids', 'attention_mask']
def __init__( self, lowerCAmelCase, lowerCAmelCase="<s>", lowerCAmelCase="</s>", lowerCAmelCase="</s>", lowerCAmelCase="<s>", lowerCAmelCase="<unk>", lowerCAmelCase="<pad>", lowerCAmelCase="<mask>", lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =AddedToken(lowerCAmelCase, lstrip=lowerCAmelCase, rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase, lowerCAmelCase ) else mask_token
lowerCamelCase_ ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, unk_token=lowerCAmelCase, sep_token=lowerCAmelCase, cls_token=lowerCAmelCase, pad_token=lowerCAmelCase, mask_token=lowerCAmelCase, sp_model_kwargs=self.sp_model_kwargs, **lowerCAmelCase, )
lowerCamelCase_ =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCAmelCase ) )
lowerCamelCase_ =vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
lowerCamelCase_ ={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowerCamelCase_ =1
lowerCamelCase_ =len(self.sp_model ) + self.fairseq_offset
lowerCamelCase_ ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
"""simple docstring"""
lowerCamelCase_ =self.__dict__.copy()
lowerCamelCase_ =None
lowerCamelCase_ =self.sp_model.serialized_model_proto()
return state
def __setstate__( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
lowerCamelCase_ ={}
lowerCamelCase_ =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ =[self.cls_token_id]
lowerCamelCase_ =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase, token_ids_a=lowerCAmelCase, already_has_special_tokens=lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase )) + [1]
return [1] + ([0] * len(lowerCAmelCase )) + [1, 1] + ([0] * len(lowerCAmelCase )) + [1]
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
lowerCamelCase_ =[self.sep_token_id]
lowerCamelCase_ =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={self.convert_ids_to_tokens(lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.sp_model.encode(lowerCAmelCase, out_type=lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowerCamelCase_ =self.sp_model.PieceToId(lowerCAmelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''''''.join(lowerCAmelCase ).replace(lowerCAmelCase, ''' ''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase, '''wb''' ) as fi:
lowerCamelCase_ =self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase )
return (out_vocab_file,)
| 75 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __UpperCamelCase :
@staticmethod
def lowercase__ ( *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
lowercase : int =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =object_detector(examples[0], threshold=0.0 )
lowerCamelCase_ =len(lowerCAmelCase )
self.assertGreater(lowerCAmelCase, 0 )
self.assertEqual(
lowerCAmelCase, [
{
'''score''': ANY(lowerCAmelCase ),
'''label''': ANY(lowerCAmelCase ),
'''box''': {'''xmin''': ANY(lowerCAmelCase ), '''ymin''': ANY(lowerCAmelCase ), '''xmax''': ANY(lowerCAmelCase ), '''ymax''': ANY(lowerCAmelCase )},
}
for i in range(lowerCAmelCase )
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
]
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0.2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], top_k=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
], )
| 75 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def a_ ( __snake_case : int , __snake_case : Optional[int] , __snake_case : Optional[Any]=None , **__snake_case : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[x.strip() for x in open(__snake_case ).readlines()]
lowerCamelCase_ =[x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )]
lowerCamelCase_ =calculate_rouge(__snake_case , __snake_case , **__snake_case )
if save_path is not None:
save_json(__snake_case , __snake_case , indent=__snake_case )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 75 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[int] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a_ : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a_ : Optional[int] = {"""facebook/blenderbot_small-90M""": 5_12}
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =set()
lowerCamelCase_ =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ =char
lowerCamelCase_ =set(__snake_case )
return pairs
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[int] =VOCAB_FILES_NAMES
lowercase : Tuple =PRETRAINED_VOCAB_FILES_MAP
lowercase : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Dict =['input_ids', 'attention_mask']
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase="__start__", lowerCAmelCase="__end__", lowerCAmelCase="__unk__", lowerCAmelCase="__null__", **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(unk_token=lowerCAmelCase, bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, pad_token=lowerCAmelCase, **lowerCAmelCase )
with open(lowerCAmelCase, encoding='''utf-8''' ) as vocab_handle:
lowerCamelCase_ =json.load(lowerCAmelCase )
lowerCamelCase_ ={v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase, encoding='''utf-8''' ) as merges_handle:
lowerCamelCase_ =merges_handle.read().split('''\n''' )[1:-1]
lowerCamelCase_ =[tuple(merge.split() ) for merge in merges]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={}
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.encoder )
def lowercase__ ( self ):
"""simple docstring"""
return dict(self.encoder, **self.added_tokens_encoder )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
lowerCamelCase_ =re.sub('''([.,!?()])''', R''' \1''', lowerCAmelCase )
lowerCamelCase_ =re.sub('''(\')''', R''' \1 ''', lowerCAmelCase )
lowerCamelCase_ =re.sub(R'''\s{2,}''', ''' ''', lowerCAmelCase )
if "\n" in token:
lowerCamelCase_ =token.replace('''\n''', ''' __newln__''' )
lowerCamelCase_ =token.split(''' ''' )
lowerCamelCase_ =[]
for token in tokens:
if not len(lowerCAmelCase ):
continue
lowerCamelCase_ =token.lower()
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
lowerCamelCase_ =get_pairs(lowerCAmelCase )
if not pairs:
words.append(lowerCAmelCase )
continue
while True:
lowerCamelCase_ =min(lowerCAmelCase, key=lambda lowerCAmelCase : self.bpe_ranks.get(lowerCAmelCase, float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_, lowerCamelCase_ =bigram
lowerCamelCase_ =[]
lowerCamelCase_ =0
while i < len(lowerCAmelCase ):
try:
lowerCamelCase_ =word.index(lowerCAmelCase, lowerCAmelCase )
new_word.extend(word[i:j] )
lowerCamelCase_ =j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =new_word
if len(lowerCAmelCase ) == 1:
break
else:
lowerCamelCase_ =get_pairs(lowerCAmelCase )
lowerCamelCase_ ='''@@ '''.join(lowerCAmelCase )
lowerCamelCase_ =word[:-4]
lowerCamelCase_ =word
words.append(lowerCAmelCase )
return " ".join(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =re.findall(R'''\S+\n?''', lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =token.lower()
return self.encoder.get(lowerCAmelCase, self.encoder.get(self.unk_token ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase, self.unk_token )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''' '''.join(lowerCAmelCase ).replace('''@@ ''', '''''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=lowerCAmelCase, ensure_ascii=lowerCAmelCase ) + '''\n''' )
lowerCamelCase_ =0
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
lowerCamelCase_ =token_index
writer.write(''' '''.join(lowerCAmelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 75 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Dict = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : str ='unispeech'
def __init__( self, lowerCAmelCase=32, lowerCAmelCase=768, lowerCAmelCase=12, lowerCAmelCase=12, lowerCAmelCase=3_072, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=0.0_2, lowerCAmelCase=1e-5, lowerCAmelCase="group", lowerCAmelCase="gelu", lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512), lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2), lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2), lowerCAmelCase=False, lowerCAmelCase=128, lowerCAmelCase=16, lowerCAmelCase=False, lowerCAmelCase=True, lowerCAmelCase=0.0_5, lowerCAmelCase=10, lowerCAmelCase=2, lowerCAmelCase=0.0, lowerCAmelCase=10, lowerCAmelCase=0, lowerCAmelCase=320, lowerCAmelCase=2, lowerCAmelCase=0.1, lowerCAmelCase=100, lowerCAmelCase=256, lowerCAmelCase=256, lowerCAmelCase=0.1, lowerCAmelCase="mean", lowerCAmelCase=False, lowerCAmelCase=False, lowerCAmelCase=256, lowerCAmelCase=80, lowerCAmelCase=0, lowerCAmelCase=1, lowerCAmelCase=2, lowerCAmelCase=0.5, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase, pad_token_id=lowerCAmelCase, bos_token_id=lowerCAmelCase, eos_token_id=lowerCAmelCase )
lowerCamelCase_ =hidden_size
lowerCamelCase_ =feat_extract_norm
lowerCamelCase_ =feat_extract_activation
lowerCamelCase_ =list(lowerCAmelCase )
lowerCamelCase_ =list(lowerCAmelCase )
lowerCamelCase_ =list(lowerCAmelCase )
lowerCamelCase_ =conv_bias
lowerCamelCase_ =num_conv_pos_embeddings
lowerCamelCase_ =num_conv_pos_embedding_groups
lowerCamelCase_ =len(self.conv_dim )
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =intermediate_size
lowerCamelCase_ =hidden_act
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =hidden_dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =feat_proj_dropout
lowerCamelCase_ =final_dropout
lowerCamelCase_ =layerdrop
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =initializer_range
lowerCamelCase_ =num_ctc_classes
lowerCamelCase_ =vocab_size
lowerCamelCase_ =do_stable_layer_norm
lowerCamelCase_ =use_weighted_layer_sum
lowerCamelCase_ =classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase_ =apply_spec_augment
lowerCamelCase_ =mask_time_prob
lowerCamelCase_ =mask_time_length
lowerCamelCase_ =mask_time_min_masks
lowerCamelCase_ =mask_feature_prob
lowerCamelCase_ =mask_feature_length
lowerCamelCase_ =mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowerCamelCase_ =num_codevectors_per_group
lowerCamelCase_ =num_codevector_groups
lowerCamelCase_ =contrastive_logits_temperature
lowerCamelCase_ =feat_quantizer_dropout
lowerCamelCase_ =num_negatives
lowerCamelCase_ =codevector_dim
lowerCamelCase_ =proj_codevector_dim
lowerCamelCase_ =diversity_loss_weight
# ctc loss
lowerCamelCase_ =ctc_loss_reduction
lowerCamelCase_ =ctc_zero_infinity
# pretraining loss
lowerCamelCase_ =replace_prob
@property
def lowercase__ ( self ):
"""simple docstring"""
return functools.reduce(operator.mul, self.conv_stride, 1 )
| 75 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[str] ='efficientformer'
def __init__( self, lowerCAmelCase = [3, 2, 6, 4], lowerCAmelCase = [48, 96, 224, 448], lowerCAmelCase = [True, True, True, True], lowerCAmelCase = 448, lowerCAmelCase = 32, lowerCAmelCase = 4, lowerCAmelCase = 7, lowerCAmelCase = 5, lowerCAmelCase = 8, lowerCAmelCase = 4, lowerCAmelCase = 0.0, lowerCAmelCase = 16, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 2, lowerCAmelCase = 1, lowerCAmelCase = 0.0, lowerCAmelCase = 1, lowerCAmelCase = True, lowerCAmelCase = True, lowerCAmelCase = 1e-5, lowerCAmelCase = "gelu", lowerCAmelCase = 0.0_2, lowerCAmelCase = 1e-12, lowerCAmelCase = 224, lowerCAmelCase = 1e-05, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =hidden_sizes
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =initializer_range
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =depths
lowerCamelCase_ =mlp_expansion_ratio
lowerCamelCase_ =downsamples
lowerCamelCase_ =dim
lowerCamelCase_ =key_dim
lowerCamelCase_ =attention_ratio
lowerCamelCase_ =resolution
lowerCamelCase_ =pool_size
lowerCamelCase_ =downsample_patch_size
lowerCamelCase_ =downsample_stride
lowerCamelCase_ =downsample_pad
lowerCamelCase_ =drop_path_rate
lowerCamelCase_ =num_metaad_blocks
lowerCamelCase_ =distillation
lowerCamelCase_ =use_layer_scale
lowerCamelCase_ =layer_scale_init_value
lowerCamelCase_ =image_size
lowerCamelCase_ =batch_norm_eps
| 75 | 1 |
'''simple docstring'''
a_ : Union[str, Any] = tuple[float, float, float]
a_ : int = tuple[float, float, float]
def a_ ( __snake_case : Pointad , __snake_case : Pointad ) -> Vectorad:
"""simple docstring"""
lowerCamelCase_ =end_pointa[0] - end_pointa[0]
lowerCamelCase_ =end_pointa[1] - end_pointa[1]
lowerCamelCase_ =end_pointa[2] - end_pointa[2]
return (x, y, z)
def a_ ( __snake_case : Vectorad , __snake_case : Vectorad ) -> Vectorad:
"""simple docstring"""
lowerCamelCase_ =ab[1] * ac[2] - ab[2] * ac[1] # *i
lowerCamelCase_ =(ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
lowerCamelCase_ =ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def a_ ( __snake_case : Vectorad , __snake_case : int ) -> bool:
"""simple docstring"""
return tuple(round(__snake_case , __snake_case ) for x in vector ) == (0, 0, 0)
def a_ ( __snake_case : Pointad , __snake_case : Pointad , __snake_case : Pointad , __snake_case : int = 10 ) -> bool:
"""simple docstring"""
lowerCamelCase_ =create_vector(__snake_case , __snake_case )
lowerCamelCase_ =create_vector(__snake_case , __snake_case )
return is_zero_vector(get_ad_vectors_cross(__snake_case , __snake_case ) , __snake_case )
| 75 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
a_ : Union[str, Any] = random.Random()
def a_ ( __snake_case : int , __snake_case : int=1.0 , __snake_case : Tuple=None , __snake_case : Union[str, Any]=None ) -> str:
"""simple docstring"""
if rng is None:
lowerCamelCase_ =global_rng
lowerCamelCase_ =[]
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self, lowerCAmelCase, lowerCAmelCase=7, lowerCAmelCase=400, lowerCAmelCase=2_000, lowerCAmelCase=24, lowerCAmelCase=24, lowerCAmelCase=0.0, lowerCAmelCase=16_000, lowerCAmelCase=True, lowerCAmelCase=True, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =min_seq_length
lowerCamelCase_ =max_seq_length
lowerCamelCase_ =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase_ =feature_size
lowerCamelCase_ =num_mel_bins
lowerCamelCase_ =padding_value
lowerCamelCase_ =sampling_rate
lowerCamelCase_ =return_attention_mask
lowerCamelCase_ =do_normalize
def lowercase__ ( self ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowercase__ ( self, lowerCAmelCase=False, lowerCAmelCase=False ):
"""simple docstring"""
def _flatten(lowerCAmelCase ):
return list(itertools.chain(*lowerCAmelCase ) )
if equal_length:
lowerCamelCase_ =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCamelCase_ =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Any =SpeechaTextFeatureExtractor if is_speech_available() else None
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextFeatureExtractionTester(self )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
self.assertTrue(np.all(np.mean(lowerCAmelCase, axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase, axis=0 ) - 1 ) < 1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]
# Test feature size
lowerCamelCase_ =feature_extractor(lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
lowerCamelCase_ =feature_extractor(speech_inputs[0], return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(np_speech_inputs[0], return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test batched
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase_ =[floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCamelCase_ =np.asarray(lowerCAmelCase )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding=lowerCAmelCase, max_length=lowerCAmelCase, return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, max_length=lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''max_length''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24) )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=16, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24) )
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =np.random.rand(100, 32 ).astype(np.floataa )
lowerCamelCase_ =np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
from datasets import load_dataset
lowerCamelCase_ =load_dataset('''hf-internal-testing/librispeech_asr_dummy''', '''clean''', split='''validation''' )
# automatic decoding with librispeech
lowerCamelCase_ =ds.sort('''id''' ).select(range(lowerCAmelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =np.array([
-1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
-1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
-1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
] )
# fmt: on
lowerCamelCase_ =self._load_datasamples(1 )
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape, (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30], lowerCAmelCase, atol=1e-4 ) )
| 75 | 1 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a_ : int = 6_37_81_37.0
a_ : List[str] = 6_35_67_52.31_42_45
a_ : List[Any] = 6_37_81_37
def a_ ( __snake_case : float , __snake_case : float , __snake_case : float , __snake_case : float ) -> float:
"""simple docstring"""
lowerCamelCase_ =(AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
lowerCamelCase_ =atan((1 - flattening) * tan(radians(__snake_case ) ) )
lowerCamelCase_ =atan((1 - flattening) * tan(radians(__snake_case ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
lowerCamelCase_ =haversine_distance(__snake_case , __snake_case , __snake_case , __snake_case ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
lowerCamelCase_ =(b_lata + b_lata) / 2
lowerCamelCase_ =(b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
lowerCamelCase_ =(sin(__snake_case ) ** 2) * (cos(__snake_case ) ** 2)
lowerCamelCase_ =cos(sigma / 2 ) ** 2
lowerCamelCase_ =(sigma - sin(__snake_case )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
lowerCamelCase_ =(cos(__snake_case ) ** 2) * (sin(__snake_case ) ** 2)
lowerCamelCase_ =sin(sigma / 2 ) ** 2
lowerCamelCase_ =(sigma + sin(__snake_case )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in table:
res += inp[i - 1]
return res
def a_ ( __snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
return data[1:] + data[0]
def a_ ( __snake_case : str , __snake_case : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def a_ ( __snake_case : Optional[Any] , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =int('''0b''' + data[0] + data[-1] , 2 )
lowerCamelCase_ =int('''0b''' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def a_ ( __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : int , __snake_case : Tuple , __snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =message[:4]
lowerCamelCase_ =message[4:]
lowerCamelCase_ =apply_table(__snake_case , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
lowerCamelCase_ =apply_sbox(__snake_case , temp[:4] ) # noqa: E741
lowerCamelCase_ =apply_sbox(__snake_case , temp[4:] )
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + l # noqa: E741
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + r
lowerCamelCase_ =apply_table(l + r , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
a_ : Any = input("""Enter 10 bit key: """)
a_ : Any = input("""Enter 8 bit message: """)
a_ : str = [6, 3, 7, 4, 8, 5, 10, 9]
a_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
a_ : str = [2, 4, 3, 1]
a_ : Optional[int] = [2, 6, 3, 1, 4, 8, 5, 7]
a_ : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
a_ : Union[str, Any] = [4, 1, 2, 3, 2, 3, 4, 1]
a_ : int = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
a_ : Any = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
a_ : List[Any] = apply_table(key, paa_table)
a_ : str = temp[:5]
a_ : Optional[Any] = temp[5:]
a_ : Tuple = left_shift(left)
a_ : Optional[Any] = left_shift(right)
a_ : str = apply_table(left + right, pa_table)
a_ : Optional[Any] = left_shift(left)
a_ : Tuple = left_shift(right)
a_ : Union[str, Any] = left_shift(left)
a_ : List[str] = left_shift(right)
a_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
a_ : Optional[int] = apply_table(message, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : str = temp[4:] + temp[:4]
a_ : List[str] = function(expansion, sa, sa, keya, temp)
a_ : Union[str, Any] = apply_table(temp, IP_inv)
print("""Cipher text is:""", CT)
# decryption
a_ : Optional[int] = apply_table(CT, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : int = temp[4:] + temp[:4]
a_ : int = function(expansion, sa, sa, keya, temp)
a_ : Optional[int] = apply_table(temp, IP_inv)
print("""Plain text after decypting is:""", PT)
| 75 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
a_ : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
a_ : Optional[Any] = """
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Union[PIL.Image.Image, np.ndarray]
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, ):
"""simple docstring"""
super().__init__()
self.register_modules(
prior=lowerCAmelCase, image_encoder=lowerCAmelCase, image_processor=lowerCAmelCase, scheduler=lowerCAmelCase, renderer=lowerCAmelCase, )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
if latents is None:
lowerCamelCase_ =randn_tensor(lowerCAmelCase, generator=lowerCAmelCase, device=lowerCAmelCase, dtype=lowerCAmelCase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowerCamelCase_ =latents.to(lowerCAmelCase )
lowerCamelCase_ =latents * scheduler.init_noise_sigma
return latents
def lowercase__ ( self, lowerCAmelCase=0 ):
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
lowerCamelCase_ =torch.device(f'''cuda:{gpu_id}''' )
lowerCamelCase_ =[self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCAmelCase, lowerCAmelCase )
@property
def lowercase__ ( self ):
"""simple docstring"""
if self.device != torch.device('''meta''' ) or not hasattr(self.image_encoder, '''_hf_hook''' ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(lowerCAmelCase, '''_hf_hook''' )
and hasattr(module._hf_hook, '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, ):
"""simple docstring"""
if isinstance(lowerCAmelCase, lowerCAmelCase ) and isinstance(image[0], torch.Tensor ):
lowerCamelCase_ =torch.cat(lowerCAmelCase, axis=0 ) if image[0].ndim == 4 else torch.stack(lowerCAmelCase, axis=0 )
if not isinstance(lowerCAmelCase, torch.Tensor ):
lowerCamelCase_ =self.image_processor(lowerCAmelCase, return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )
lowerCamelCase_ =image.to(dtype=self.image_encoder.dtype, device=lowerCAmelCase )
lowerCamelCase_ =self.image_encoder(lowerCAmelCase )['''last_hidden_state''']
lowerCamelCase_ =image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
lowerCamelCase_ =image_embeds.repeat_interleave(lowerCAmelCase, dim=0 )
if do_classifier_free_guidance:
lowerCamelCase_ =torch.zeros_like(lowerCAmelCase )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase_ =torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(lowerCAmelCase )
def __call__( self, lowerCAmelCase, lowerCAmelCase = 1, lowerCAmelCase = 25, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = 4.0, lowerCAmelCase = 64, lowerCAmelCase = "pil", lowerCAmelCase = True, ):
"""simple docstring"""
if isinstance(lowerCAmelCase, PIL.Image.Image ):
lowerCamelCase_ =1
elif isinstance(lowerCAmelCase, torch.Tensor ):
lowerCamelCase_ =image.shape[0]
elif isinstance(lowerCAmelCase, lowerCAmelCase ) and isinstance(image[0], (torch.Tensor, PIL.Image.Image) ):
lowerCamelCase_ =len(lowerCAmelCase )
else:
raise ValueError(
f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowerCAmelCase )}''' )
lowerCamelCase_ =self._execution_device
lowerCamelCase_ =batch_size * num_images_per_prompt
lowerCamelCase_ =guidance_scale > 1.0
lowerCamelCase_ =self._encode_image(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
# prior
self.scheduler.set_timesteps(lowerCAmelCase, device=lowerCAmelCase )
lowerCamelCase_ =self.scheduler.timesteps
lowerCamelCase_ =self.prior.config.num_embeddings
lowerCamelCase_ =self.prior.config.embedding_dim
lowerCamelCase_ =self.prepare_latents(
(batch_size, num_embeddings * embedding_dim), image_embeds.dtype, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, self.scheduler, )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
lowerCamelCase_ =latents.reshape(latents.shape[0], lowerCAmelCase, lowerCAmelCase )
for i, t in enumerate(self.progress_bar(lowerCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase_ =torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase_ =self.scheduler.scale_model_input(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =self.prior(
lowerCAmelCase, timestep=lowerCAmelCase, proj_embedding=lowerCAmelCase, ).predicted_image_embedding
# remove the variance
lowerCamelCase_, lowerCamelCase_ =noise_pred.split(
scaled_model_input.shape[2], dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
lowerCamelCase_, lowerCamelCase_ =noise_pred.chunk(2 )
lowerCamelCase_ =noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
lowerCamelCase_ =self.scheduler.step(
lowerCAmelCase, timestep=lowerCAmelCase, sample=lowerCAmelCase, ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=lowerCAmelCase )
lowerCamelCase_ =[]
for i, latent in enumerate(lowerCAmelCase ):
print()
lowerCamelCase_ =self.renderer.decode(
latent[None, :], lowerCAmelCase, size=lowerCAmelCase, ray_batch_size=4_096, n_coarse_samples=64, n_fine_samples=128, )
images.append(lowerCAmelCase )
lowerCamelCase_ =torch.stack(lowerCAmelCase )
if output_type not in ["np", "pil"]:
raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )
lowerCamelCase_ =images.cpu().numpy()
if output_type == "pil":
lowerCamelCase_ =[self.numpy_to_pil(lowerCAmelCase ) for image in images]
# Offload last model to CPU
if hasattr(self, '''final_offload_hook''' ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=lowerCAmelCase )
| 75 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
a_ : List[Any] = logging.get_logger(__name__)
a_ : Tuple = OrderedDict(
[
("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
("""beit""", """BeitFeatureExtractor"""),
("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
("""clap""", """ClapFeatureExtractor"""),
("""clip""", """CLIPFeatureExtractor"""),
("""clipseg""", """ViTFeatureExtractor"""),
("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
("""convnext""", """ConvNextFeatureExtractor"""),
("""cvt""", """ConvNextFeatureExtractor"""),
("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
("""data2vec-vision""", """BeitFeatureExtractor"""),
("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
("""deit""", """DeiTFeatureExtractor"""),
("""detr""", """DetrFeatureExtractor"""),
("""dinat""", """ViTFeatureExtractor"""),
("""donut-swin""", """DonutFeatureExtractor"""),
("""dpt""", """DPTFeatureExtractor"""),
("""encodec""", """EncodecFeatureExtractor"""),
("""flava""", """FlavaFeatureExtractor"""),
("""glpn""", """GLPNFeatureExtractor"""),
("""groupvit""", """CLIPFeatureExtractor"""),
("""hubert""", """Wav2Vec2FeatureExtractor"""),
("""imagegpt""", """ImageGPTFeatureExtractor"""),
("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
("""levit""", """LevitFeatureExtractor"""),
("""maskformer""", """MaskFormerFeatureExtractor"""),
("""mctct""", """MCTCTFeatureExtractor"""),
("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
("""mobilevit""", """MobileViTFeatureExtractor"""),
("""nat""", """ViTFeatureExtractor"""),
("""owlvit""", """OwlViTFeatureExtractor"""),
("""perceiver""", """PerceiverFeatureExtractor"""),
("""poolformer""", """PoolFormerFeatureExtractor"""),
("""regnet""", """ConvNextFeatureExtractor"""),
("""resnet""", """ConvNextFeatureExtractor"""),
("""segformer""", """SegformerFeatureExtractor"""),
("""sew""", """Wav2Vec2FeatureExtractor"""),
("""sew-d""", """Wav2Vec2FeatureExtractor"""),
("""speech_to_text""", """Speech2TextFeatureExtractor"""),
("""speecht5""", """SpeechT5FeatureExtractor"""),
("""swiftformer""", """ViTFeatureExtractor"""),
("""swin""", """ViTFeatureExtractor"""),
("""swinv2""", """ViTFeatureExtractor"""),
("""table-transformer""", """DetrFeatureExtractor"""),
("""timesformer""", """VideoMAEFeatureExtractor"""),
("""tvlt""", """TvltFeatureExtractor"""),
("""unispeech""", """Wav2Vec2FeatureExtractor"""),
("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
("""van""", """ConvNextFeatureExtractor"""),
("""videomae""", """VideoMAEFeatureExtractor"""),
("""vilt""", """ViltFeatureExtractor"""),
("""vit""", """ViTFeatureExtractor"""),
("""vit_mae""", """ViTFeatureExtractor"""),
("""vit_msn""", """ViTFeatureExtractor"""),
("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
("""wavlm""", """Wav2Vec2FeatureExtractor"""),
("""whisper""", """WhisperFeatureExtractor"""),
("""xclip""", """CLIPFeatureExtractor"""),
("""yolos""", """YolosFeatureExtractor"""),
]
)
a_ : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def a_ ( __snake_case : str ) -> Any:
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCamelCase_ =model_type_to_module_name(__snake_case )
lowerCamelCase_ =importlib.import_module(F'''.{module_name}''' , '''transformers.models''' )
try:
return getattr(__snake_case , __snake_case )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(__snake_case , '''__name__''' , __snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCamelCase_ =importlib.import_module('''transformers''' )
if hasattr(__snake_case , __snake_case ):
return getattr(__snake_case , __snake_case )
return None
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =get_file_from_repo(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(__snake_case , encoding='''utf-8''' ) as reader:
return json.load(__snake_case )
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(lowerCAmelCase )
def lowercase__ ( cls, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =kwargs.pop('''config''', lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''trust_remote_code''', lowerCAmelCase )
lowerCamelCase_ =True
lowerCamelCase_, lowerCamelCase_ =FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =config_dict.get('''feature_extractor_type''', lowerCAmelCase )
lowerCamelCase_ =None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''', {} ):
lowerCamelCase_ =config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =AutoConfig.from_pretrained(lowerCAmelCase, **lowerCAmelCase )
# It could be in `config.feature_extractor_type``
lowerCamelCase_ =getattr(lowerCAmelCase, '''feature_extractor_type''', lowerCAmelCase )
if hasattr(lowerCAmelCase, '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
lowerCamelCase_ =config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
lowerCamelCase_ =feature_extractor_class_from_name(lowerCAmelCase )
lowerCamelCase_ =feature_extractor_auto_map is not None
lowerCamelCase_ =feature_extractor_class is not None or type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
lowerCamelCase_ =resolve_trust_remote_code(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
if has_remote_code and trust_remote_code:
lowerCamelCase_ =get_class_from_dynamic_module(
lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''code_revision''', lowerCAmelCase )
if os.path.isdir(lowerCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
lowerCamelCase_ =FEATURE_EXTRACTOR_MAPPING[type(lowerCAmelCase )]
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
raise ValueError(
f'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
f'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase, lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
)
a_ : Dict = logging.getLogger(__name__)
def a_ ( __snake_case : str ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =git.Repo(search_parent_directories=__snake_case )
lowerCamelCase_ ={
'''repo_id''': str(__snake_case ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
}
with open(os.path.join(__snake_case , '''git_log.json''' ) , '''w''' ) as f:
json.dump(__snake_case , __snake_case , indent=4 )
def a_ ( __snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if params.n_gpu <= 0:
lowerCamelCase_ =0
lowerCamelCase_ =-1
lowerCamelCase_ =True
lowerCamelCase_ =False
return
assert torch.cuda.is_available()
logger.info('''Initializing GPUs''' )
if params.n_gpu > 1:
assert params.local_rank != -1
lowerCamelCase_ =int(os.environ['''WORLD_SIZE'''] )
lowerCamelCase_ =int(os.environ['''N_GPU_NODE'''] )
lowerCamelCase_ =int(os.environ['''RANK'''] )
# number of nodes / node ID
lowerCamelCase_ =params.world_size // params.n_gpu_per_node
lowerCamelCase_ =params.global_rank // params.n_gpu_per_node
lowerCamelCase_ =True
assert params.n_nodes == int(os.environ['''N_NODES'''] )
assert params.node_id == int(os.environ['''NODE_RANK'''] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowerCamelCase_ =1
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =1
lowerCamelCase_ =1
lowerCamelCase_ =False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowerCamelCase_ =params.node_id == 0 and params.local_rank == 0
lowerCamelCase_ =params.n_nodes > 1
# summary
lowerCamelCase_ =F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes )
logger.info(PREFIX + '''Node ID : %i''' % params.node_id )
logger.info(PREFIX + '''Local rank : %i''' % params.local_rank )
logger.info(PREFIX + '''World size : %i''' % params.world_size )
logger.info(PREFIX + '''GPUs per node : %i''' % params.n_gpu_per_node )
logger.info(PREFIX + '''Master : %s''' % str(params.is_master ) )
logger.info(PREFIX + '''Multi-node : %s''' % str(params.multi_node ) )
logger.info(PREFIX + '''Multi-GPU : %s''' % str(params.multi_gpu ) )
logger.info(PREFIX + '''Hostname : %s''' % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info('''Initializing PyTorch distributed''' )
torch.distributed.init_process_group(
init_method='''env://''' , backend='''nccl''' , )
def a_ ( __snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 75 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ : Optional[int] = logging.getLogger(__name__)
def a_ ( __snake_case : Union[str, Any] , __snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ =np.argmax(__snake_case , axis=1 )
return np.sum(outputs == labels )
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
with open(__snake_case , encoding='''utf_8''' ) as f:
lowerCamelCase_ =csv.reader(__snake_case )
lowerCamelCase_ =[]
next(__snake_case ) # skip the first line
for line in tqdm(__snake_case ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a_ ( __snake_case : str , __snake_case : Dict , __snake_case : List[str] , __snake_case : List[str] , __snake_case : List[Any] , __snake_case : Dict ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
for dataset in encoded_datasets:
lowerCamelCase_ =len(__snake_case )
lowerCamelCase_ =np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch, 2) , dtype=np.intaa )
lowerCamelCase_ =np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__snake_case ):
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =mc_label
lowerCamelCase_ =(input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__snake_case ) for t in all_inputs ) )
return tensor_datasets
def a_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=__snake_case , default='''openai-gpt''' , help='''pretrained model name''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' , action='''store_true''' , help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' , default=__snake_case , type=__snake_case , required=__snake_case , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument('''--train_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--eval_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--seed''' , type=__snake_case , default=42 )
parser.add_argument('''--num_train_epochs''' , type=__snake_case , default=3 )
parser.add_argument('''--train_batch_size''' , type=__snake_case , default=8 )
parser.add_argument('''--eval_batch_size''' , type=__snake_case , default=16 )
parser.add_argument('''--adam_epsilon''' , default=1e-8 , type=__snake_case , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' , type=__snake_case , default=1 )
parser.add_argument(
'''--max_steps''' , default=-1 , type=__snake_case , help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) , )
parser.add_argument(
'''--gradient_accumulation_steps''' , type=__snake_case , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--learning_rate''' , type=__snake_case , default=6.25e-5 )
parser.add_argument('''--warmup_steps''' , default=0 , type=__snake_case , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' , type=__snake_case , default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' , type=__snake_case , default=0.0_1 )
parser.add_argument('''--lm_coef''' , type=__snake_case , default=0.9 )
parser.add_argument('''--n_valid''' , type=__snake_case , default=374 )
parser.add_argument('''--server_ip''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
lowerCamelCase_ =parser.parse_args()
print(__snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowerCamelCase_ =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
lowerCamelCase_ =torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(__snake_case , __snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowerCamelCase_ =['''_start_''', '''_delimiter_''', '''_classify_''']
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(__snake_case )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(__snake_case )
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(__snake_case ) )
model.to(__snake_case )
# Load and encode the datasets
def tokenize_and_encode(__snake_case : Union[str, Any] ):
if isinstance(__snake_case , __snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__snake_case ) )
elif isinstance(__snake_case , __snake_case ):
return obj
return [tokenize_and_encode(__snake_case ) for o in obj]
logger.info('''Encoding dataset...''' )
lowerCamelCase_ =load_rocstories_dataset(args.train_dataset )
lowerCamelCase_ =load_rocstories_dataset(args.eval_dataset )
lowerCamelCase_ =(train_dataset, eval_dataset)
lowerCamelCase_ =tokenize_and_encode(__snake_case )
# Compute the max input length for the Transformer
lowerCamelCase_ =model.config.n_positions // 2 - 2
lowerCamelCase_ =max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowerCamelCase_ =min(__snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowerCamelCase_ =pre_process_datasets(__snake_case , __snake_case , __snake_case , *__snake_case )
lowerCamelCase_, lowerCamelCase_ =tensor_datasets[0], tensor_datasets[1]
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =RandomSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.train_batch_size )
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =SequentialSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowerCamelCase_ =args.max_steps
lowerCamelCase_ =args.max_steps // (len(__snake_case ) // args.gradient_accumulation_steps) + 1
else:
lowerCamelCase_ =len(__snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
lowerCamelCase_ =list(model.named_parameters() )
lowerCamelCase_ =['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
lowerCamelCase_ =[
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
lowerCamelCase_ =AdamW(__snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
lowerCamelCase_ =get_linear_schedule_with_warmup(
__snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=__snake_case )
if args.do_train:
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='''Epoch''' ):
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =tqdm(__snake_case , desc='''Training''' )
for step, batch in enumerate(__snake_case ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
lowerCamelCase_ =model(__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowerCamelCase_ =(
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowerCamelCase_ ='''Training loss: {:.2e} lr: {:.2e}'''.format(__snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowerCamelCase_ =model.module if hasattr(__snake_case , '''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
torch.save(model_to_save.state_dict() , __snake_case )
model_to_save.config.to_json_file(__snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(__snake_case )
if args.do_eval:
model.eval()
lowerCamelCase_, lowerCamelCase_ =0, 0
lowerCamelCase_, lowerCamelCase_ =0, 0
for batch in tqdm(__snake_case , desc='''Evaluating''' ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
with torch.no_grad():
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =model(
__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =mc_logits.detach().cpu().numpy()
lowerCamelCase_ =mc_labels.to('''cpu''' ).numpy()
lowerCamelCase_ =accuracy(__snake_case , __snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowerCamelCase_ =eval_loss / nb_eval_steps
lowerCamelCase_ =eval_accuracy / nb_eval_examples
lowerCamelCase_ =tr_loss / nb_tr_steps if args.do_train else None
lowerCamelCase_ ={'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
lowerCamelCase_ =os.path.join(args.output_dir , '''eval_results.txt''' )
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' , __snake_case , str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 75 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : Union[str, Any] = {
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""ClapTextConfig""",
],
"""processing_clap""": ["""ClapProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : str = [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapModel""",
"""ClapPreTrainedModel""",
"""ClapTextModel""",
"""ClapTextModelWithProjection""",
"""ClapAudioModel""",
"""ClapAudioModelWithProjection""",
]
a_ : List[Any] = ["""ClapFeatureExtractor"""]
if TYPE_CHECKING:
from .configuration_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioConfig,
ClapConfig,
ClapTextConfig,
)
from .processing_clap import ClapProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clap import ClapFeatureExtractor
from .modeling_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioModel,
ClapAudioModelWithProjection,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
else:
import sys
a_ : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 75 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ =''''''
lowerCamelCase_ =[]
lowerCamelCase_ =0
lowerCamelCase_ =256
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =cva.imread(lowerCAmelCase, 0 )
lowerCamelCase_ =copy.deepcopy(self.img )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =plt.hist(self.img.ravel(), 256, [0, 256], label='''x''' )
lowerCamelCase_ =np.sum(lowerCAmelCase )
for i in range(len(lowerCAmelCase ) ):
lowerCamelCase_ =x[i] / self.k
self.sk += prk
lowerCamelCase_ =(self.L - 1) * self.sk
if self.rem != 0:
lowerCamelCase_ =int(last % last )
lowerCamelCase_ =int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowerCAmelCase )
lowerCamelCase_ =int(np.ma.count(self.img ) / self.img[1].size )
lowerCamelCase_ =self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCamelCase_ =self.img[j][i]
if num != self.last_list[num]:
lowerCamelCase_ =self.last_list[num]
cva.imwrite('''output_data/output.jpg''', self.img )
def lowercase__ ( self ):
"""simple docstring"""
plt.hist(self.img.ravel(), 256, [0, 256] )
def lowercase__ ( self ):
"""simple docstring"""
cva.imshow('''Output-Image''', self.img )
cva.imshow('''Input-Image''', self.original_image )
cva.waitKey(5_000 )
cva.destroyAllWindows()
if __name__ == "__main__":
a_ : str = os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
a_ : Optional[Any] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 75 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return 0.0
def a_ ( __snake_case : np.ndarray , __snake_case : int ) -> tuple[int | float, int | float]:
"""simple docstring"""
lowerCamelCase_ =min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
lowerCamelCase_ =max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.abs(np.fft.fft(__snake_case ) )
lowerCamelCase_ =20 * np.logaa(__snake_case )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
lowerCamelCase_ =get_bounds(__snake_case , __snake_case )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(__snake_case )
plt.show()
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.angle(np.fft.fft(__snake_case ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(__snake_case , -2 * pi ) )
plt.show()
| 75 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Any = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
# No specific FOR_XXX available yet
def __call__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return super().__call__(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ={}
if "candidate_labels" in kwargs:
lowerCamelCase_ =kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
lowerCamelCase_ =kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(lowerCAmelCase, lowerCAmelCase ):
if audio.startswith('''http://''' ) or audio.startswith('''https://''' ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
lowerCamelCase_ =requests.get(lowerCAmelCase ).content
else:
with open(lowerCAmelCase, '''rb''' ) as f:
lowerCamelCase_ =f.read()
if isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =ffmpeg_read(lowerCAmelCase, self.feature_extractor.sampling_rate )
if not isinstance(lowerCAmelCase, np.ndarray ):
raise ValueError('''We expect a numpy ndarray as input''' )
if len(audio.shape ) != 1:
raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''' )
lowerCamelCase_ =self.feature_extractor(
[audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='''pt''' )
lowerCamelCase_ =candidate_labels
lowerCamelCase_ =[hypothesis_template.format(lowerCAmelCase ) for x in candidate_labels]
lowerCamelCase_ =self.tokenizer(lowerCAmelCase, return_tensors=self.framework, padding=lowerCAmelCase )
lowerCamelCase_ =[text_inputs]
return inputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_inputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0], lowerCAmelCase ):
lowerCamelCase_ =text_inputs[0]
else:
# Batching case.
lowerCamelCase_ =text_inputs[0][0]
lowerCamelCase_ =self.model(**lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ ={
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_audio,
}
return model_outputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_outputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_outputs['''logits'''][0]
if self.framework == "pt":
lowerCamelCase_ =logits.softmax(dim=0 )
lowerCamelCase_ =probs.tolist()
else:
raise ValueError('''`tf` framework not supported.''' )
lowerCamelCase_ =[
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(lowerCAmelCase, lowerCAmelCase ), key=lambda lowerCAmelCase : -x[0] )
]
return result
| 75 | 1 |
'''simple docstring'''
from PIL import Image
def a_ ( __snake_case : Image , __snake_case : int ) -> Image:
"""simple docstring"""
lowerCamelCase_ =(259 * (level + 255)) / (255 * (259 - level))
def contrast(__snake_case : int ) -> int:
return int(128 + factor * (c - 128) )
return img.point(__snake_case )
if __name__ == "__main__":
# Load image
with Image.open("""image_data/lena.jpg""") as img:
# Change contrast to 170
a_ : List[str] = change_contrast(img, 1_70)
cont_img.save("""image_data/lena_high_contrast.png""", format="""png""")
| 75 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
a_ : Optional[Any] = 5
a_ : str = 10
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : int =SpeechaTextTokenizer
lowercase : int =False
lowercase : List[str] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =sp.SentencePieceProcessor()
spm_model.Load(lowerCAmelCase )
lowerCamelCase_ =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowerCAmelCase ) )]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =Path(self.tmpdirname )
save_json(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''<pad>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<s>''' )
self.assertEqual(vocab_keys[1], '''<pad>''' )
self.assertEqual(vocab_keys[-1], '''j''' )
self.assertEqual(len(lowerCAmelCase ), 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [289, 50, 14, 174, 386], )
lowerCamelCase_ =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.'''], )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ =tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.'''], )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase, model_name='''facebook/s2t-small-mustc-en-de-st''', revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''', )
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Tuple ='valhalla/s2t_mustc_multilinguial_medium'
lowercase : Dict ='C\'est trop cool'
lowercase : str ='Esto es genial'
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''], 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''], 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''], 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''], 11 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 10_000 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertIn(lowerCAmelCase, self.tokenizer.all_special_ids )
lowerCamelCase_ =[ES_CODE, 4, 1_601, 47, 7_647, 2]
lowerCamelCase_ =self.tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
lowerCamelCase_ =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0], lowerCAmelCase )
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE] )
lowerCamelCase_ ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE] )
| 75 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=lowerCamelCase__ ):
lowercase : Any =['flax', 'transformers']
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(self, ['''flax''', '''transformers'''] )
@classmethod
def lowercase__ ( cls, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''flax''', '''transformers'''] )
@classmethod
def lowercase__ ( cls, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''flax''', '''transformers'''] )
class __UpperCamelCase ( metaclass=lowerCamelCase__ ):
lowercase : Union[str, Any] =['flax', 'transformers']
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(self, ['''flax''', '''transformers'''] )
@classmethod
def lowercase__ ( cls, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''flax''', '''transformers'''] )
@classmethod
def lowercase__ ( cls, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''flax''', '''transformers'''] )
class __UpperCamelCase ( metaclass=lowerCamelCase__ ):
lowercase : Union[str, Any] =['flax', 'transformers']
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(self, ['''flax''', '''transformers'''] )
@classmethod
def lowercase__ ( cls, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''flax''', '''transformers'''] )
@classmethod
def lowercase__ ( cls, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''flax''', '''transformers'''] )
class __UpperCamelCase ( metaclass=lowerCamelCase__ ):
lowercase : List[str] =['flax', 'transformers']
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(self, ['''flax''', '''transformers'''] )
@classmethod
def lowercase__ ( cls, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''flax''', '''transformers'''] )
@classmethod
def lowercase__ ( cls, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''flax''', '''transformers'''] )
| 75 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def a_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase_ ='''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
lowerCamelCase_ =Image.open(requests.get(__snake_case , stream=__snake_case ).raw ).convert('''RGB''' )
return image
def a_ ( __snake_case : Tuple ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''') )
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''') )
# fmt: on
return rename_keys
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =dct.pop(__snake_case )
lowerCamelCase_ =val
def a_ ( __snake_case : str , __snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
lowerCamelCase_ =torch.cat((q_bias, torch.zeros_like(__snake_case , requires_grad=__snake_case ), v_bias) )
lowerCamelCase_ =qkv_bias
def a_ ( __snake_case : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ =364 if '''coco''' in model_name else 224
lowerCamelCase_ =InstructBlipVisionConfig(image_size=__snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xxl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf''' , vocab_size=3_2001 ).to_dict()
elif "vicuna-13b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf''' , vocab_size=3_2001 ).to_dict()
else:
raise ValueError('''Model name not supported''' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
lowerCamelCase_ =InstructBlipQFormerConfig(vocab_size=3_0523 ).to_dict()
lowerCamelCase_ =InstructBlipConfig(vision_config=__snake_case , text_config=__snake_case , qformer_config=__snake_case )
return config, image_size
@torch.no_grad()
def a_ ( __snake_case : Optional[int] , __snake_case : str=None , __snake_case : List[Any]=False ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =AutoTokenizer.from_pretrained('''bert-base-uncased''' , truncation_side='''left''' )
qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''} )
if "t5" in model_name:
lowerCamelCase_ =TaTokenizerFast.from_pretrained('''google/flan-t5-xl''' , truncation_side='''left''' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
lowerCamelCase_ =LlamaTokenizerFast.from_pretrained(
'''huggyllama/llama-7b''' , truncation_side='''left''' , bos_token='''</s>''' , unk_token='''</s>''' )
tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''} )
lowerCamelCase_, lowerCamelCase_ =get_blipa_config(__snake_case )
lowerCamelCase_ =InstructBlipForConditionalGeneration(__snake_case ).eval()
lowerCamelCase_ ={
'''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
'''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
'''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
'''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
}
lowerCamelCase_, lowerCamelCase_ =model_name_to_original[model_name]
# load original model
print('''Loading original model...''' )
lowerCamelCase_ ='''cuda:1''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_ ='''cuda:2''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =load_model_and_preprocess(
name=__snake_case , model_type=__snake_case , is_eval=__snake_case , device=__snake_case )
original_model.eval()
print('''Done!''' )
# update state dict keys
lowerCamelCase_ =original_model.state_dict()
lowerCamelCase_ =create_rename_keys(__snake_case )
for src, dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
lowerCamelCase_ =state_dict.pop(__snake_case )
if key.startswith('''Qformer.bert''' ):
lowerCamelCase_ =key.replace('''Qformer.bert''' , '''qformer''' )
if "attention.self" in key:
lowerCamelCase_ =key.replace('''self''' , '''attention''' )
if "llm_proj" in key:
lowerCamelCase_ =key.replace('''llm_proj''' , '''language_projection''' )
if "t5_proj" in key:
lowerCamelCase_ =key.replace('''t5_proj''' , '''language_projection''' )
if key.startswith('''llm_model''' ):
lowerCamelCase_ =key.replace('''llm_model''' , '''language_model''' )
if key.startswith('''t5''' ):
lowerCamelCase_ =key.replace('''t5''' , '''language''' )
lowerCamelCase_ =val
# read in qv biases
read_in_q_v_bias(__snake_case , __snake_case )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(__snake_case , strict=__snake_case )
lowerCamelCase_ =load_demo_image()
lowerCamelCase_ ='''What is unusual about this image?'''
# create processor
lowerCamelCase_ =BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size} , image_mean=__snake_case , image_std=__snake_case )
lowerCamelCase_ =InstructBlipProcessor(
image_processor=__snake_case , tokenizer=__snake_case , qformer_tokenizer=__snake_case , )
lowerCamelCase_ =processor(images=__snake_case , text=__snake_case , return_tensors='''pt''' ).to(__snake_case )
# make sure processor creates exact same pixel values
lowerCamelCase_ =vis_processors['''eval'''](__snake_case ).unsqueeze(0 ).to(__snake_case )
lowerCamelCase_ =inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , __snake_case )
original_model.to(__snake_case )
hf_model.to(__snake_case )
with torch.no_grad():
if "vicuna" in model_name:
lowerCamelCase_ =original_model({'''image''': original_pixel_values, '''text_input''': [prompt]} ).logits
lowerCamelCase_ =hf_model(**__snake_case ).logits
else:
lowerCamelCase_ =original_model(
{'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']} ).logits
lowerCamelCase_ =tokenizer('''\n''' , return_tensors='''pt''' ).input_ids.to(__snake_case )
lowerCamelCase_ =label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
lowerCamelCase_ =hf_model(**__snake_case , labels=__snake_case ).logits
print('''First values of original logits:''' , original_logits[0, :3, :3] )
print('''First values of HF logits:''' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
lowerCamelCase_ =1e-4 if '''vicuna''' in model_name else 1e-5
assert torch.allclose(original_logits.to(logits.device ) , __snake_case , atol=__snake_case )
print('''Looks ok!''' )
print('''Generating with original model...''' )
lowerCamelCase_ =original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('''Generating with HF model...''' )
lowerCamelCase_ =hf_model.generate(
**__snake_case , do_sample=__snake_case , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
lowerCamelCase_ =2
print('''Original generation:''' , __snake_case )
lowerCamelCase_ =processor.batch_decode(__snake_case , skip_special_tokens=__snake_case )
lowerCamelCase_ =[text.strip() for text in output_text]
print('''HF generation:''' , __snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__snake_case )
hf_model.save_pretrained(__snake_case )
if push_to_hub:
processor.push_to_hub(F'''Salesforce/{model_name}''' )
hf_model.push_to_hub(F'''Salesforce/{model_name}''' )
if __name__ == "__main__":
a_ : Any = argparse.ArgumentParser()
a_ : Any = [
"""instructblip-vicuna-7b""",
"""instructblip-vicuna-13b""",
"""instructblip-flan-t5-xl""",
"""instructblip-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""instructblip-flan-t5-xl""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
a_ : str = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 75 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ : int = logging.get_logger(__name__)
a_ : Tuple = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : int ='table-transformer'
lowercase : Union[str, Any] =['past_key_values']
lowercase : str ={
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self, lowerCAmelCase=True, lowerCAmelCase=None, lowerCAmelCase=3, lowerCAmelCase=100, lowerCAmelCase=6, lowerCAmelCase=2_048, lowerCAmelCase=8, lowerCAmelCase=6, lowerCAmelCase=2_048, lowerCAmelCase=8, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=True, lowerCAmelCase="relu", lowerCAmelCase=256, lowerCAmelCase=0.1, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=0.0_2, lowerCAmelCase=1.0, lowerCAmelCase=False, lowerCAmelCase="sine", lowerCAmelCase="resnet50", lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase=1, lowerCAmelCase=5, lowerCAmelCase=2, lowerCAmelCase=1, lowerCAmelCase=1, lowerCAmelCase=5, lowerCAmelCase=2, lowerCAmelCase=0.1, **lowerCAmelCase, ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowerCamelCase_ =CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =backbone_config.get('''model_type''' )
lowerCamelCase_ =CONFIG_MAPPING[backbone_model_type]
lowerCamelCase_ =config_class.from_dict(lowerCAmelCase )
# set timm attributes to None
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =None, None, None
lowerCamelCase_ =use_timm_backbone
lowerCamelCase_ =backbone_config
lowerCamelCase_ =num_channels
lowerCamelCase_ =num_queries
lowerCamelCase_ =d_model
lowerCamelCase_ =encoder_ffn_dim
lowerCamelCase_ =encoder_layers
lowerCamelCase_ =encoder_attention_heads
lowerCamelCase_ =decoder_ffn_dim
lowerCamelCase_ =decoder_layers
lowerCamelCase_ =decoder_attention_heads
lowerCamelCase_ =dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =activation_function
lowerCamelCase_ =init_std
lowerCamelCase_ =init_xavier_std
lowerCamelCase_ =encoder_layerdrop
lowerCamelCase_ =decoder_layerdrop
lowerCamelCase_ =encoder_layers
lowerCamelCase_ =auxiliary_loss
lowerCamelCase_ =position_embedding_type
lowerCamelCase_ =backbone
lowerCamelCase_ =use_pretrained_backbone
lowerCamelCase_ =dilation
# Hungarian matcher
lowerCamelCase_ =class_cost
lowerCamelCase_ =bbox_cost
lowerCamelCase_ =giou_cost
# Loss coefficients
lowerCamelCase_ =mask_loss_coefficient
lowerCamelCase_ =dice_loss_coefficient
lowerCamelCase_ =bbox_loss_coefficient
lowerCamelCase_ =giou_loss_coefficient
lowerCamelCase_ =eos_coefficient
super().__init__(is_encoder_decoder=lowerCAmelCase, **lowerCAmelCase )
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.d_model
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Tuple =version.parse('1.11' )
@property
def lowercase__ ( self ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def lowercase__ ( self ):
"""simple docstring"""
return 1e-5
@property
def lowercase__ ( self ):
"""simple docstring"""
return 12
| 75 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return 0.0
def a_ ( __snake_case : np.ndarray , __snake_case : int ) -> tuple[int | float, int | float]:
"""simple docstring"""
lowerCamelCase_ =min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
lowerCamelCase_ =max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.abs(np.fft.fft(__snake_case ) )
lowerCamelCase_ =20 * np.logaa(__snake_case )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
lowerCamelCase_ =get_bounds(__snake_case , __snake_case )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(__snake_case )
plt.show()
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.angle(np.fft.fft(__snake_case ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(__snake_case , -2 * pi ) )
plt.show()
| 75 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
a_ : str = sys.version_info >= (3, 10)
def a_ ( __snake_case : Union[str, Any]=None , __snake_case : Union[str, Any]=None ) -> int:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class __UpperCamelCase :
lowercase : int
lowercase : float
lowercase : str
lowercase : bool
@dataclass
class __UpperCamelCase :
lowercase : int =42
lowercase : str =field(default='toto' , metadata={'help': 'help message'} )
@dataclass
class __UpperCamelCase :
lowercase : bool =False
lowercase : bool =True
lowercase : Optional[bool] =None
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Union[str, Any] ='titi'
lowercase : Union[str, Any] ='toto'
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[int] ='titi'
lowercase : Dict ='toto'
lowercase : int =42
@dataclass
class __UpperCamelCase :
lowercase : BasicEnum ="toto"
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BasicEnum(self.foo )
@dataclass
class __UpperCamelCase :
lowercase : MixedTypeEnum ="toto"
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MixedTypeEnum(self.foo )
@dataclass
class __UpperCamelCase :
lowercase : Optional[int] =None
lowercase : Optional[float] =field(default=lowerCamelCase__ , metadata={'help': 'help message'} )
lowercase : Optional[str] =None
lowercase : Optional[List[str]] =list_field(default=[] )
lowercase : Optional[List[int]] =list_field(default=[] )
@dataclass
class __UpperCamelCase :
lowercase : List[int] =list_field(default=[] )
lowercase : List[int] =list_field(default=[1, 2, 3] )
lowercase : List[str] =list_field(default=['Hallo', 'Bonjour', 'Hello'] )
lowercase : List[float] =list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class __UpperCamelCase :
lowercase : List[int] =field()
lowercase : str =field()
lowercase : BasicEnum =field()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BasicEnum(self.required_enum )
@dataclass
class __UpperCamelCase :
lowercase : int
lowercase : "BasicEnum" =field()
lowercase : "Optional[bool]" =None
lowercase : "str" =field(default='toto' , metadata={'help': 'help message'} )
lowercase : "List[str]" =list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class __UpperCamelCase :
lowercase : bool =False
lowercase : bool =True
lowercase : bool | None =None
@dataclass
class __UpperCamelCase :
lowercase : int | None =None
lowercase : float | None =field(default=lowerCamelCase__ , metadata={'help': 'help message'} )
lowercase : str | None =None
lowercase : list[str] | None =list_field(default=[] )
lowercase : list[int] | None =list_field(default=[] )
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
self.assertEqual(len(a._actions ), len(b._actions ) )
for x, y in zip(a._actions, b._actions ):
lowerCamelCase_ ={k: v for k, v in vars(lowerCAmelCase ).items() if k != '''container'''}
lowerCamelCase_ ={k: v for k, v in vars(lowerCAmelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''', lowerCAmelCase ) and yy.get('''choices''', lowerCAmelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](lowerCAmelCase ), yy['''type'''](lowerCAmelCase ) )
del xx["type"], yy["type"]
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument('''--foo''', type=lowerCAmelCase, required=lowerCAmelCase )
expected.add_argument('''--bar''', type=lowerCAmelCase, required=lowerCAmelCase )
expected.add_argument('''--baz''', type=lowerCAmelCase, required=lowerCAmelCase )
expected.add_argument('''--flag''', type=lowerCAmelCase, default=lowerCAmelCase, const=lowerCAmelCase, nargs='''?''' )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((lowerCamelCase_), ) =parser.parse_args_into_dataclasses(lowerCAmelCase, look_for_args_file=lowerCAmelCase )
self.assertFalse(example.flag )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument('''--foo''', default=42, type=lowerCAmelCase )
expected.add_argument('''--baz''', default='''toto''', type=lowerCAmelCase, help='''help message''' )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument('''--foo''', type=lowerCAmelCase, default=lowerCAmelCase, const=lowerCAmelCase, nargs='''?''' )
expected.add_argument('''--baz''', type=lowerCAmelCase, default=lowerCAmelCase, const=lowerCAmelCase, nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''', action='''store_false''', default=lowerCAmelCase, dest='''baz''' )
expected.add_argument('''--opt''', type=lowerCAmelCase, default=lowerCAmelCase )
lowerCamelCase_ =[WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(lowerCAmelCase )
for dataclass_type in dataclass_types:
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =parser.parse_args([] )
self.assertEqual(lowerCAmelCase, Namespace(foo=lowerCAmelCase, baz=lowerCAmelCase, opt=lowerCAmelCase ) )
lowerCamelCase_ =parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(lowerCAmelCase, Namespace(foo=lowerCAmelCase, baz=lowerCAmelCase, opt=lowerCAmelCase ) )
lowerCamelCase_ =parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(lowerCAmelCase, Namespace(foo=lowerCAmelCase, baz=lowerCAmelCase, opt=lowerCAmelCase ) )
lowerCamelCase_ =parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(lowerCAmelCase, Namespace(foo=lowerCAmelCase, baz=lowerCAmelCase, opt=lowerCAmelCase ) )
lowerCamelCase_ =parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(lowerCAmelCase, Namespace(foo=lowerCAmelCase, baz=lowerCAmelCase, opt=lowerCAmelCase ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument(
'''--foo''', default='''toto''', choices=['''titi''', '''toto''', 42], type=make_choice_type_function(['''titi''', '''toto''', 42] ), )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =parser.parse_args([] )
self.assertEqual(args.foo, '''toto''' )
lowerCamelCase_ =parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo, MixedTypeEnum.toto )
lowerCamelCase_ =parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo, '''titi''' )
lowerCamelCase_ =parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo, MixedTypeEnum.titi )
lowerCamelCase_ =parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo, 42 )
lowerCamelCase_ =parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo, MixedTypeEnum.fourtytwo )
def lowercase__ ( self ):
"""simple docstring"""
@dataclass
class __UpperCamelCase :
lowercase : Literal["titi", "toto", 42] ="toto"
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument(
'''--foo''', default='''toto''', choices=('''titi''', '''toto''', 42), type=make_choice_type_function(['''titi''', '''toto''', 42] ), )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =parser.parse_args([] )
self.assertEqual(args.foo, '''toto''' )
lowerCamelCase_ =parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo, '''titi''' )
lowerCamelCase_ =parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo, 42 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument('''--foo_int''', nargs='''+''', default=[], type=lowerCAmelCase )
expected.add_argument('''--bar_int''', nargs='''+''', default=[1, 2, 3], type=lowerCAmelCase )
expected.add_argument('''--foo_str''', nargs='''+''', default=['''Hallo''', '''Bonjour''', '''Hello'''], type=lowerCAmelCase )
expected.add_argument('''--foo_float''', nargs='''+''', default=[0.1, 0.2, 0.3], type=lowerCAmelCase )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =parser.parse_args([] )
self.assertEqual(
lowerCAmelCase, Namespace(foo_int=[], bar_int=[1, 2, 3], foo_str=['''Hallo''', '''Bonjour''', '''Hello'''], foo_float=[0.1, 0.2, 0.3] ), )
lowerCamelCase_ =parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(lowerCAmelCase, Namespace(foo_int=[1], bar_int=[2, 3], foo_str=['''a''', '''b''', '''c'''], foo_float=[0.1, 0.7] ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument('''--foo''', default=lowerCAmelCase, type=lowerCAmelCase )
expected.add_argument('''--bar''', default=lowerCAmelCase, type=lowerCAmelCase, help='''help message''' )
expected.add_argument('''--baz''', default=lowerCAmelCase, type=lowerCAmelCase )
expected.add_argument('''--ces''', nargs='''+''', default=[], type=lowerCAmelCase )
expected.add_argument('''--des''', nargs='''+''', default=[], type=lowerCAmelCase )
lowerCamelCase_ =[OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(lowerCAmelCase )
for dataclass_type in dataclass_types:
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =parser.parse_args([] )
self.assertEqual(lowerCAmelCase, Namespace(foo=lowerCAmelCase, bar=lowerCAmelCase, baz=lowerCAmelCase, ces=[], des=[] ) )
lowerCamelCase_ =parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(lowerCAmelCase, Namespace(foo=12, bar=3.1_4, baz='''42''', ces=['''a''', '''b''', '''c'''], des=[1, 2, 3] ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument('''--required_list''', nargs='''+''', type=lowerCAmelCase, required=lowerCAmelCase )
expected.add_argument('''--required_str''', type=lowerCAmelCase, required=lowerCAmelCase )
expected.add_argument(
'''--required_enum''', type=make_choice_type_function(['''titi''', '''toto'''] ), choices=['''titi''', '''toto'''], required=lowerCAmelCase, )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ =argparse.ArgumentParser()
expected.add_argument('''--foo''', type=lowerCAmelCase, required=lowerCAmelCase )
expected.add_argument(
'''--required_enum''', type=make_choice_type_function(['''titi''', '''toto'''] ), choices=['''titi''', '''toto'''], required=lowerCAmelCase, )
expected.add_argument('''--opt''', type=lowerCAmelCase, default=lowerCAmelCase )
expected.add_argument('''--baz''', default='''toto''', type=lowerCAmelCase, help='''help message''' )
expected.add_argument('''--foo_str''', nargs='''+''', default=['''Hallo''', '''Bonjour''', '''Hello'''], type=lowerCAmelCase )
self.argparsersEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ ={
'''foo''': 12,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
lowerCamelCase_ =parser.parse_dict(lowerCAmelCase )[0]
lowerCamelCase_ =BasicExample(**lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ ={
'''foo''': 12,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 42,
}
self.assertRaises(lowerCAmelCase, parser.parse_dict, lowerCAmelCase, allow_extra_keys=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ ={
'''foo''': 12,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ =os.path.join(lowerCAmelCase, '''temp_json''' )
os.mkdir(lowerCAmelCase )
with open(temp_local_path + '''.json''', '''w+''' ) as f:
json.dump(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
lowerCamelCase_ =BasicExample(**lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
lowerCamelCase_ ={
'''foo''': 12,
'''bar''': 3.1_4,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ =os.path.join(lowerCAmelCase, '''temp_yaml''' )
os.mkdir(lowerCAmelCase )
with open(temp_local_path + '''.yaml''', '''w+''' ) as f:
yaml.dump(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
lowerCamelCase_ =BasicExample(**lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =HfArgumentParser(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
| 75 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =FunnelTokenizer
lowercase : List[str] =FunnelTokenizerFast
lowercase : Union[str, Any] =True
lowercase : int =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =[
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return FunnelTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ='''UNwant\u00E9d,running'''
lowerCamelCase_ ='''unwanted, running'''
return input_text, output_text
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.tokenizer_class(self.vocab_file )
lowerCamelCase_ =tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(lowerCAmelCase, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [7, 4, 5, 10, 8, 9] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
lowerCamelCase_ =tokenizer('''UNwant\u00E9d,running''' )
lowerCamelCase_ =len(inputs['''input_ids'''] ) - 1
self.assertListEqual(inputs['''token_type_ids'''], [2] + [0] * sentence_len )
lowerCamelCase_ =tokenizer('''UNwant\u00E9d,running''', '''UNwant\u00E9d,running''' )
self.assertListEqual(inputs['''token_type_ids'''], [2] + [0] * sentence_len + [1] * sentence_len )
| 75 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[int] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a_ : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a_ : Optional[int] = {"""facebook/blenderbot_small-90M""": 5_12}
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =set()
lowerCamelCase_ =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ =char
lowerCamelCase_ =set(__snake_case )
return pairs
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[int] =VOCAB_FILES_NAMES
lowercase : Tuple =PRETRAINED_VOCAB_FILES_MAP
lowercase : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Dict =['input_ids', 'attention_mask']
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase="__start__", lowerCAmelCase="__end__", lowerCAmelCase="__unk__", lowerCAmelCase="__null__", **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(unk_token=lowerCAmelCase, bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, pad_token=lowerCAmelCase, **lowerCAmelCase )
with open(lowerCAmelCase, encoding='''utf-8''' ) as vocab_handle:
lowerCamelCase_ =json.load(lowerCAmelCase )
lowerCamelCase_ ={v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase, encoding='''utf-8''' ) as merges_handle:
lowerCamelCase_ =merges_handle.read().split('''\n''' )[1:-1]
lowerCamelCase_ =[tuple(merge.split() ) for merge in merges]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={}
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.encoder )
def lowercase__ ( self ):
"""simple docstring"""
return dict(self.encoder, **self.added_tokens_encoder )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
lowerCamelCase_ =re.sub('''([.,!?()])''', R''' \1''', lowerCAmelCase )
lowerCamelCase_ =re.sub('''(\')''', R''' \1 ''', lowerCAmelCase )
lowerCamelCase_ =re.sub(R'''\s{2,}''', ''' ''', lowerCAmelCase )
if "\n" in token:
lowerCamelCase_ =token.replace('''\n''', ''' __newln__''' )
lowerCamelCase_ =token.split(''' ''' )
lowerCamelCase_ =[]
for token in tokens:
if not len(lowerCAmelCase ):
continue
lowerCamelCase_ =token.lower()
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
lowerCamelCase_ =get_pairs(lowerCAmelCase )
if not pairs:
words.append(lowerCAmelCase )
continue
while True:
lowerCamelCase_ =min(lowerCAmelCase, key=lambda lowerCAmelCase : self.bpe_ranks.get(lowerCAmelCase, float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_, lowerCamelCase_ =bigram
lowerCamelCase_ =[]
lowerCamelCase_ =0
while i < len(lowerCAmelCase ):
try:
lowerCamelCase_ =word.index(lowerCAmelCase, lowerCAmelCase )
new_word.extend(word[i:j] )
lowerCamelCase_ =j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =new_word
if len(lowerCAmelCase ) == 1:
break
else:
lowerCamelCase_ =get_pairs(lowerCAmelCase )
lowerCamelCase_ ='''@@ '''.join(lowerCAmelCase )
lowerCamelCase_ =word[:-4]
lowerCamelCase_ =word
words.append(lowerCAmelCase )
return " ".join(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =re.findall(R'''\S+\n?''', lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =token.lower()
return self.encoder.get(lowerCAmelCase, self.encoder.get(self.unk_token ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase, self.unk_token )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''' '''.join(lowerCAmelCase ).replace('''@@ ''', '''''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=lowerCAmelCase, ensure_ascii=lowerCAmelCase ) + '''\n''' )
lowerCamelCase_ =0
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
lowerCamelCase_ =token_index
writer.write(''' '''.join(lowerCAmelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 75 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def a_ ( __snake_case : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ =checkpoints.load_tax_checkpoint(__snake_case )
lowerCamelCase_ =flatten_dict(__snake_case )
return flax_params
def a_ ( __snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ ={}
lowerCamelCase_ ={
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCamelCase_ ={
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCamelCase_ ='''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =flax_dict[key]
lowerCamelCase_ ={}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCamelCase_ =torch.from_numpy(converted_dict[key].T )
else:
lowerCamelCase_ =torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Any=False , __snake_case : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =get_flax_param(__snake_case )
if not use_large:
lowerCamelCase_ =PixaStructVisionConfig()
lowerCamelCase_ =PixaStructTextConfig()
else:
lowerCamelCase_ =PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowerCamelCase_ =PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowerCamelCase_ =PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__snake_case )
lowerCamelCase_ =PixaStructForConditionalGeneration(__snake_case )
lowerCamelCase_ =rename_and_convert_flax_params(__snake_case )
model.load_state_dict(__snake_case )
lowerCamelCase_ =AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCamelCase_ =PixaStructImageProcessor()
lowerCamelCase_ =PixaStructProcessor(image_processor=__snake_case , tokenizer=__snake_case )
if use_large:
lowerCamelCase_ =4096
lowerCamelCase_ =True
# mkdir if needed
os.makedirs(__snake_case , exist_ok=__snake_case )
model.save_pretrained(__snake_case )
processor.save_pretrained(__snake_case )
print('''Model saved in {}'''.format(__snake_case ) )
if __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
a_ : Tuple = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 75 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCamelCase ( unittest.TestCase , lowerCamelCase__ ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =load_tool('''text-classification''' )
self.tool.setup()
lowerCamelCase_ =load_tool('''text-classification''', remote=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.tool('''That\'s quite cool''', ['''positive''', '''negative'''] )
self.assertEqual(lowerCAmelCase, '''positive''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.remote_tool('''That\'s quite cool''', ['''positive''', '''negative'''] )
self.assertEqual(lowerCAmelCase, '''positive''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.tool(text='''That\'s quite cool''', labels=['''positive''', '''negative'''] )
self.assertEqual(lowerCAmelCase, '''positive''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.remote_tool(text='''That\'s quite cool''', labels=['''positive''', '''negative'''] )
self.assertEqual(lowerCAmelCase, '''positive''' )
| 75 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ : Union[str, Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Tuple =['pixel_values']
def __init__( self, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = True, lowerCAmelCase = 1 / 255, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = True, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =size if size is not None else {'''shortest_edge''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
lowerCamelCase_ =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase, param_name='''crop_size''' )
lowerCamelCase_ =do_resize
lowerCamelCase_ =size
lowerCamelCase_ =resample
lowerCamelCase_ =do_center_crop
lowerCamelCase_ =crop_size
lowerCamelCase_ =do_rescale
lowerCamelCase_ =rescale_factor
lowerCamelCase_ =do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCamelCase_ =image_std if image_std is not None else OPENAI_CLIP_STD
lowerCamelCase_ =do_convert_rgb
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowerCamelCase_ =get_resize_output_image_size(lowerCAmelCase, size=size['''shortest_edge'''], default_to_square=lowerCAmelCase )
return resize(lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowerCAmelCase, size=(size['''height'''], size['''width''']), data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return rescale(lowerCAmelCase, scale=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return normalize(lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = ChannelDimension.FIRST, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ =size if size is not None else self.size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =resample if resample is not None else self.resample
lowerCamelCase_ =do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase_ =crop_size if crop_size is not None else self.crop_size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''crop_size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ =rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ =do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ =image_std if image_std is not None else self.image_std
lowerCamelCase_ =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase_ =make_list_of_images(lowerCAmelCase )
if not valid_images(lowerCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase_ =[convert_to_rgb(lowerCAmelCase ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase_ =[to_numpy_array(lowerCAmelCase ) for image in images]
if do_resize:
lowerCamelCase_ =[self.resize(image=lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase ) for image in images]
if do_center_crop:
lowerCamelCase_ =[self.center_crop(image=lowerCAmelCase, size=lowerCAmelCase ) for image in images]
if do_rescale:
lowerCamelCase_ =[self.rescale(image=lowerCAmelCase, scale=lowerCAmelCase ) for image in images]
if do_normalize:
lowerCamelCase_ =[self.normalize(image=lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase ) for image in images]
lowerCamelCase_ =[to_channel_dimension_format(lowerCAmelCase, lowerCAmelCase ) for image in images]
lowerCamelCase_ ={'''pixel_values''': images}
return BatchFeature(data=lowerCAmelCase, tensor_type=lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCAmelCase, '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(lowerCAmelCase, '''neck_hidden_sizes''' ) )
self.parent.assertTrue(hasattr(lowerCAmelCase, '''num_attention_heads''' ) )
class __UpperCamelCase :
def __init__( self, lowerCAmelCase, lowerCAmelCase=13, lowerCAmelCase=32, lowerCAmelCase=2, lowerCAmelCase=3, lowerCAmelCase=640, lowerCAmelCase=4, lowerCAmelCase="silu", lowerCAmelCase=3, lowerCAmelCase=32, lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=0.0_2, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=10, lowerCAmelCase=None, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =image_size
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =last_hidden_size
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =hidden_act
lowerCamelCase_ =conv_kernel_size
lowerCamelCase_ =output_stride
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =attention_probs_dropout_prob
lowerCamelCase_ =classifier_dropout_prob
lowerCamelCase_ =use_labels
lowerCamelCase_ =is_training
lowerCamelCase_ =num_labels
lowerCamelCase_ =initializer_range
lowerCamelCase_ =scope
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ =None
lowerCamelCase_ =None
if self.use_labels:
lowerCamelCase_ =ids_tensor([self.batch_size], self.num_labels )
lowerCamelCase_ =ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
lowerCamelCase_ =self.get_config()
return config, pixel_values, labels, pixel_labels
def lowercase__ ( self ):
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_attention_heads=self.num_attention_heads, hidden_act=self.hidden_act, conv_kernel_size=self.conv_kernel_size, output_stride=self.output_stride, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =MobileViTModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape, (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.num_labels
lowerCamelCase_ =MobileViTForImageClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase, labels=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.num_labels
lowerCamelCase_ =MobileViTForSemanticSegmentation(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape, (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
lowerCamelCase_ =model(lowerCAmelCase, labels=lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape, (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =config_and_inputs
lowerCamelCase_ ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =(
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
lowercase : List[str] =(
{
'feature-extraction': MobileViTModel,
'image-classification': MobileViTForImageClassification,
'image-segmentation': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowercase : int =False
lowercase : str =False
lowercase : Optional[Any] =False
lowercase : Union[str, Any] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MobileViTModelTester(self )
lowerCamelCase_ =MobileViTConfigTester(self, config_class=lowerCAmelCase, has_text_modality=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''MobileViT does not output attentions''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
lowerCamelCase_ =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ =[*signature.parameters.keys()]
lowerCamelCase_ =['''pixel_values''']
self.assertListEqual(arg_names[:1], lowerCAmelCase )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
with torch.no_grad():
lowerCamelCase_ =model(**self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
lowerCamelCase_ =outputs.hidden_states
lowerCamelCase_ =5
self.assertEqual(len(lowerCAmelCase ), lowerCAmelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
lowerCamelCase_ =2
for i in range(len(lowerCAmelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ), [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor], )
divisor *= 2
self.assertEqual(self.model_tester.output_stride, divisor // 2 )
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =True
check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase_ =True
check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =MobileViTModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def a_ ( ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' ) if is_vision_available() else None
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(lowerCAmelCase )
lowerCamelCase_ =self.default_image_processor
lowerCamelCase_ =prepare_img()
lowerCamelCase_ =image_processor(images=lowerCAmelCase, return_tensors='''pt''' ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
lowerCamelCase_ =model(**lowerCAmelCase )
# verify the logits
lowerCamelCase_ =torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape, lowerCAmelCase )
lowerCamelCase_ =torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] ).to(lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3], lowerCAmelCase, atol=1e-4 ) )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
lowerCamelCase_ =model.to(lowerCAmelCase )
lowerCamelCase_ =MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
lowerCamelCase_ =prepare_img()
lowerCamelCase_ =image_processor(images=lowerCAmelCase, return_tensors='''pt''' ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
lowerCamelCase_ =model(**lowerCAmelCase )
lowerCamelCase_ =outputs.logits
# verify the logits
lowerCamelCase_ =torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape, lowerCAmelCase )
lowerCamelCase_ =torch.tensor(
[
[[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
[[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
[[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
], device=lowerCAmelCase, )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], lowerCAmelCase, atol=1e-4 ) )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
lowerCamelCase_ =model.to(lowerCAmelCase )
lowerCamelCase_ =MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
lowerCamelCase_ =prepare_img()
lowerCamelCase_ =image_processor(images=lowerCAmelCase, return_tensors='''pt''' ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
lowerCamelCase_ =model(**lowerCAmelCase )
lowerCamelCase_ =outputs.logits.detach().cpu()
lowerCamelCase_ =image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase, target_sizes=[(50, 60)] )
lowerCamelCase_ =torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape, lowerCAmelCase )
lowerCamelCase_ =image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase )
lowerCamelCase_ =torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape, lowerCAmelCase )
| 75 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : str , __snake_case : list[str] | None = None , __snake_case : dict[str, float] | None = None , __snake_case : bool = False , ) -> tuple[int, float, str]:
"""simple docstring"""
lowerCamelCase_ =cipher_alphabet or [chr(__snake_case ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
lowerCamelCase_ ={
'''a''': 0.0_8_4_9_7,
'''b''': 0.0_1_4_9_2,
'''c''': 0.0_2_2_0_2,
'''d''': 0.0_4_2_5_3,
'''e''': 0.1_1_1_6_2,
'''f''': 0.0_2_2_2_8,
'''g''': 0.0_2_0_1_5,
'''h''': 0.0_6_0_9_4,
'''i''': 0.0_7_5_4_6,
'''j''': 0.0_0_1_5_3,
'''k''': 0.0_1_2_9_2,
'''l''': 0.0_4_0_2_5,
'''m''': 0.0_2_4_0_6,
'''n''': 0.0_6_7_4_9,
'''o''': 0.0_7_5_0_7,
'''p''': 0.0_1_9_2_9,
'''q''': 0.0_0_0_9_5,
'''r''': 0.0_7_5_8_7,
'''s''': 0.0_6_3_2_7,
'''t''': 0.0_9_3_5_6,
'''u''': 0.0_2_7_5_8,
'''v''': 0.0_0_9_7_8,
'''w''': 0.0_2_5_6_0,
'''x''': 0.0_0_1_5_0,
'''y''': 0.0_1_9_9_4,
'''z''': 0.0_0_0_7_7,
}
else:
# Custom frequencies dictionary
lowerCamelCase_ =frequencies_dict
if not case_sensitive:
lowerCamelCase_ =ciphertext.lower()
# Chi squared statistic values
lowerCamelCase_ ={}
# cycle through all of the shifts
for shift in range(len(__snake_case ) ):
lowerCamelCase_ =''''''
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
lowerCamelCase_ =(alphabet_letters.index(letter.lower() ) - shift) % len(
__snake_case )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
lowerCamelCase_ =0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
lowerCamelCase_ =letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.lower().count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
lowerCamelCase_ =(
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(__snake_case : int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
lowerCamelCase_ =min(
__snake_case , key=__snake_case , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
lowerCamelCase_
), (
lowerCamelCase_
),
) =chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 75 | 1 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
a_ : List[str] = """3"""
print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
print("""Torch version:""", None)
try:
import deepspeed
print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
print("""DeepSpeed version:""", None)
try:
import tensorflow as tf
print("""TensorFlow version:""", tf.__version__)
print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
print("""TensorFlow version:""", None)
| 75 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
a_ : List[Any] = (
"""https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)
a_ : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def a_ ( ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ ='''https://pypi.org/pypi/diffusers/json'''
lowerCamelCase_ =json.loads(request.urlopen(__snake_case ).read() )['''releases'''].keys()
return sorted(__snake_case , key=lambda __snake_case : version.Version(__snake_case ) )
def a_ ( ) -> str:
"""simple docstring"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__snake_case )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase_ =Path(__snake_case ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def a_ ( __snake_case : Union[str, os.PathLike] ) -> List[str]:
"""simple docstring"""
init_hf_modules()
lowerCamelCase_ =Path(__snake_case ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase_ =dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def a_ ( __snake_case : Tuple ) -> List[str]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowerCamelCase_ =f.read()
# Imports of the form `import .xxx`
lowerCamelCase_ =re.findall('''^\s*import\s+\.(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Unique-ify
return list(set(__snake_case ) )
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
lowerCamelCase_ =False
lowerCamelCase_ =[module_file]
lowerCamelCase_ =[]
# Let's recurse through all relative imports
while not no_change:
lowerCamelCase_ =[]
for f in files_to_check:
new_imports.extend(get_relative_imports(__snake_case ) )
lowerCamelCase_ =Path(__snake_case ).parent
lowerCamelCase_ =[str(module_path / m ) for m in new_imports]
lowerCamelCase_ =[f for f in new_import_files if f not in all_relative_imports]
lowerCamelCase_ =[F'''{f}.py''' for f in new_import_files]
lowerCamelCase_ =len(__snake_case ) == 0
all_relative_imports.extend(__snake_case )
return all_relative_imports
def a_ ( __snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowerCamelCase_ =f.read()
# Imports of the form `import xxx`
lowerCamelCase_ =re.findall('''^\s*import\s+(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Only keep the top-level module
lowerCamelCase_ =[imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
lowerCamelCase_ =list(set(__snake_case ) )
lowerCamelCase_ =[]
for imp in imports:
try:
importlib.import_module(__snake_case )
except ImportError:
missing_packages.append(__snake_case )
if len(__snake_case ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
F'''{', '.join(__snake_case )}. Run `pip install {' '.join(__snake_case )}`''' )
return get_relative_imports(__snake_case )
def a_ ( __snake_case : Tuple , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =module_path.replace(os.path.sep , '''.''' )
lowerCamelCase_ =importlib.import_module(__snake_case )
if class_name is None:
return find_pipeline_class(__snake_case )
return getattr(__snake_case , __snake_case )
def a_ ( __snake_case : Dict ) -> Any:
"""simple docstring"""
from ..pipelines import DiffusionPipeline
lowerCamelCase_ =dict(inspect.getmembers(__snake_case , inspect.isclass ) )
lowerCamelCase_ =None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __snake_case )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''' )
lowerCamelCase_ =cls
return pipeline_class
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =str(__snake_case )
lowerCamelCase_ =os.path.join(__snake_case , __snake_case )
if os.path.isfile(__snake_case ):
lowerCamelCase_ =module_file_or_url
lowerCamelCase_ ='''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
lowerCamelCase_ =get_diffusers_versions()
# cut ".dev0"
lowerCamelCase_ ='''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
lowerCamelCase_ =latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(F'''Defaulting to latest_version: {revision}.''' )
elif revision in available_versions:
lowerCamelCase_ =F'''v{revision}'''
elif revision == "main":
lowerCamelCase_ =revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {', '.join(available_versions + ['main'] )}.''' )
# community pipeline on GitHub
lowerCamelCase_ =COMMUNITY_PIPELINES_URL.format(revision=__snake_case , pipeline=__snake_case )
try:
lowerCamelCase_ =cached_download(
__snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowerCamelCase_ ='''git'''
lowerCamelCase_ =pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
else:
try:
# Load from URL or cache if already cached
lowerCamelCase_ =hf_hub_download(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowerCamelCase_ =os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
# Check we have all the requirements in our environment
lowerCamelCase_ =check_imports(__snake_case )
# Now we move the module inside our cached dynamic modules.
lowerCamelCase_ =DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__snake_case )
lowerCamelCase_ =Path(__snake_case ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__snake_case , submodule_path / module_file )
for module_needed in modules_needed:
lowerCamelCase_ =F'''{module_needed}.py'''
shutil.copy(os.path.join(__snake_case , __snake_case ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__snake_case , __snake_case ):
lowerCamelCase_ =use_auth_token
elif use_auth_token is True:
lowerCamelCase_ =HfFolder.get_token()
else:
lowerCamelCase_ =None
lowerCamelCase_ =model_info(__snake_case , revision=__snake_case , token=__snake_case ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
lowerCamelCase_ =submodule_path / commit_hash
lowerCamelCase_ =full_submodule + os.path.sep + commit_hash
create_dynamic_module(__snake_case )
if not (submodule_path / module_file).exists():
shutil.copy(__snake_case , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__snake_case , F'''{module_needed}.py''' , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return os.path.join(__snake_case , __snake_case )
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[str] = None , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =get_cached_module_file(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return get_class_in_module(__snake_case , final_module.replace('''.py''' , '''''' ) )
| 75 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self, lowerCAmelCase, lowerCAmelCase=3, lowerCAmelCase=32, lowerCAmelCase=3, lowerCAmelCase=10, lowerCAmelCase=[10, 20, 30, 40], lowerCAmelCase=[1, 1, 2, 1], lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase="relu", lowerCAmelCase=3, lowerCAmelCase=None, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =image_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =embeddings_size
lowerCamelCase_ =hidden_sizes
lowerCamelCase_ =depths
lowerCamelCase_ =is_training
lowerCamelCase_ =use_labels
lowerCamelCase_ =hidden_act
lowerCamelCase_ =num_labels
lowerCamelCase_ =scope
lowerCamelCase_ =len(lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ =self.get_config()
return config, pixel_values
def lowercase__ ( self ):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, image_size=self.image_size, )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =FlaxRegNetModel(config=lowerCAmelCase )
lowerCamelCase_ =model(lowerCAmelCase )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.num_labels
lowerCamelCase_ =FlaxRegNetForImageClassification(config=lowerCAmelCase )
lowerCamelCase_ =model(lowerCAmelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
lowerCamelCase_, lowerCamelCase_ =config_and_inputs
lowerCamelCase_ ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowercase : List[str] =False
lowercase : List[str] =False
lowercase : Optional[int] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =FlaxRegNetModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, has_text_modality=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowercase__ ( self ):
"""simple docstring"""
return
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
lowerCamelCase_ =inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ =[*signature.parameters.keys()]
lowerCamelCase_ =['''pixel_values''']
self.assertListEqual(arg_names[:1], lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =model_class(lowerCAmelCase )
lowerCamelCase_ =model(**self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
lowerCamelCase_ =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase_ =self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase ), expected_num_stages + 1 )
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =True
check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase_ =True
check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCamelCase_ =self._prepare_for_class(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =model_class(lowerCAmelCase )
@jax.jit
def model_jitted(lowerCAmelCase, **lowerCAmelCase ):
return model(pixel_values=lowerCAmelCase, **lowerCAmelCase )
with self.subTest('''JIT Enabled''' ):
lowerCamelCase_ =model_jitted(**lowerCAmelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
lowerCamelCase_ =model_jitted(**lowerCAmelCase ).to_tuple()
self.assertEqual(len(lowerCAmelCase ), len(lowerCAmelCase ) )
for jitted_output, output in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertEqual(jitted_output.shape, output.shape )
def a_ ( ) -> int:
"""simple docstring"""
lowerCamelCase_ =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
lowerCamelCase_ =self.default_image_processor
lowerCamelCase_ =prepare_img()
lowerCamelCase_ =image_processor(images=lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =model(**lowerCAmelCase )
# verify the logits
lowerCamelCase_ =(1, 1_000)
self.assertEqual(outputs.logits.shape, lowerCAmelCase )
lowerCamelCase_ =jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3], lowerCAmelCase, atol=1e-4 ) )
| 75 |
'''simple docstring'''
a_ : Any = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
a_ : Any = [
9_99,
9_76,
9_52,
9_28,
9_05,
8_82,
8_58,
8_57,
8_10,
7_62,
7_15,
7_14,
5_72,
4_29,
4_28,
2_86,
2_85,
2_38,
1_90,
1_43,
1_42,
1_18,
95,
71,
47,
24,
0,
]
a_ : Optional[Any] = [
9_99,
9_88,
9_77,
9_66,
9_55,
9_44,
9_33,
9_22,
9_11,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_50,
3_00,
2_99,
2_66,
2_33,
2_00,
1_99,
1_79,
1_59,
1_40,
1_20,
1_00,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
a_ : str = [
9_99,
9_95,
9_92,
9_89,
9_85,
9_81,
9_78,
9_75,
9_71,
9_67,
9_64,
9_61,
9_57,
9_56,
9_51,
9_47,
9_42,
9_37,
9_33,
9_28,
9_23,
9_19,
9_14,
9_13,
9_08,
9_03,
8_97,
8_92,
8_87,
8_81,
8_76,
8_71,
8_70,
8_64,
8_58,
8_52,
8_46,
8_40,
8_34,
8_28,
8_27,
8_20,
8_13,
8_06,
7_99,
7_92,
7_85,
7_84,
7_77,
7_70,
7_63,
7_56,
7_49,
7_42,
7_41,
7_33,
7_24,
7_16,
7_07,
6_99,
6_98,
6_88,
6_77,
6_66,
6_56,
6_55,
6_45,
6_34,
6_23,
6_13,
6_12,
5_98,
5_84,
5_70,
5_69,
5_55,
5_41,
5_27,
5_26,
5_05,
4_84,
4_83,
4_62,
4_40,
4_39,
3_96,
3_95,
3_52,
3_51,
3_08,
3_07,
2_64,
2_63,
2_20,
2_19,
1_76,
1_32,
88,
44,
0,
]
a_ : Optional[int] = [
9_99,
9_97,
9_95,
9_92,
9_90,
9_88,
9_86,
9_84,
9_81,
9_79,
9_77,
9_75,
9_72,
9_70,
9_68,
9_66,
9_64,
9_61,
9_59,
9_57,
9_56,
9_54,
9_51,
9_49,
9_46,
9_44,
9_41,
9_39,
9_36,
9_34,
9_31,
9_29,
9_26,
9_24,
9_21,
9_19,
9_16,
9_14,
9_13,
9_10,
9_07,
9_05,
9_02,
8_99,
8_96,
8_93,
8_91,
8_88,
8_85,
8_82,
8_79,
8_77,
8_74,
8_71,
8_70,
8_67,
8_64,
8_61,
8_58,
8_55,
8_52,
8_49,
8_46,
8_43,
8_40,
8_37,
8_34,
8_31,
8_28,
8_27,
8_24,
8_21,
8_17,
8_14,
8_11,
8_08,
8_04,
8_01,
7_98,
7_95,
7_91,
7_88,
7_85,
7_84,
7_80,
7_77,
7_74,
7_70,
7_66,
7_63,
7_60,
7_56,
7_52,
7_49,
7_46,
7_42,
7_41,
7_37,
7_33,
7_30,
7_26,
7_22,
7_18,
7_14,
7_10,
7_07,
7_03,
6_99,
6_98,
6_94,
6_90,
6_85,
6_81,
6_77,
6_73,
6_69,
6_64,
6_60,
6_56,
6_55,
6_50,
6_46,
6_41,
6_36,
6_32,
6_27,
6_22,
6_18,
6_13,
6_12,
6_07,
6_02,
5_96,
5_91,
5_86,
5_80,
5_75,
5_70,
5_69,
5_63,
5_57,
5_51,
5_45,
5_39,
5_33,
5_27,
5_26,
5_19,
5_12,
5_05,
4_98,
4_91,
4_84,
4_83,
4_74,
4_66,
4_57,
4_49,
4_40,
4_39,
4_28,
4_18,
4_07,
3_96,
3_95,
3_81,
3_66,
3_52,
3_51,
3_30,
3_08,
3_07,
2_86,
2_64,
2_63,
2_42,
2_20,
2_19,
1_76,
1_75,
1_32,
1_31,
88,
44,
0,
]
a_ : Dict = [
9_99,
9_91,
9_82,
9_74,
9_66,
9_58,
9_50,
9_41,
9_33,
9_25,
9_16,
9_08,
9_00,
8_99,
8_74,
8_50,
8_25,
8_00,
7_99,
7_00,
6_00,
5_00,
4_00,
3_00,
2_00,
1_00,
0,
]
a_ : Tuple = [
9_99,
9_92,
9_85,
9_78,
9_71,
9_64,
9_57,
9_49,
9_42,
9_35,
9_28,
9_21,
9_14,
9_07,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_00,
2_99,
2_00,
1_99,
1_00,
99,
0,
]
a_ : Any = [
9_99,
9_96,
9_92,
9_89,
9_85,
9_82,
9_79,
9_75,
9_72,
9_68,
9_65,
9_61,
9_58,
9_55,
9_51,
9_48,
9_44,
9_41,
9_38,
9_34,
9_31,
9_27,
9_24,
9_20,
9_17,
9_14,
9_10,
9_07,
9_03,
9_00,
8_99,
8_91,
8_84,
8_76,
8_69,
8_61,
8_53,
8_46,
8_38,
8_30,
8_23,
8_15,
8_08,
8_00,
7_99,
7_88,
7_77,
7_66,
7_55,
7_44,
7_33,
7_22,
7_11,
7_00,
6_99,
6_88,
6_77,
6_66,
6_55,
6_44,
6_33,
6_22,
6_11,
6_00,
5_99,
5_85,
5_71,
5_57,
5_42,
5_28,
5_14,
5_00,
4_99,
4_85,
4_71,
4_57,
4_42,
4_28,
4_14,
4_00,
3_99,
3_79,
3_59,
3_40,
3_20,
3_00,
2_99,
2_79,
2_59,
2_40,
2_20,
2_00,
1_99,
1_66,
1_33,
1_00,
99,
66,
33,
0,
]
| 75 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''ylacombe/bark-small'''
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ ='''en_speaker_1'''
lowerCamelCase_ ='''This is a test string'''
lowerCamelCase_ ='''speaker_embeddings_path.json'''
lowerCamelCase_ ='''speaker_embeddings'''
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint, **lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =BarkProcessor(tokenizer=lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ =BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab() )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path, )
processor.save_pretrained(
self.tmpdirname, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path, speaker_embeddings_directory=self.speaker_embeddings_directory, )
lowerCamelCase_ =self.get_tokenizer(bos_token='''(BOS)''', eos_token='''(EOS)''' )
lowerCamelCase_ =BarkProcessor.from_pretrained(
self.tmpdirname, self.speaker_embeddings_dict_path, bos_token='''(BOS)''', eos_token='''(EOS)''', )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path, )
lowerCamelCase_ =35
lowerCamelCase_ =2
lowerCamelCase_ =8
lowerCamelCase_ ={
'''semantic_prompt''': np.ones(lowerCAmelCase ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowerCamelCase_ =processor(text=self.input_string, voice_preset=lowerCAmelCase )
lowerCamelCase_ =inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist(), processed_voice_preset.get(lowerCAmelCase, np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowerCamelCase_ =os.path.join(self.tmpdirname, '''file.npz''' )
np.savez(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =processor(text=self.input_string, voice_preset=lowerCAmelCase )
lowerCamelCase_ =inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist(), processed_voice_preset.get(lowerCAmelCase, np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowerCamelCase_ =processor(text=self.input_string, voice_preset=self.voice_preset )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =BarkProcessor(tokenizer=lowerCAmelCase )
lowerCamelCase_ =processor(text=self.input_string )
lowerCamelCase_ =tokenizer(
self.input_string, padding='''max_length''', max_length=256, add_special_tokens=lowerCAmelCase, return_attention_mask=lowerCAmelCase, return_token_type_ids=lowerCAmelCase, )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key].squeeze().tolist() )
| 75 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""convert_funnel_original_tf_checkpoint_to_pytorch""": [],
"""tokenization_funnel""": ["""FunnelTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[str] = ["""FunnelTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[int] = [
"""FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FunnelBaseModel""",
"""FunnelForMaskedLM""",
"""FunnelForMultipleChoice""",
"""FunnelForPreTraining""",
"""FunnelForQuestionAnswering""",
"""FunnelForSequenceClassification""",
"""FunnelForTokenClassification""",
"""FunnelModel""",
"""FunnelPreTrainedModel""",
"""load_tf_weights_in_funnel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = [
"""TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFFunnelBaseModel""",
"""TFFunnelForMaskedLM""",
"""TFFunnelForMultipleChoice""",
"""TFFunnelForPreTraining""",
"""TFFunnelForQuestionAnswering""",
"""TFFunnelForSequenceClassification""",
"""TFFunnelForTokenClassification""",
"""TFFunnelModel""",
"""TFFunnelPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
a_ : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 75 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
a_ : Tuple = logging.getLogger(__name__)
@dataclass(frozen=lowerCamelCase__ )
class __UpperCamelCase :
lowercase : str
lowercase : str
lowercase : Optional[str] =None
lowercase : Optional[str] =None
lowercase : Optional[str] =None
@dataclass(frozen=lowerCamelCase__ )
class __UpperCamelCase :
lowercase : List[int]
lowercase : Optional[List[int]] =None
lowercase : Optional[List[int]] =None
lowercase : Optional[Union[int, float]] =None
lowercase : Optional[int] =None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[InputFeatures]
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase=False, lowerCAmelCase = False, ):
"""simple docstring"""
lowerCamelCase_ =hans_processors[task]()
lowerCamelCase_ =os.path.join(
lowerCAmelCase, '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''', tokenizer.__class__.__name__, str(lowerCAmelCase ), lowerCAmelCase, ), )
lowerCamelCase_ =processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowerCamelCase_, lowerCamelCase_ =label_list[2], label_list[1]
lowerCamelCase_ =label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCamelCase_ =cached_features_file + '''.lock'''
with FileLock(lowerCAmelCase ):
if os.path.exists(lowerCAmelCase ) and not overwrite_cache:
logger.info(f'''Loading features from cached file {cached_features_file}''' )
lowerCamelCase_ =torch.load(lowerCAmelCase )
else:
logger.info(f'''Creating features from dataset file at {data_dir}''' )
lowerCamelCase_ =(
processor.get_dev_examples(lowerCAmelCase ) if evaluate else processor.get_train_examples(lowerCAmelCase )
)
logger.info('''Training examples: %s''', len(lowerCAmelCase ) )
lowerCamelCase_ =hans_convert_examples_to_features(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
logger.info('''Saving features into cached file %s''', lowerCAmelCase )
torch.save(self.features, lowerCAmelCase )
def __len__( self ):
"""simple docstring"""
return len(self.features )
def __getitem__( self, lowerCAmelCase ):
"""simple docstring"""
return self.features[i]
def lowercase__ ( self ):
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class __UpperCamelCase :
lowercase : List[InputFeatures]
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = 128, lowerCAmelCase=False, lowerCAmelCase = False, ):
"""simple docstring"""
lowerCamelCase_ =hans_processors[task]()
lowerCamelCase_ =processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowerCamelCase_, lowerCamelCase_ =label_list[2], label_list[1]
lowerCamelCase_ =label_list
lowerCamelCase_ =processor.get_dev_examples(lowerCAmelCase ) if evaluate else processor.get_train_examples(lowerCAmelCase )
lowerCamelCase_ =hans_convert_examples_to_features(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ), desc='''convert examples to features''' ):
if ex_index % 10_000 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(lowerCAmelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
lowerCamelCase_ =tf.data.Dataset.from_generator(
lowerCAmelCase, (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
), (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
), )
def lowercase__ ( self ):
"""simple docstring"""
return self.dataset
def __len__( self ):
"""simple docstring"""
return len(self.features )
def __getitem__( self, lowerCAmelCase ):
"""simple docstring"""
return self.features[i]
def lowercase__ ( self ):
"""simple docstring"""
return self.label_list
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase, '''heuristics_train_set.txt''' ) ), '''train''' )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase, '''heuristics_evaluation_set.txt''' ) ), '''dev''' )
def lowercase__ ( self ):
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
for i, line in enumerate(lowerCAmelCase ):
if i == 0:
continue
lowerCamelCase_ ='''%s-%s''' % (set_type, line[0])
lowerCamelCase_ =line[5]
lowerCamelCase_ =line[6]
lowerCamelCase_ =line[7][2:] if line[7].startswith('''ex''' ) else line[7]
lowerCamelCase_ =line[0]
examples.append(InputExample(guid=lowerCAmelCase, text_a=lowerCAmelCase, text_b=lowerCAmelCase, label=lowerCAmelCase, pairID=lowerCAmelCase ) )
return examples
def a_ ( __snake_case : List[InputExample] , __snake_case : List[str] , __snake_case : int , __snake_case : PreTrainedTokenizer , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ ={label: i for i, label in enumerate(__snake_case )}
lowerCamelCase_ =[]
for ex_index, example in tqdm.tqdm(enumerate(__snake_case ) , desc='''convert examples to features''' ):
if ex_index % 1_0000 == 0:
logger.info('''Writing example %d''' % (ex_index) )
lowerCamelCase_ =tokenizer(
example.text_a , example.text_b , add_special_tokens=__snake_case , max_length=__snake_case , padding='''max_length''' , truncation=__snake_case , return_overflowing_tokens=__snake_case , )
lowerCamelCase_ =label_map[example.label] if example.label in label_map else 0
lowerCamelCase_ =int(example.pairID )
features.append(InputFeatures(**__snake_case , label=__snake_case , pairID=__snake_case ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(F'''guid: {example}''' )
logger.info(F'''features: {features[i]}''' )
return features
a_ : int = {
"""hans""": 3,
}
a_ : List[str] = {
"""hans""": HansProcessor,
}
| 75 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
lowerCamelCase_ ={
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 16_000,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(self.tmpdirname, lowerCAmelCase )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
with open(self.feature_extraction_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
# load decoder from hub
lowerCamelCase_ ='''hf-internal-testing/ngram-beam-search-decoder'''
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.add_kwargs_tokens_map.copy()
kwargs.update(lowerCAmelCase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name, **lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer, lowerCAmelCase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor, lowerCAmelCase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels, decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set, decoder.model_container[decoder._model_key]._unigram_set, )
self.assertIsInstance(processor.decoder, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname, alpha=5.0, beta=3.0, score_boundary=-7.0, unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha, 5.0 )
self.assertEqual(processor.language_model.beta, 3.0 )
self.assertEqual(processor.language_model.score_boundary, -7.0 )
self.assertEqual(processor.language_model.unk_score_offset, 3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(lowerCAmelCase, '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=lowerCAmelCase, feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =floats_list((3, 1_000) )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =processor(lowerCAmelCase, return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ ='''This is a test string'''
lowerCamelCase_ =processor(text=lowerCAmelCase )
lowerCamelCase_ =tokenizer(lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def lowercase__ ( self, lowerCAmelCase=(2, 10, 16), lowerCAmelCase=77 ):
"""simple docstring"""
np.random.seed(lowerCAmelCase )
return np.random.rand(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits(shape=(10, 16), seed=13 )
lowerCamelCase_ =processor.decode(lowerCAmelCase )
lowerCamelCase_ =decoder.decode_beams(lowerCAmelCase )[0]
self.assertEqual(decoded_decoder[0], decoded_processor.text )
self.assertEqual('''</s> <s> </s>''', decoded_processor.text )
self.assertEqual(decoded_decoder[-2], decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1], decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase )
else:
with get_context(lowerCAmelCase ).Pool() as pool:
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =list(lowerCAmelCase )
with get_context('''fork''' ).Pool() as p:
lowerCamelCase_ =decoder.decode_beams_batch(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =[], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowerCAmelCase, decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''], decoded_processor.text )
self.assertListEqual(lowerCAmelCase, decoded_processor.logit_score )
self.assertListEqual(lowerCAmelCase, decoded_processor.lm_score )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =15
lowerCamelCase_ =-2_0.0
lowerCamelCase_ =-4.0
lowerCamelCase_ =processor.batch_decode(
lowerCAmelCase, beam_width=lowerCAmelCase, beam_prune_logp=lowerCAmelCase, token_min_logp=lowerCAmelCase, )
lowerCamelCase_ =decoded_processor_out.text
lowerCamelCase_ =list(lowerCAmelCase )
with get_context('''fork''' ).Pool() as pool:
lowerCamelCase_ =decoder.decode_beams_batch(
lowerCAmelCase, lowerCAmelCase, beam_width=lowerCAmelCase, beam_prune_logp=lowerCAmelCase, token_min_logp=lowerCAmelCase, )
lowerCamelCase_ =[d[0][0] for d in decoded_decoder_out]
lowerCamelCase_ =[d[0][2] for d in decoded_decoder_out]
lowerCamelCase_ =[d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''], lowerCAmelCase )
self.assertTrue(np.array_equal(lowerCAmelCase, decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7], lowerCAmelCase, atol=1e-3 ) )
self.assertTrue(np.array_equal(lowerCAmelCase, decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4], lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =2.0
lowerCamelCase_ =5.0
lowerCamelCase_ =-2_0.0
lowerCamelCase_ =True
lowerCamelCase_ =processor.batch_decode(
lowerCAmelCase, alpha=lowerCAmelCase, beta=lowerCAmelCase, unk_score_offset=lowerCAmelCase, lm_score_boundary=lowerCAmelCase, )
lowerCamelCase_ =decoded_processor_out.text
lowerCamelCase_ =list(lowerCAmelCase )
decoder.reset_params(
alpha=lowerCAmelCase, beta=lowerCAmelCase, unk_score_offset=lowerCAmelCase, lm_score_boundary=lowerCAmelCase, )
with get_context('''fork''' ).Pool() as pool:
lowerCamelCase_ =decoder.decode_beams_batch(
lowerCAmelCase, lowerCAmelCase, )
lowerCamelCase_ =[d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''], lowerCAmelCase )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha, 2.0 )
self.assertEqual(lm_model.beta, 5.0 )
self.assertEqual(lm_model.unk_score_offset, -2_0.0 )
self.assertEqual(lm_model.score_boundary, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
lowerCamelCase_ =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
lowerCamelCase_ =os.listdir(lowerCAmelCase )
lowerCamelCase_ =['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =snapshot_download('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
lowerCamelCase_ =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
lowerCamelCase_ =os.listdir(lowerCAmelCase )
lowerCamelCase_ =os.listdir(lowerCAmelCase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =floats_list((3, 1_000) )
lowerCamelCase_ =processor_wavaveca(lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =processor_auto(lowerCAmelCase, return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum(), input_auto[key].sum(), delta=1e-2 )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =processor_wavaveca.batch_decode(lowerCAmelCase )
lowerCamelCase_ =processor_auto.batch_decode(lowerCAmelCase )
self.assertListEqual(decoded_wavaveca.text, decoded_auto.text )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
self.assertListEqual(
processor.model_input_names, feature_extractor.model_input_names, msg='''`processor` and `feature_extractor` model input names do not match''', )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[d[key] for d in offsets]
return retrieved_list
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =self._get_dummy_logits()[0]
lowerCamelCase_ =processor.decode(lowerCAmelCase, output_word_offsets=lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ), 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(lowerCAmelCase, lowerCAmelCase ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''], '''word''' ) ), outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''word''' ), ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''start_offset''' ), [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''end_offset''' ), [1, 3, 5] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase, output_word_offsets=lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ), 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(lowerCAmelCase, lowerCAmelCase ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ) for o in outputs['''word_offsets''']], outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''word''' ), ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''start_offset''' ), [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''end_offset''' ), [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =load_dataset('''common_voice''', '''en''', split='''train''', streaming=lowerCAmelCase )
lowerCamelCase_ =ds.cast_column('''audio''', datasets.Audio(sampling_rate=16_000 ) )
lowerCamelCase_ =iter(lowerCAmelCase )
lowerCamelCase_ =next(lowerCAmelCase )
lowerCamelCase_ =AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
lowerCamelCase_ =WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
lowerCamelCase_ =processor(sample['''audio''']['''array'''], return_tensors='''pt''' ).input_values
with torch.no_grad():
lowerCamelCase_ =model(lowerCAmelCase ).logits.cpu().numpy()
lowerCamelCase_ =processor.decode(logits[0], output_word_offsets=lowerCAmelCase )
lowerCamelCase_ =model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
lowerCamelCase_ =[
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
lowerCamelCase_ ='''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ), lowerCAmelCase )
self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ), output.text )
# output times
lowerCamelCase_ =torch.tensor(self.get_from_offsets(lowerCAmelCase, '''start_time''' ) )
lowerCamelCase_ =torch.tensor(self.get_from_offsets(lowerCAmelCase, '''end_time''' ) )
# fmt: off
lowerCamelCase_ =torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
lowerCamelCase_ =torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=0.0_1 ) )
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=0.0_1 ) )
| 75 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Any =['image_processor', 'tokenizer']
lowercase : Optional[int] ='BridgeTowerImageProcessor'
lowercase : str =('RobertaTokenizer', 'RobertaTokenizerFast')
def __init__( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
super().__init__(lowerCAmelCase, lowerCAmelCase )
def __call__( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = True, lowerCAmelCase = False, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = 0, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = True, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =self.tokenizer(
text=lowerCAmelCase, add_special_tokens=lowerCAmelCase, padding=lowerCAmelCase, truncation=lowerCAmelCase, max_length=lowerCAmelCase, stride=lowerCAmelCase, pad_to_multiple_of=lowerCAmelCase, return_token_type_ids=lowerCAmelCase, return_attention_mask=lowerCAmelCase, return_overflowing_tokens=lowerCAmelCase, return_special_tokens_mask=lowerCAmelCase, return_offsets_mapping=lowerCAmelCase, return_length=lowerCAmelCase, verbose=lowerCAmelCase, return_tensors=lowerCAmelCase, **lowerCAmelCase, )
# add pixel_values + pixel_mask
lowerCamelCase_ =self.image_processor(
lowerCAmelCase, return_tensors=lowerCAmelCase, do_normalize=lowerCAmelCase, do_center_crop=lowerCAmelCase, **lowerCAmelCase )
encoding.update(lowerCAmelCase )
return encoding
def lowercase__ ( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase, **lowerCAmelCase )
@property
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.tokenizer.model_input_names
lowerCamelCase_ =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 75 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =StableDiffusionInstructPixaPixPipeline
lowercase : List[Any] =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
lowercase : Optional[Any] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase : Union[str, Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase : List[Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=8, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=32, )
lowerCamelCase_ =PNDMScheduler(skip_prk_steps=lowerCAmelCase )
torch.manual_seed(0 )
lowerCamelCase_ =AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
torch.manual_seed(0 )
lowerCamelCase_ =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, )
lowerCamelCase_ =CLIPTextModel(lowerCAmelCase )
lowerCamelCase_ =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCamelCase_ ={
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowerCamelCase_ =image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase_ =Image.fromarray(np.uinta(lowerCAmelCase ) ).convert('''RGB''' )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ ='''french fries'''
lowerCamelCase_ =sd_pipe(**lowerCAmelCase, negative_prompt=lowerCAmelCase )
lowerCamelCase_ =output.images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =[inputs['''prompt''']] * 2
lowerCamelCase_ =np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
lowerCamelCase_ =torch.from_numpy(lowerCAmelCase ).unsqueeze(0 ).to(lowerCAmelCase )
lowerCamelCase_ =image / 2 + 0.5
lowerCamelCase_ =image.permute(0, 3, 1, 2 )
lowerCamelCase_ =image.repeat(2, 1, 1, 1 )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
lowerCamelCase_ =np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='''scaled_linear''' )
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
lowerCamelCase_ =[round(lowerCAmelCase, 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(lowerCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =VaeImageProcessor(do_resize=lowerCAmelCase, do_normalize=lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' ) )[0]
lowerCamelCase_ =components['''vae''']
lowerCamelCase_ =self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
lowerCamelCase_ =vae.encode(inputs[image_param] ).latent_dist.mode()
lowerCamelCase_ =pipe(**lowerCAmelCase )[0]
lowerCamelCase_ =np.abs(out - out_latents_inputs ).max()
self.assertLess(lowerCAmelCase, 1e-4, '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
lowerCamelCase_ ={
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0
def callback_fn(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ) -> None:
lowerCamelCase_ =True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowerCamelCase_ =False
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
pipe(**lowerCAmelCase, callback=lowerCAmelCase, callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowercase__ ( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCamelCase_ =inputs['''image'''].resize((504, 504) )
lowerCamelCase_ ='''timbrooks/instruct-pix2pix'''
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
lowerCAmelCase, safety_checker=lowerCAmelCase, )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =output.images[0]
lowerCamelCase_ =image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
lowerCamelCase_ =np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 75 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def a_ ( __snake_case : int ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
lowerCamelCase_ =True if '''large''' in model_name or '''huge''' in model_name else False
lowerCamelCase_ =True if '''large''' in model_name or '''huge''' in model_name else False
lowerCamelCase_ =True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowerCamelCase_ =[3, 3, 3, 3]
lowerCamelCase_ =[5, 5, 5, 5]
elif "fl4" in model_name:
lowerCamelCase_ =[4, 4, 4, 4]
lowerCamelCase_ =[3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowerCamelCase_ =[3, 3, 3, 3]
if "lrf" in model_name:
lowerCamelCase_ =[3, 3, 3, 3]
else:
lowerCamelCase_ =[2, 2, 2, 2]
if "tiny" in model_name:
lowerCamelCase_ =96
elif "small" in model_name:
lowerCamelCase_ =96
elif "base" in model_name:
lowerCamelCase_ =128
elif "large" in model_name:
lowerCamelCase_ =192
elif "xlarge" in model_name:
lowerCamelCase_ =256
elif "huge" in model_name:
lowerCamelCase_ =352
# set label information
lowerCamelCase_ ='''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
lowerCamelCase_ ='''imagenet-22k-id2label.json'''
else:
lowerCamelCase_ ='''imagenet-1k-id2label.json'''
lowerCamelCase_ =json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type='''dataset''' ) , '''r''' ) )
lowerCamelCase_ ={int(__snake_case ): v for k, v in idalabel.items()}
lowerCamelCase_ ={v: k for k, v in idalabel.items()}
lowerCamelCase_ =FocalNetConfig(
embed_dim=__snake_case , depths=__snake_case , focal_levels=__snake_case , focal_windows=__snake_case , use_conv_embed=__snake_case , idalabel=__snake_case , labelaid=__snake_case , use_post_layernorm=__snake_case , use_layerscale=__snake_case , )
return config
def a_ ( __snake_case : Any ) -> int:
"""simple docstring"""
if "patch_embed.proj" in name:
lowerCamelCase_ =name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowerCamelCase_ =name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
lowerCamelCase_ ='''encoder.''' + name
if "encoder.layers" in name:
lowerCamelCase_ =name.replace('''encoder.layers''' , '''encoder.stages''' )
if "downsample.proj" in name:
lowerCamelCase_ =name.replace('''downsample.proj''' , '''downsample.projection''' )
if "blocks" in name:
lowerCamelCase_ =name.replace('''blocks''' , '''layers''' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowerCamelCase_ =name.replace('''modulation.f''' , '''modulation.projection_in''' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowerCamelCase_ =name.replace('''modulation.h''' , '''modulation.projection_context''' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowerCamelCase_ =name.replace('''modulation.proj''' , '''modulation.projection_out''' )
if name == "norm.weight":
lowerCamelCase_ ='''layernorm.weight'''
if name == "norm.bias":
lowerCamelCase_ ='''layernorm.bias'''
if "head" in name:
lowerCamelCase_ =name.replace('''head''' , '''classifier''' )
else:
lowerCamelCase_ ='''focalnet.''' + name
return name
def a_ ( __snake_case : List[Any] , __snake_case : List[str] , __snake_case : int=False ) -> Optional[int]:
"""simple docstring"""
# fmt: off
lowerCamelCase_ ={
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
lowerCamelCase_ =model_name_to_url[model_name]
print('''Checkpoint URL: ''' , __snake_case )
lowerCamelCase_ =torch.hub.load_state_dict_from_url(__snake_case , map_location='''cpu''' )['''model''']
# rename keys
for key in state_dict.copy().keys():
lowerCamelCase_ =state_dict.pop(__snake_case )
lowerCamelCase_ =val
lowerCamelCase_ =get_focalnet_config(__snake_case )
lowerCamelCase_ =FocalNetForImageClassification(__snake_case )
model.eval()
# load state dict
model.load_state_dict(__snake_case )
# verify conversion
lowerCamelCase_ ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCamelCase_ =BitImageProcessor(
do_resize=__snake_case , size={'''shortest_edge''': 256} , resample=PILImageResampling.BILINEAR , do_center_crop=__snake_case , crop_size=224 , do_normalize=__snake_case , image_mean=__snake_case , image_std=__snake_case , )
lowerCamelCase_ =Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
lowerCamelCase_ =processor(images=__snake_case , return_tensors='''pt''' )
lowerCamelCase_ =transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
lowerCamelCase_ =image_transforms(__snake_case ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , __snake_case , atol=1e-4 )
lowerCamelCase_ =model(**__snake_case )
lowerCamelCase_ =outputs.logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
print('''First values of logits:''' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowerCamelCase_ =torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
lowerCamelCase_ =torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
lowerCamelCase_ =torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
lowerCamelCase_ =torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
lowerCamelCase_ =torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
lowerCamelCase_ =torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
assert torch.allclose(outputs.logits[0, :3] , __snake_case , atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__snake_case )
processor.save_pretrained(__snake_case )
if push_to_hub:
print(F'''Pushing model and processor of {model_name} to the hub...''' )
model.push_to_hub(F'''{model_name}''' )
processor.push_to_hub(F'''{model_name}''' )
if __name__ == "__main__":
a_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""focalnet-tiny""",
type=str,
help="""Name of the FocalNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub.""",
)
a_ : int = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 75 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __UpperCamelCase :
lowercase : Union[str, Any] =XGLMConfig
lowercase : Optional[Any] ={}
lowercase : Optional[int] ='gelu'
def __init__( self, lowerCAmelCase, lowerCAmelCase=14, lowerCAmelCase=7, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=99, lowerCAmelCase=32, lowerCAmelCase=2, lowerCAmelCase=4, lowerCAmelCase=37, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=512, lowerCAmelCase=0.0_2, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =is_training
lowerCamelCase_ =use_input_mask
lowerCamelCase_ =use_labels
lowerCamelCase_ =vocab_size
lowerCamelCase_ =d_model
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =ffn_dim
lowerCamelCase_ =activation_function
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =initializer_range
lowerCamelCase_ =None
lowerCamelCase_ =0
lowerCamelCase_ =2
lowerCamelCase_ =1
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length], self.vocab_size ), clip_value_min=0, clip_value_max=3 )
lowerCamelCase_ =None
if self.use_input_mask:
lowerCamelCase_ =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ =self.get_config()
lowerCamelCase_ =floats_tensor([self.num_hidden_layers, self.num_attention_heads], 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size, d_model=self.hidden_size, num_layers=self.num_hidden_layers, attention_heads=self.num_attention_heads, ffn_dim=self.ffn_dim, activation_function=self.activation_function, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=lowerCAmelCase, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =config_and_inputs
lowerCamelCase_ ={
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : int =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowercase : Optional[Any] =(TFXGLMForCausalLM,) if is_tf_available() else ()
lowercase : Tuple =(
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowercase : Optional[Any] =False
lowercase : Optional[Any] =False
lowercase : Optional[int] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, n_embd=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =TFXGLMModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
def lowercase__ ( self ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def lowercase__ ( self, lowerCAmelCase=True ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =tf.convert_to_tensor([[2, 268, 9_865]], dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
lowerCamelCase_ =[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
# fmt: on
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist(), lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
tf.random.set_seed(0 )
lowerCamelCase_ =tokenizer('''Today is a nice day and''', return_tensors='''tf''' )
lowerCamelCase_ =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(''':/CPU:0''' ):
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, seed=[7, 0] )
lowerCamelCase_ =tokenizer.decode(output_ids[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =(
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ ='''left'''
# use different length sentences to test batching
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
lowerCamelCase_ =tokenizer(lowerCAmelCase, return_tensors='''tf''', padding=lowerCAmelCase )
lowerCamelCase_ =inputs['''input_ids''']
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, attention_mask=inputs['''attention_mask'''], max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[0], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[1], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer.batch_decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_non_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [non_padded_sentence, padded_sentence] )
| 75 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def a_ ( __snake_case : str = "AAPL" ) -> str:
"""simple docstring"""
lowerCamelCase_ =F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowerCamelCase_ =BeautifulSoup(requests.get(__snake_case ).text , '''html.parser''' )
lowerCamelCase_ ='''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
| 75 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __UpperCamelCase :
@staticmethod
def lowercase__ ( *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
lowercase : int =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =object_detector(examples[0], threshold=0.0 )
lowerCamelCase_ =len(lowerCAmelCase )
self.assertGreater(lowerCAmelCase, 0 )
self.assertEqual(
lowerCAmelCase, [
{
'''score''': ANY(lowerCAmelCase ),
'''label''': ANY(lowerCAmelCase ),
'''box''': {'''xmin''': ANY(lowerCAmelCase ), '''ymin''': ANY(lowerCAmelCase ), '''xmax''': ANY(lowerCAmelCase ), '''ymax''': ANY(lowerCAmelCase )},
}
for i in range(lowerCAmelCase )
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
]
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0.2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], top_k=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
], )
| 75 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
a_ : List[str] = yaml.safe_load(
"""\
name: \"\"
allow_empty: false
allow_empty_text: true
subsections:
- name: \"Dataset Card for X\" # First-level markdown heading
allow_empty: false
allow_empty_text: true
subsections:
- name: \"Table of Contents\"
allow_empty: false
allow_empty_text: false
subsections: null
- name: \"Dataset Description\"
allow_empty: false
allow_empty_text: false
subsections:
- name: \"Dataset Summary\"
allow_empty: false
allow_empty_text: false
subsections: null
- name: \"Supported Tasks and Leaderboards\"
allow_empty: true
allow_empty_text: true
subsections: null
- name: Languages
allow_empty: false
allow_empty_text: true
subsections: null
"""
)
a_ : int = {
"""name""": """root""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [
{
"""name""": """Dataset Card for My Dataset""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [
{"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
{
"""name""": """Dataset Description""",
"""text""": """Some text here.""",
"""is_empty_text""": False,
"""subsections""": [
{
"""name""": """Dataset Summary""",
"""text""": """Some text here.""",
"""is_empty_text""": False,
"""subsections""": [],
},
{
"""name""": """Supported Tasks and Leaderboards""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [],
},
{"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
],
},
],
}
],
}
a_ : List[str] = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : str = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : List[str] = {
"""name""": """root""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [
{
"""name""": """Dataset Card for My Dataset""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [
{"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
{
"""name""": """Dataset Description""",
"""text""": """Some text here.""",
"""is_empty_text""": False,
"""subsections""": [
{
"""name""": """Dataset Summary""",
"""text""": """Some text here.""",
"""is_empty_text""": False,
"""subsections""": [
{
"""name""": """Extra Ignored Subsection""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [],
}
],
},
{
"""name""": """Supported Tasks and Leaderboards""",
"""text""": """""",
"""is_empty_text""": True,
"""subsections""": [],
},
{"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
],
},
],
}
],
}
a_ : str = """\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : Tuple = (
"""The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."""
)
a_ : List[Any] = """\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : Dict = (
"""The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."""
)
a_ : List[str] = """\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : str = """The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."""
a_ : Optional[Any] = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : int = """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."""
a_ : Optional[Any] = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
"""
a_ : List[Any] = """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."""
a_ : Any = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
"""
a_ : Optional[Any] = """The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."""
a_ : int = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
"""
a_ : str = """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."""
a_ : int = """\
---
language:
- zh
- en
---
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : List[Any] = """The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."""
a_ : int = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
"""
a_ : Optional[Any] = """The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."""
a_ : Dict = """\
---
language:
- zh
- en
---
# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : List[Any] = """The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."""
a_ : Optional[Any] = """"""
a_ : Tuple = """The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."""
a_ : Optional[Any] = """\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
a_ : Dict = """The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."""
@pytest.mark.parametrize(
'''readme_md, expected_dict''' , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def a_ ( __snake_case : Tuple , __snake_case : str ) -> str:
"""simple docstring"""
assert ReadMe.from_string(__snake_case , __snake_case ).to_dict() == expected_dict
@pytest.mark.parametrize(
'''readme_md, expected_error''' , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def a_ ( __snake_case : List[str] , __snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
with pytest.raises(__snake_case , match=re.escape(expected_error.format(path='''root''' ) ) ):
lowerCamelCase_ =ReadMe.from_string(__snake_case , __snake_case )
readme.validate()
@pytest.mark.parametrize(
'''readme_md, expected_error''' , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def a_ ( __snake_case : Union[str, Any] , __snake_case : Dict ) -> List[str]:
"""simple docstring"""
with pytest.raises(__snake_case , match=re.escape(expected_error.format(path='''root''' ) ) ):
ReadMe.from_string(__snake_case , __snake_case )
@pytest.mark.parametrize(
'''readme_md,''' , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def a_ ( __snake_case : Tuple ) -> int:
"""simple docstring"""
ReadMe.from_string(__snake_case , __snake_case , suppress_parsing_errors=__snake_case )
@pytest.mark.parametrize(
'''readme_md, expected_dict''' , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def a_ ( __snake_case : Tuple , __snake_case : Tuple ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ =Path(__snake_case ) / '''README.md'''
with open(__snake_case , '''w+''' ) as readme_file:
readme_file.write(__snake_case )
lowerCamelCase_ =ReadMe.from_readme(__snake_case , __snake_case ).to_dict()
assert out["name"] == path
assert out["text"] == ""
assert out["is_empty_text"]
assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
'''readme_md, expected_error''' , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def a_ ( __snake_case : Union[str, Any] , __snake_case : int ) -> str:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ =Path(__snake_case ) / '''README.md'''
with open(__snake_case , '''w+''' ) as readme_file:
readme_file.write(__snake_case )
lowerCamelCase_ =expected_error.format(path=__snake_case )
with pytest.raises(__snake_case , match=re.escape(__snake_case ) ):
lowerCamelCase_ =ReadMe.from_readme(__snake_case , __snake_case )
readme.validate()
@pytest.mark.parametrize(
'''readme_md, expected_error''' , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def a_ ( __snake_case : List[Any] , __snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ =Path(__snake_case ) / '''README.md'''
with open(__snake_case , '''w+''' ) as readme_file:
readme_file.write(__snake_case )
lowerCamelCase_ =expected_error.format(path=__snake_case )
with pytest.raises(__snake_case , match=re.escape(__snake_case ) ):
ReadMe.from_readme(__snake_case , __snake_case )
@pytest.mark.parametrize(
'''readme_md,''' , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def a_ ( __snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase_ =Path(__snake_case ) / '''README.md'''
with open(__snake_case , '''w+''' ) as readme_file:
readme_file.write(__snake_case )
ReadMe.from_readme(__snake_case , __snake_case , suppress_parsing_errors=__snake_case )
| 75 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[int] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a_ : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a_ : Optional[int] = {"""facebook/blenderbot_small-90M""": 5_12}
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =set()
lowerCamelCase_ =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ =char
lowerCamelCase_ =set(__snake_case )
return pairs
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[int] =VOCAB_FILES_NAMES
lowercase : Tuple =PRETRAINED_VOCAB_FILES_MAP
lowercase : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Dict =['input_ids', 'attention_mask']
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase="__start__", lowerCAmelCase="__end__", lowerCAmelCase="__unk__", lowerCAmelCase="__null__", **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(unk_token=lowerCAmelCase, bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, pad_token=lowerCAmelCase, **lowerCAmelCase )
with open(lowerCAmelCase, encoding='''utf-8''' ) as vocab_handle:
lowerCamelCase_ =json.load(lowerCAmelCase )
lowerCamelCase_ ={v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase, encoding='''utf-8''' ) as merges_handle:
lowerCamelCase_ =merges_handle.read().split('''\n''' )[1:-1]
lowerCamelCase_ =[tuple(merge.split() ) for merge in merges]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={}
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.encoder )
def lowercase__ ( self ):
"""simple docstring"""
return dict(self.encoder, **self.added_tokens_encoder )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
lowerCamelCase_ =re.sub('''([.,!?()])''', R''' \1''', lowerCAmelCase )
lowerCamelCase_ =re.sub('''(\')''', R''' \1 ''', lowerCAmelCase )
lowerCamelCase_ =re.sub(R'''\s{2,}''', ''' ''', lowerCAmelCase )
if "\n" in token:
lowerCamelCase_ =token.replace('''\n''', ''' __newln__''' )
lowerCamelCase_ =token.split(''' ''' )
lowerCamelCase_ =[]
for token in tokens:
if not len(lowerCAmelCase ):
continue
lowerCamelCase_ =token.lower()
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
lowerCamelCase_ =get_pairs(lowerCAmelCase )
if not pairs:
words.append(lowerCAmelCase )
continue
while True:
lowerCamelCase_ =min(lowerCAmelCase, key=lambda lowerCAmelCase : self.bpe_ranks.get(lowerCAmelCase, float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_, lowerCamelCase_ =bigram
lowerCamelCase_ =[]
lowerCamelCase_ =0
while i < len(lowerCAmelCase ):
try:
lowerCamelCase_ =word.index(lowerCAmelCase, lowerCAmelCase )
new_word.extend(word[i:j] )
lowerCamelCase_ =j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =new_word
if len(lowerCAmelCase ) == 1:
break
else:
lowerCamelCase_ =get_pairs(lowerCAmelCase )
lowerCamelCase_ ='''@@ '''.join(lowerCAmelCase )
lowerCamelCase_ =word[:-4]
lowerCamelCase_ =word
words.append(lowerCAmelCase )
return " ".join(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =re.findall(R'''\S+\n?''', lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =token.lower()
return self.encoder.get(lowerCAmelCase, self.encoder.get(self.unk_token ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase, self.unk_token )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''' '''.join(lowerCAmelCase ).replace('''@@ ''', '''''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=lowerCAmelCase, ensure_ascii=lowerCAmelCase ) + '''\n''' )
lowerCamelCase_ =0
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
lowerCamelCase_ =token_index
writer.write(''' '''.join(lowerCAmelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 75 | 1 |
'''simple docstring'''
import os
def a_ ( ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =os.path.join(os.path.dirname(__snake_case ) , '''num.txt''' )
with open(__snake_case ) as file_hand:
return str(sum(int(__snake_case ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 75 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[str] ='efficientformer'
def __init__( self, lowerCAmelCase = [3, 2, 6, 4], lowerCAmelCase = [48, 96, 224, 448], lowerCAmelCase = [True, True, True, True], lowerCAmelCase = 448, lowerCAmelCase = 32, lowerCAmelCase = 4, lowerCAmelCase = 7, lowerCAmelCase = 5, lowerCAmelCase = 8, lowerCAmelCase = 4, lowerCAmelCase = 0.0, lowerCAmelCase = 16, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 2, lowerCAmelCase = 1, lowerCAmelCase = 0.0, lowerCAmelCase = 1, lowerCAmelCase = True, lowerCAmelCase = True, lowerCAmelCase = 1e-5, lowerCAmelCase = "gelu", lowerCAmelCase = 0.0_2, lowerCAmelCase = 1e-12, lowerCAmelCase = 224, lowerCAmelCase = 1e-05, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =hidden_sizes
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =initializer_range
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =depths
lowerCamelCase_ =mlp_expansion_ratio
lowerCamelCase_ =downsamples
lowerCamelCase_ =dim
lowerCamelCase_ =key_dim
lowerCamelCase_ =attention_ratio
lowerCamelCase_ =resolution
lowerCamelCase_ =pool_size
lowerCamelCase_ =downsample_patch_size
lowerCamelCase_ =downsample_stride
lowerCamelCase_ =downsample_pad
lowerCamelCase_ =drop_path_rate
lowerCamelCase_ =num_metaad_blocks
lowerCamelCase_ =distillation
lowerCamelCase_ =use_layer_scale
lowerCamelCase_ =layer_scale_init_value
lowerCamelCase_ =image_size
lowerCamelCase_ =batch_norm_eps
| 75 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
a_ : Optional[int] = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 75 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
a_ : Union[str, Any] = random.Random()
def a_ ( __snake_case : int , __snake_case : int=1.0 , __snake_case : Tuple=None , __snake_case : Union[str, Any]=None ) -> str:
"""simple docstring"""
if rng is None:
lowerCamelCase_ =global_rng
lowerCamelCase_ =[]
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self, lowerCAmelCase, lowerCAmelCase=7, lowerCAmelCase=400, lowerCAmelCase=2_000, lowerCAmelCase=24, lowerCAmelCase=24, lowerCAmelCase=0.0, lowerCAmelCase=16_000, lowerCAmelCase=True, lowerCAmelCase=True, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =min_seq_length
lowerCamelCase_ =max_seq_length
lowerCamelCase_ =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase_ =feature_size
lowerCamelCase_ =num_mel_bins
lowerCamelCase_ =padding_value
lowerCamelCase_ =sampling_rate
lowerCamelCase_ =return_attention_mask
lowerCamelCase_ =do_normalize
def lowercase__ ( self ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowercase__ ( self, lowerCAmelCase=False, lowerCAmelCase=False ):
"""simple docstring"""
def _flatten(lowerCAmelCase ):
return list(itertools.chain(*lowerCAmelCase ) )
if equal_length:
lowerCamelCase_ =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCamelCase_ =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Any =SpeechaTextFeatureExtractor if is_speech_available() else None
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextFeatureExtractionTester(self )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
self.assertTrue(np.all(np.mean(lowerCAmelCase, axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase, axis=0 ) - 1 ) < 1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]
# Test feature size
lowerCamelCase_ =feature_extractor(lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
lowerCamelCase_ =feature_extractor(speech_inputs[0], return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(np_speech_inputs[0], return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test batched
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase_ =[floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCamelCase_ =np.asarray(lowerCAmelCase )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding=lowerCAmelCase, max_length=lowerCAmelCase, return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, max_length=lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''max_length''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24) )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=16, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24) )
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =np.random.rand(100, 32 ).astype(np.floataa )
lowerCamelCase_ =np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
from datasets import load_dataset
lowerCamelCase_ =load_dataset('''hf-internal-testing/librispeech_asr_dummy''', '''clean''', split='''validation''' )
# automatic decoding with librispeech
lowerCamelCase_ =ds.sort('''id''' ).select(range(lowerCAmelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =np.array([
-1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
-1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
-1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
] )
# fmt: on
lowerCamelCase_ =self._load_datasamples(1 )
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape, (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30], lowerCAmelCase, atol=1e-4 ) )
| 75 | 1 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCamelCase ( lowerCamelCase__ ):
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
lowerCamelCase_ ='''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
lowerCamelCase_ ='''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(lowerCAmelCase )
BertModel.from_pretrained(lowerCAmelCase )
BertTokenizer.from_pretrained(lowerCAmelCase )
pipeline(task='''fill-mask''', model=lowerCAmelCase )
# baseline - just load from_pretrained with normal network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
lowerCamelCase_ =self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
lowerCamelCase_ ='''1'''
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
lowerCamelCase_ ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
lowerCamelCase_ ='''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(lowerCAmelCase )
BertModel.from_pretrained(lowerCAmelCase )
BertTokenizer.from_pretrained(lowerCAmelCase )
pipeline(task='''fill-mask''', model=lowerCAmelCase )
# baseline - just load from_pretrained with normal network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
lowerCamelCase_ =self.get_env()
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import BertConfig, BertModel, BertTokenizer
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
'''
lowerCamelCase_ ='''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
'''
# baseline - just load from_pretrained with normal network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
lowerCamelCase_ =self.get_env()
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
# next emulate no network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
lowerCamelCase_ ='''1'''
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import pipeline
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
'''
lowerCamelCase_ ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
'''
lowerCamelCase_ =self.get_env()
lowerCamelCase_ ='''1'''
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 1, result.stderr )
self.assertIn(
'''You cannot infer task automatically within `pipeline` when using offline mode''', result.stderr.decode().replace('''\n''', '''''' ), )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''
from transformers import AutoModel
'''
lowerCamelCase_ ='''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
'''
# baseline - just load from_pretrained with normal network
lowerCamelCase_ =[sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
lowerCamelCase_ =self.get_env()
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
lowerCamelCase_ ='''1'''
lowerCamelCase_ =subprocess.run(lowerCAmelCase, env=lowerCAmelCase, check=lowerCAmelCase, capture_output=lowerCAmelCase )
self.assertEqual(result.returncode, 0, result.stderr )
self.assertIn('''success''', result.stdout.decode() )
| 75 |
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in table:
res += inp[i - 1]
return res
def a_ ( __snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
return data[1:] + data[0]
def a_ ( __snake_case : str , __snake_case : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def a_ ( __snake_case : Optional[Any] , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =int('''0b''' + data[0] + data[-1] , 2 )
lowerCamelCase_ =int('''0b''' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def a_ ( __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : int , __snake_case : Tuple , __snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =message[:4]
lowerCamelCase_ =message[4:]
lowerCamelCase_ =apply_table(__snake_case , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
lowerCamelCase_ =apply_sbox(__snake_case , temp[:4] ) # noqa: E741
lowerCamelCase_ =apply_sbox(__snake_case , temp[4:] )
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + l # noqa: E741
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + r
lowerCamelCase_ =apply_table(l + r , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
a_ : Any = input("""Enter 10 bit key: """)
a_ : Any = input("""Enter 8 bit message: """)
a_ : str = [6, 3, 7, 4, 8, 5, 10, 9]
a_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
a_ : str = [2, 4, 3, 1]
a_ : Optional[int] = [2, 6, 3, 1, 4, 8, 5, 7]
a_ : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
a_ : Union[str, Any] = [4, 1, 2, 3, 2, 3, 4, 1]
a_ : int = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
a_ : Any = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
a_ : List[Any] = apply_table(key, paa_table)
a_ : str = temp[:5]
a_ : Optional[Any] = temp[5:]
a_ : Tuple = left_shift(left)
a_ : Optional[Any] = left_shift(right)
a_ : str = apply_table(left + right, pa_table)
a_ : Optional[Any] = left_shift(left)
a_ : Tuple = left_shift(right)
a_ : Union[str, Any] = left_shift(left)
a_ : List[str] = left_shift(right)
a_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
a_ : Optional[int] = apply_table(message, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : str = temp[4:] + temp[:4]
a_ : List[str] = function(expansion, sa, sa, keya, temp)
a_ : Union[str, Any] = apply_table(temp, IP_inv)
print("""Cipher text is:""", CT)
# decryption
a_ : Optional[int] = apply_table(CT, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : int = temp[4:] + temp[:4]
a_ : int = function(expansion, sa, sa, keya, temp)
a_ : Optional[int] = apply_table(temp, IP_inv)
print("""Plain text after decypting is:""", PT)
| 75 | 1 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
a_ : Optional[int] = """us-east-1""" # defaults region
@dataclass
class __UpperCamelCase :
lowercase : str
lowercase : Optional[Any] ='arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase : Any ={
'task_name': 'mnli',
'per_device_train_batch_size': 16,
'per_device_eval_batch_size': 16,
'do_train': True,
'do_eval': True,
'do_predict': True,
'output_dir': '/opt/ml/model',
'overwrite_output_dir': True,
'max_steps': 5_00,
'save_steps': 55_00,
}
lowercase : int ={**hyperparameters, 'max_steps': 10_00}
@property
def lowercase__ ( self ):
"""simple docstring"""
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def lowercase__ ( self ):
"""simple docstring"""
return f'''{self.framework}-transfromers-test'''
@property
def lowercase__ ( self ):
"""simple docstring"""
return f'''./tests/sagemaker/scripts/{self.framework}'''
@property
def lowercase__ ( self ):
"""simple docstring"""
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope='''class''' )
def a_ ( __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =SageMakerTestEnvironment(framework=request.cls.framework )
| 75 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
a_ : List[Any] = logging.get_logger(__name__)
a_ : Tuple = OrderedDict(
[
("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
("""beit""", """BeitFeatureExtractor"""),
("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
("""clap""", """ClapFeatureExtractor"""),
("""clip""", """CLIPFeatureExtractor"""),
("""clipseg""", """ViTFeatureExtractor"""),
("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
("""convnext""", """ConvNextFeatureExtractor"""),
("""cvt""", """ConvNextFeatureExtractor"""),
("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
("""data2vec-vision""", """BeitFeatureExtractor"""),
("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
("""deit""", """DeiTFeatureExtractor"""),
("""detr""", """DetrFeatureExtractor"""),
("""dinat""", """ViTFeatureExtractor"""),
("""donut-swin""", """DonutFeatureExtractor"""),
("""dpt""", """DPTFeatureExtractor"""),
("""encodec""", """EncodecFeatureExtractor"""),
("""flava""", """FlavaFeatureExtractor"""),
("""glpn""", """GLPNFeatureExtractor"""),
("""groupvit""", """CLIPFeatureExtractor"""),
("""hubert""", """Wav2Vec2FeatureExtractor"""),
("""imagegpt""", """ImageGPTFeatureExtractor"""),
("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
("""levit""", """LevitFeatureExtractor"""),
("""maskformer""", """MaskFormerFeatureExtractor"""),
("""mctct""", """MCTCTFeatureExtractor"""),
("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
("""mobilevit""", """MobileViTFeatureExtractor"""),
("""nat""", """ViTFeatureExtractor"""),
("""owlvit""", """OwlViTFeatureExtractor"""),
("""perceiver""", """PerceiverFeatureExtractor"""),
("""poolformer""", """PoolFormerFeatureExtractor"""),
("""regnet""", """ConvNextFeatureExtractor"""),
("""resnet""", """ConvNextFeatureExtractor"""),
("""segformer""", """SegformerFeatureExtractor"""),
("""sew""", """Wav2Vec2FeatureExtractor"""),
("""sew-d""", """Wav2Vec2FeatureExtractor"""),
("""speech_to_text""", """Speech2TextFeatureExtractor"""),
("""speecht5""", """SpeechT5FeatureExtractor"""),
("""swiftformer""", """ViTFeatureExtractor"""),
("""swin""", """ViTFeatureExtractor"""),
("""swinv2""", """ViTFeatureExtractor"""),
("""table-transformer""", """DetrFeatureExtractor"""),
("""timesformer""", """VideoMAEFeatureExtractor"""),
("""tvlt""", """TvltFeatureExtractor"""),
("""unispeech""", """Wav2Vec2FeatureExtractor"""),
("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
("""van""", """ConvNextFeatureExtractor"""),
("""videomae""", """VideoMAEFeatureExtractor"""),
("""vilt""", """ViltFeatureExtractor"""),
("""vit""", """ViTFeatureExtractor"""),
("""vit_mae""", """ViTFeatureExtractor"""),
("""vit_msn""", """ViTFeatureExtractor"""),
("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
("""wavlm""", """Wav2Vec2FeatureExtractor"""),
("""whisper""", """WhisperFeatureExtractor"""),
("""xclip""", """CLIPFeatureExtractor"""),
("""yolos""", """YolosFeatureExtractor"""),
]
)
a_ : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def a_ ( __snake_case : str ) -> Any:
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCamelCase_ =model_type_to_module_name(__snake_case )
lowerCamelCase_ =importlib.import_module(F'''.{module_name}''' , '''transformers.models''' )
try:
return getattr(__snake_case , __snake_case )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(__snake_case , '''__name__''' , __snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCamelCase_ =importlib.import_module('''transformers''' )
if hasattr(__snake_case , __snake_case ):
return getattr(__snake_case , __snake_case )
return None
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =get_file_from_repo(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(__snake_case , encoding='''utf-8''' ) as reader:
return json.load(__snake_case )
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(lowerCAmelCase )
def lowercase__ ( cls, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =kwargs.pop('''config''', lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''trust_remote_code''', lowerCAmelCase )
lowerCamelCase_ =True
lowerCamelCase_, lowerCamelCase_ =FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =config_dict.get('''feature_extractor_type''', lowerCAmelCase )
lowerCamelCase_ =None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''', {} ):
lowerCamelCase_ =config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =AutoConfig.from_pretrained(lowerCAmelCase, **lowerCAmelCase )
# It could be in `config.feature_extractor_type``
lowerCamelCase_ =getattr(lowerCAmelCase, '''feature_extractor_type''', lowerCAmelCase )
if hasattr(lowerCAmelCase, '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
lowerCamelCase_ =config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
lowerCamelCase_ =feature_extractor_class_from_name(lowerCAmelCase )
lowerCamelCase_ =feature_extractor_auto_map is not None
lowerCamelCase_ =feature_extractor_class is not None or type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
lowerCamelCase_ =resolve_trust_remote_code(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
if has_remote_code and trust_remote_code:
lowerCamelCase_ =get_class_from_dynamic_module(
lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''code_revision''', lowerCAmelCase )
if os.path.isdir(lowerCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
lowerCamelCase_ =FEATURE_EXTRACTOR_MAPPING[type(lowerCAmelCase )]
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
raise ValueError(
f'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
f'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase, lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __UpperCamelCase :
def __init__( self, lowerCAmelCase, lowerCAmelCase=3, lowerCAmelCase=32, lowerCAmelCase=3, lowerCAmelCase=10, lowerCAmelCase=[8, 16, 32, 64], lowerCAmelCase=[1, 1, 2, 1], lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase="relu", lowerCAmelCase=3, lowerCAmelCase=None, lowerCAmelCase=["stage2", "stage3", "stage4"], lowerCAmelCase=[2, 3, 4], lowerCAmelCase=1, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =image_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =embeddings_size
lowerCamelCase_ =hidden_sizes
lowerCamelCase_ =depths
lowerCamelCase_ =is_training
lowerCamelCase_ =use_labels
lowerCamelCase_ =hidden_act
lowerCamelCase_ =num_labels
lowerCamelCase_ =scope
lowerCamelCase_ =len(lowerCAmelCase )
lowerCamelCase_ =out_features
lowerCamelCase_ =out_indices
lowerCamelCase_ =num_groups
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ =None
if self.use_labels:
lowerCamelCase_ =ids_tensor([self.batch_size], self.num_labels )
lowerCamelCase_ =self.get_config()
return config, pixel_values, labels
def lowercase__ ( self ):
"""simple docstring"""
return BitConfig(
num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, out_features=self.out_features, out_indices=self.out_indices, num_groups=self.num_groups, )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =BitModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.num_labels
lowerCamelCase_ =BitForImageClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase, labels=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =BitBackbone(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ), len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ), [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ), len(config.out_features ) )
self.parent.assertListEqual(model.channels, config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowerCamelCase_ =None
lowerCamelCase_ =BitBackbone(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ), 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ), [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ), 1 )
self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =config_and_inputs
lowerCamelCase_ ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : List[str] =(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowercase : Tuple =(
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
lowercase : Dict =False
lowercase : Any =False
lowercase : Any =False
lowercase : List[Any] =False
lowercase : Dict =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BitModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, has_text_modality=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowercase__ ( self ):
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
lowerCamelCase_ =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ =[*signature.parameters.keys()]
lowerCamelCase_ =['''pixel_values''']
self.assertListEqual(arg_names[:1], lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(config=lowerCAmelCase )
for name, module in model.named_modules():
if isinstance(lowerCAmelCase, (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ), msg=f'''Parameter {name} of model {model_class} seems not properly initialized''', )
self.assertTrue(
torch.all(module.bias == 0 ), msg=f'''Parameter {name} of model {model_class} seems not properly initialized''', )
def lowercase__ ( self ):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
with torch.no_grad():
lowerCamelCase_ =model(**self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
lowerCamelCase_ =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase_ =self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase ), expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], )
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ =['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowerCamelCase_ =layer_type
lowerCamelCase_ =True
check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase_ =True
check_hidden_states_output(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =BitModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def a_ ( ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowerCAmelCase )
lowerCamelCase_ =self.default_image_processor
lowerCamelCase_ =prepare_img()
lowerCamelCase_ =image_processor(images=lowerCAmelCase, return_tensors='''pt''' ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
lowerCamelCase_ =model(**lowerCAmelCase )
# verify the logits
lowerCamelCase_ =torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape, lowerCAmelCase )
lowerCamelCase_ =torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]] ).to(lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3], lowerCAmelCase, atol=1e-4 ) )
@require_torch
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Tuple =(BitBackbone,) if is_torch_available() else ()
lowercase : Optional[int] =BitConfig
lowercase : Any =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BitModelTester(self )
| 75 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ : Optional[int] = logging.getLogger(__name__)
def a_ ( __snake_case : Union[str, Any] , __snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ =np.argmax(__snake_case , axis=1 )
return np.sum(outputs == labels )
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
with open(__snake_case , encoding='''utf_8''' ) as f:
lowerCamelCase_ =csv.reader(__snake_case )
lowerCamelCase_ =[]
next(__snake_case ) # skip the first line
for line in tqdm(__snake_case ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a_ ( __snake_case : str , __snake_case : Dict , __snake_case : List[str] , __snake_case : List[str] , __snake_case : List[Any] , __snake_case : Dict ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
for dataset in encoded_datasets:
lowerCamelCase_ =len(__snake_case )
lowerCamelCase_ =np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch, 2) , dtype=np.intaa )
lowerCamelCase_ =np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__snake_case ):
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =mc_label
lowerCamelCase_ =(input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__snake_case ) for t in all_inputs ) )
return tensor_datasets
def a_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=__snake_case , default='''openai-gpt''' , help='''pretrained model name''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' , action='''store_true''' , help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' , default=__snake_case , type=__snake_case , required=__snake_case , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument('''--train_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--eval_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--seed''' , type=__snake_case , default=42 )
parser.add_argument('''--num_train_epochs''' , type=__snake_case , default=3 )
parser.add_argument('''--train_batch_size''' , type=__snake_case , default=8 )
parser.add_argument('''--eval_batch_size''' , type=__snake_case , default=16 )
parser.add_argument('''--adam_epsilon''' , default=1e-8 , type=__snake_case , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' , type=__snake_case , default=1 )
parser.add_argument(
'''--max_steps''' , default=-1 , type=__snake_case , help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) , )
parser.add_argument(
'''--gradient_accumulation_steps''' , type=__snake_case , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--learning_rate''' , type=__snake_case , default=6.25e-5 )
parser.add_argument('''--warmup_steps''' , default=0 , type=__snake_case , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' , type=__snake_case , default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' , type=__snake_case , default=0.0_1 )
parser.add_argument('''--lm_coef''' , type=__snake_case , default=0.9 )
parser.add_argument('''--n_valid''' , type=__snake_case , default=374 )
parser.add_argument('''--server_ip''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
lowerCamelCase_ =parser.parse_args()
print(__snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowerCamelCase_ =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
lowerCamelCase_ =torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(__snake_case , __snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowerCamelCase_ =['''_start_''', '''_delimiter_''', '''_classify_''']
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(__snake_case )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(__snake_case )
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(__snake_case ) )
model.to(__snake_case )
# Load and encode the datasets
def tokenize_and_encode(__snake_case : Union[str, Any] ):
if isinstance(__snake_case , __snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__snake_case ) )
elif isinstance(__snake_case , __snake_case ):
return obj
return [tokenize_and_encode(__snake_case ) for o in obj]
logger.info('''Encoding dataset...''' )
lowerCamelCase_ =load_rocstories_dataset(args.train_dataset )
lowerCamelCase_ =load_rocstories_dataset(args.eval_dataset )
lowerCamelCase_ =(train_dataset, eval_dataset)
lowerCamelCase_ =tokenize_and_encode(__snake_case )
# Compute the max input length for the Transformer
lowerCamelCase_ =model.config.n_positions // 2 - 2
lowerCamelCase_ =max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowerCamelCase_ =min(__snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowerCamelCase_ =pre_process_datasets(__snake_case , __snake_case , __snake_case , *__snake_case )
lowerCamelCase_, lowerCamelCase_ =tensor_datasets[0], tensor_datasets[1]
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =RandomSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.train_batch_size )
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =SequentialSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowerCamelCase_ =args.max_steps
lowerCamelCase_ =args.max_steps // (len(__snake_case ) // args.gradient_accumulation_steps) + 1
else:
lowerCamelCase_ =len(__snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
lowerCamelCase_ =list(model.named_parameters() )
lowerCamelCase_ =['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
lowerCamelCase_ =[
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
lowerCamelCase_ =AdamW(__snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
lowerCamelCase_ =get_linear_schedule_with_warmup(
__snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=__snake_case )
if args.do_train:
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='''Epoch''' ):
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =tqdm(__snake_case , desc='''Training''' )
for step, batch in enumerate(__snake_case ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
lowerCamelCase_ =model(__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowerCamelCase_ =(
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowerCamelCase_ ='''Training loss: {:.2e} lr: {:.2e}'''.format(__snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowerCamelCase_ =model.module if hasattr(__snake_case , '''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
torch.save(model_to_save.state_dict() , __snake_case )
model_to_save.config.to_json_file(__snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(__snake_case )
if args.do_eval:
model.eval()
lowerCamelCase_, lowerCamelCase_ =0, 0
lowerCamelCase_, lowerCamelCase_ =0, 0
for batch in tqdm(__snake_case , desc='''Evaluating''' ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
with torch.no_grad():
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =model(
__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =mc_logits.detach().cpu().numpy()
lowerCamelCase_ =mc_labels.to('''cpu''' ).numpy()
lowerCamelCase_ =accuracy(__snake_case , __snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowerCamelCase_ =eval_loss / nb_eval_steps
lowerCamelCase_ =eval_accuracy / nb_eval_examples
lowerCamelCase_ =tr_loss / nb_tr_steps if args.do_train else None
lowerCamelCase_ ={'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
lowerCamelCase_ =os.path.join(args.output_dir , '''eval_results.txt''' )
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' , __snake_case , str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 75 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __UpperCamelCase :
def __init__( self, lowerCAmelCase, lowerCAmelCase=13, lowerCAmelCase=7, lowerCAmelCase=6, lowerCAmelCase=17, lowerCAmelCase=23, lowerCAmelCase=11, lowerCAmelCase=True, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =act_dim
lowerCamelCase_ =state_dim
lowerCamelCase_ =hidden_size
lowerCamelCase_ =max_length
lowerCamelCase_ =is_training
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowerCamelCase_ =floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowerCamelCase_ =floats_tensor((self.batch_size, self.seq_length, 1) )
lowerCamelCase_ =floats_tensor((self.batch_size, self.seq_length, 1) )
lowerCamelCase_ =ids_tensor((self.batch_size, self.seq_length), vocab_size=1_000 )
lowerCamelCase_ =random_attention_mask((self.batch_size, self.seq_length) )
lowerCamelCase_ =self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def lowercase__ ( self ):
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size, seq_length=self.seq_length, act_dim=self.act_dim, state_dim=self.state_dim, hidden_size=self.hidden_size, max_length=self.max_length, )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =DecisionTransformerModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
self.parent.assertEqual(result.state_preds.shape, states.shape )
self.parent.assertEqual(result.action_preds.shape, actions.shape )
self.parent.assertEqual(result.return_preds.shape, returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =config_and_inputs
lowerCamelCase_ ={
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : str =(DecisionTransformerModel,) if is_torch_available() else ()
lowercase : Optional[Any] =()
lowercase : List[str] ={'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
lowercase : Dict =False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
lowercase : Tuple =False
lowercase : Any =False
lowercase : Any =False
lowercase : Any =False
lowercase : List[str] =False
lowercase : Any =False
lowercase : Dict =False
lowercase : List[Any] =False
lowercase : Optional[int] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =DecisionTransformerModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, hidden_size=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =DecisionTransformerModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
lowerCamelCase_ =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ =[*signature.parameters.keys()]
lowerCamelCase_ =[
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(lowerCAmelCase )], lowerCAmelCase )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =2 # number of steps of autoregressive prediction we will perform
lowerCamelCase_ =10 # defined by the RL environment, may be normalized
lowerCamelCase_ =DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
lowerCamelCase_ =model.to(lowerCAmelCase )
lowerCamelCase_ =model.config
torch.manual_seed(0 )
lowerCamelCase_ =torch.randn(1, 1, config.state_dim ).to(device=lowerCAmelCase, dtype=torch.floataa ) # env.reset()
lowerCamelCase_ =torch.tensor(
[[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]], device=lowerCAmelCase )
lowerCamelCase_ =torch.tensor(lowerCAmelCase, device=lowerCAmelCase, dtype=torch.floataa ).reshape(1, 1, 1 )
lowerCamelCase_ =state
lowerCamelCase_ =torch.zeros(1, 0, config.act_dim, device=lowerCAmelCase, dtype=torch.floataa )
lowerCamelCase_ =torch.zeros(1, 0, device=lowerCAmelCase, dtype=torch.floataa )
lowerCamelCase_ =torch.tensor(0, device=lowerCAmelCase, dtype=torch.long ).reshape(1, 1 )
for step in range(lowerCAmelCase ):
lowerCamelCase_ =torch.cat([actions, torch.zeros(1, 1, config.act_dim, device=lowerCAmelCase )], dim=1 )
lowerCamelCase_ =torch.cat([rewards, torch.zeros(1, 1, device=lowerCAmelCase )], dim=1 )
lowerCamelCase_ =torch.ones(1, states.shape[1] ).to(dtype=torch.long, device=states.device )
with torch.no_grad():
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =model(
states=lowerCAmelCase, actions=lowerCAmelCase, rewards=lowerCAmelCase, returns_to_go=lowerCAmelCase, timesteps=lowerCAmelCase, attention_mask=lowerCAmelCase, return_dict=lowerCAmelCase, )
self.assertEqual(action_pred.shape, actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1], expected_outputs[step], atol=1e-4 ) )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =( # env.step(action)
torch.randn(1, 1, config.state_dim ).to(device=lowerCAmelCase, dtype=torch.floataa ),
1.0,
False,
{},
)
lowerCamelCase_ =action_pred[0, -1]
lowerCamelCase_ =torch.cat([states, state], dim=1 )
lowerCamelCase_ =returns_to_go[0, -1] - reward
lowerCamelCase_ =torch.cat([returns_to_go, pred_return.reshape(1, 1, 1 )], dim=1 )
lowerCamelCase_ =torch.cat(
[timesteps, torch.ones((1, 1), device=lowerCAmelCase, dtype=torch.long ) * (step + 1)], dim=1 )
| 75 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ =''''''
lowerCamelCase_ =[]
lowerCamelCase_ =0
lowerCamelCase_ =256
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =cva.imread(lowerCAmelCase, 0 )
lowerCamelCase_ =copy.deepcopy(self.img )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =plt.hist(self.img.ravel(), 256, [0, 256], label='''x''' )
lowerCamelCase_ =np.sum(lowerCAmelCase )
for i in range(len(lowerCAmelCase ) ):
lowerCamelCase_ =x[i] / self.k
self.sk += prk
lowerCamelCase_ =(self.L - 1) * self.sk
if self.rem != 0:
lowerCamelCase_ =int(last % last )
lowerCamelCase_ =int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowerCAmelCase )
lowerCamelCase_ =int(np.ma.count(self.img ) / self.img[1].size )
lowerCamelCase_ =self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCamelCase_ =self.img[j][i]
if num != self.last_list[num]:
lowerCamelCase_ =self.last_list[num]
cva.imwrite('''output_data/output.jpg''', self.img )
def lowercase__ ( self ):
"""simple docstring"""
plt.hist(self.img.ravel(), 256, [0, 256] )
def lowercase__ ( self ):
"""simple docstring"""
cva.imshow('''Output-Image''', self.img )
cva.imshow('''Input-Image''', self.original_image )
cva.waitKey(5_000 )
cva.destroyAllWindows()
if __name__ == "__main__":
a_ : str = os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
a_ : Optional[Any] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 75 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ : Union[str, Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Tuple =['pixel_values']
def __init__( self, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = True, lowerCAmelCase = 1 / 255, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = True, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =size if size is not None else {'''shortest_edge''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
lowerCamelCase_ =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase, param_name='''crop_size''' )
lowerCamelCase_ =do_resize
lowerCamelCase_ =size
lowerCamelCase_ =resample
lowerCamelCase_ =do_center_crop
lowerCamelCase_ =crop_size
lowerCamelCase_ =do_rescale
lowerCamelCase_ =rescale_factor
lowerCamelCase_ =do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCamelCase_ =image_std if image_std is not None else OPENAI_CLIP_STD
lowerCamelCase_ =do_convert_rgb
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowerCamelCase_ =get_resize_output_image_size(lowerCAmelCase, size=size['''shortest_edge'''], default_to_square=lowerCAmelCase )
return resize(lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowerCAmelCase, size=(size['''height'''], size['''width''']), data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return rescale(lowerCAmelCase, scale=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return normalize(lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = ChannelDimension.FIRST, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ =size if size is not None else self.size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =resample if resample is not None else self.resample
lowerCamelCase_ =do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase_ =crop_size if crop_size is not None else self.crop_size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''crop_size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ =rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ =do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ =image_std if image_std is not None else self.image_std
lowerCamelCase_ =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase_ =make_list_of_images(lowerCAmelCase )
if not valid_images(lowerCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase_ =[convert_to_rgb(lowerCAmelCase ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase_ =[to_numpy_array(lowerCAmelCase ) for image in images]
if do_resize:
lowerCamelCase_ =[self.resize(image=lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase ) for image in images]
if do_center_crop:
lowerCamelCase_ =[self.center_crop(image=lowerCAmelCase, size=lowerCAmelCase ) for image in images]
if do_rescale:
lowerCamelCase_ =[self.rescale(image=lowerCAmelCase, scale=lowerCAmelCase ) for image in images]
if do_normalize:
lowerCamelCase_ =[self.normalize(image=lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase ) for image in images]
lowerCamelCase_ =[to_channel_dimension_format(lowerCAmelCase, lowerCAmelCase ) for image in images]
lowerCamelCase_ ={'''pixel_values''': images}
return BatchFeature(data=lowerCAmelCase, tensor_type=lowerCAmelCase )
| 75 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Any = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
# No specific FOR_XXX available yet
def __call__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return super().__call__(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ={}
if "candidate_labels" in kwargs:
lowerCamelCase_ =kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
lowerCamelCase_ =kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(lowerCAmelCase, lowerCAmelCase ):
if audio.startswith('''http://''' ) or audio.startswith('''https://''' ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
lowerCamelCase_ =requests.get(lowerCAmelCase ).content
else:
with open(lowerCAmelCase, '''rb''' ) as f:
lowerCamelCase_ =f.read()
if isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =ffmpeg_read(lowerCAmelCase, self.feature_extractor.sampling_rate )
if not isinstance(lowerCAmelCase, np.ndarray ):
raise ValueError('''We expect a numpy ndarray as input''' )
if len(audio.shape ) != 1:
raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''' )
lowerCamelCase_ =self.feature_extractor(
[audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='''pt''' )
lowerCamelCase_ =candidate_labels
lowerCamelCase_ =[hypothesis_template.format(lowerCAmelCase ) for x in candidate_labels]
lowerCamelCase_ =self.tokenizer(lowerCAmelCase, return_tensors=self.framework, padding=lowerCAmelCase )
lowerCamelCase_ =[text_inputs]
return inputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_inputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0], lowerCAmelCase ):
lowerCamelCase_ =text_inputs[0]
else:
# Batching case.
lowerCamelCase_ =text_inputs[0][0]
lowerCamelCase_ =self.model(**lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ ={
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_audio,
}
return model_outputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_outputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_outputs['''logits'''][0]
if self.framework == "pt":
lowerCamelCase_ =logits.softmax(dim=0 )
lowerCamelCase_ =probs.tolist()
else:
raise ValueError('''`tf` framework not supported.''' )
lowerCamelCase_ =[
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(lowerCAmelCase, lowerCAmelCase ), key=lambda lowerCAmelCase : -x[0] )
]
return result
| 75 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Any =CanineTokenizer
lowercase : Tuple =False
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return CanineTokenizer.from_pretrained('''google/canine-s''' )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.tokenizer_class.from_pretrained(self.tmpdirname, **lowerCAmelCase )
lowerCamelCase_ =1_024
return tokenizer
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.canine_tokenizer
lowerCamelCase_ =['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
# fmt: off
lowerCamelCase_ =[57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
# fmt: on
lowerCamelCase_ =tokenizer(lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''pt''' )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =list(batch.input_ids.numpy()[0] )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertEqual((2, 39), batch.input_ids.shape )
self.assertEqual((2, 39), batch.attention_mask.shape )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.canine_tokenizer
lowerCamelCase_ =['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
lowerCamelCase_ =tokenizer(lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''pt''' )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn('''input_ids''', lowerCAmelCase )
self.assertIn('''attention_mask''', lowerCAmelCase )
self.assertIn('''token_type_ids''', lowerCAmelCase )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.canine_tokenizer
lowerCamelCase_ =[
'''What\'s the weater?''',
'''It\'s about 25 degrees.''',
]
lowerCamelCase_ =tokenizer(
text_target=lowerCAmelCase, max_length=32, padding='''max_length''', truncation=lowerCAmelCase, return_tensors='''pt''' )
self.assertEqual(32, targets['''input_ids'''].shape[1] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length, 42 )
# Now let's start the test
lowerCamelCase_ =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =''' He is very happy, UNwant\u00E9d,running'''
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
tokenizer.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =tokenizer.__class__.from_pretrained(lowerCAmelCase )
lowerCamelCase_ =after_tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
shutil.rmtree(lowerCAmelCase )
lowerCamelCase_ =self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =''' He is very happy, UNwant\u00E9d,running'''
lowerCamelCase_ =tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
lowerCamelCase_ =chr(0xE_007 )
additional_special_tokens.append(lowerCAmelCase )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
tokenizer.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =tokenizer.__class__.from_pretrained(lowerCAmelCase )
lowerCamelCase_ =after_tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertIn(lowerCAmelCase, after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length, 42 )
lowerCamelCase_ =tokenizer.__class__.from_pretrained(lowerCAmelCase, model_max_length=43 )
self.assertEqual(tokenizer.model_max_length, 43 )
shutil.rmtree(lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase_, lowerCamelCase_ =self.get_clean_sequence(lowerCAmelCase )
# a special token for Canine can be defined as follows:
lowerCamelCase_ =0xE_005
lowerCamelCase_ =chr(lowerCAmelCase )
tokenizer.add_special_tokens({'''cls_token''': special_token} )
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
self.assertEqual(len(lowerCAmelCase ), 1 )
lowerCamelCase_ =tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=lowerCAmelCase )
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, input_encoded + special_token_id )
lowerCamelCase_ =tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
self.assertTrue(special_token not in decoded )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase_ =chr(0xE_005 )
lowerCamelCase_ =chr(0xE_006 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1], special_tokens=lowerCAmelCase )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]} )
lowerCamelCase_ =tokenizer.tokenize(lowerCAmelCase )
lowerCamelCase_ =tokenizer.tokenize(lowerCAmelCase )
self.assertEqual(len(lowerCAmelCase ), 1 )
self.assertEqual(len(lowerCAmelCase ), 1 )
self.assertEqual(token_a[0], lowerCAmelCase )
self.assertEqual(token_a[0], lowerCAmelCase )
@require_tokenizers
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# a special token for Canine can be defined as follows:
lowerCamelCase_ =0xE_006
lowerCamelCase_ =chr(lowerCAmelCase )
lowerCamelCase_ =AddedToken(lowerCAmelCase, lstrip=lowerCAmelCase )
tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(lowerCAmelCase )
tokenizer.from_pretrained(lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =[]
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCAmelCase )
with open(os.path.join(lowerCAmelCase, '''special_tokens_map.json''' ), encoding='''utf-8''' ) as json_file:
lowerCamelCase_ =json.load(lowerCAmelCase )
with open(os.path.join(lowerCAmelCase, '''tokenizer_config.json''' ), encoding='''utf-8''' ) as json_file:
lowerCamelCase_ =json.load(lowerCAmelCase )
# a special token for Canine can be defined as follows:
lowerCamelCase_ =0xE_006
lowerCamelCase_ =chr(lowerCAmelCase )
lowerCamelCase_ =[new_token_a]
lowerCamelCase_ =[new_token_a]
with open(os.path.join(lowerCAmelCase, '''special_tokens_map.json''' ), '''w''', encoding='''utf-8''' ) as outfile:
json.dump(lowerCAmelCase, lowerCAmelCase )
with open(os.path.join(lowerCAmelCase, '''tokenizer_config.json''' ), '''w''', encoding='''utf-8''' ) as outfile:
json.dump(lowerCAmelCase, lowerCAmelCase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCamelCase_ =tokenizer_class.from_pretrained(lowerCAmelCase, extra_ids=0 )
self.assertIn(lowerCAmelCase, tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a], tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ), )
lowerCamelCase_ =0xE_007
lowerCamelCase_ =chr(lowerCAmelCase )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCamelCase_ =[AddedToken(lowerCAmelCase, lstrip=lowerCAmelCase )]
lowerCamelCase_ =tokenizer_class.from_pretrained(
lowerCAmelCase, additional_special_tokens=lowerCAmelCase, extra_ids=0 )
self.assertIn(lowerCAmelCase, tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a], tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase_ ='''hello world'''
if self.space_between_special_tokens:
lowerCamelCase_ ='''[CLS] hello world [SEP]'''
else:
lowerCamelCase_ =input
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(lowerCAmelCase, spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(lowerCAmelCase, [output, output.lower()] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase_ =[
'''bos_token''',
'''eos_token''',
'''unk_token''',
'''sep_token''',
'''pad_token''',
'''cls_token''',
'''mask_token''',
]
lowerCamelCase_ ='''a'''
lowerCamelCase_ =ord(lowerCAmelCase )
for attr in attributes_list:
setattr(lowerCAmelCase, attr + '''_id''', lowerCAmelCase )
self.assertEqual(getattr(lowerCAmelCase, lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(getattr(lowerCAmelCase, attr + '''_id''' ), lowerCAmelCase )
setattr(lowerCAmelCase, attr + '''_id''', lowerCAmelCase )
self.assertEqual(getattr(lowerCAmelCase, lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(getattr(lowerCAmelCase, attr + '''_id''' ), lowerCAmelCase )
setattr(lowerCAmelCase, '''additional_special_tokens_ids''', [] )
self.assertListEqual(getattr(lowerCAmelCase, '''additional_special_tokens''' ), [] )
self.assertListEqual(getattr(lowerCAmelCase, '''additional_special_tokens_ids''' ), [] )
lowerCamelCase_ =0xE_006
lowerCamelCase_ =chr(lowerCAmelCase )
setattr(lowerCAmelCase, '''additional_special_tokens_ids''', [additional_special_token_id] )
self.assertListEqual(getattr(lowerCAmelCase, '''additional_special_tokens''' ), [additional_special_token] )
self.assertListEqual(getattr(lowerCAmelCase, '''additional_special_tokens_ids''' ), [additional_special_token_id] )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
| 75 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
a_ : Optional[Any] = 5
a_ : str = 10
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : int =SpeechaTextTokenizer
lowercase : int =False
lowercase : List[str] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =sp.SentencePieceProcessor()
spm_model.Load(lowerCAmelCase )
lowerCamelCase_ =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowerCAmelCase ) )]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =Path(self.tmpdirname )
save_json(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''<pad>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<s>''' )
self.assertEqual(vocab_keys[1], '''<pad>''' )
self.assertEqual(vocab_keys[-1], '''j''' )
self.assertEqual(len(lowerCAmelCase ), 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [289, 50, 14, 174, 386], )
lowerCamelCase_ =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.'''], )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ =tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.'''], )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase, model_name='''facebook/s2t-small-mustc-en-de-st''', revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''', )
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Tuple ='valhalla/s2t_mustc_multilinguial_medium'
lowercase : Dict ='C\'est trop cool'
lowercase : str ='Esto es genial'
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''], 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''], 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''], 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''], 11 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 10_000 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertIn(lowerCAmelCase, self.tokenizer.all_special_ids )
lowerCamelCase_ =[ES_CODE, 4, 1_601, 47, 7_647, 2]
lowerCamelCase_ =self.tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
lowerCamelCase_ =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0], lowerCAmelCase )
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE] )
lowerCamelCase_ ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE] )
| 75 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ : Optional[Any] = R"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.
Args:
title_sep (`str`, *optional*, defaults to `\" / \"`):
Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
doc_sep (`str`, *optional*, defaults to `\" // \"`):
Separator inserted between the text of the retrieved document and the original input when calling
[`RagRetriever`].
n_docs (`int`, *optional*, defaults to 5):
Number of documents to retrieve.
max_combined_length (`int`, *optional*, defaults to 300):
Max length of contextualized input returned by [`~RagRetriever.__call__`].
retrieval_vector_size (`int`, *optional*, defaults to 768):
Dimensionality of the document embeddings indexed by [`RagRetriever`].
retrieval_batch_size (`int`, *optional*, defaults to 8):
Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
[`RagRetriever`].
dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
using `datasets.list_datasets()`).
dataset_split (`str`, *optional*, defaults to `\"train\"`)
Which split of the `dataset` to load.
index_name (`str`, *optional*, defaults to `\"compressed\"`)
The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and
`\"compressed\"`.
index_path (`str`, *optional*)
The path to the serialized faiss index on disk.
passages_path (`str`, *optional*):
A path to text passages compatible with the faiss index. Required if using
[`~models.rag.retrieval_rag.LegacyIndex`]
use_dummy_dataset (`bool`, *optional*, defaults to `False`)
Whether to load a \"dummy\" variant of the dataset specified by `dataset`.
label_smoothing (`float`, *optional*, defaults to 0.0):
Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
in the loss calculation. If set to 0, no label smoothing is performed.
do_marginalize (`bool`, *optional*, defaults to `False`):
If `True`, the logits are marginalized over all documents by making use of
`torch.nn.functional.log_softmax`.
reduce_loss (`bool`, *optional*, defaults to `False`):
Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
do_deduplication (`bool`, *optional*, defaults to `True`):
Whether or not to deduplicate the generations from different context documents for a given input. Has to be
set to `False` if used while training with distributed backend.
exclude_bos_score (`bool`, *optional*, defaults to `False`):
Whether or not to disregard the BOS token when computing the loss.
output_retrieved(`bool`, *optional*, defaults to `False`):
If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
`context_attention_mask` are returned. See returned tensors for more detail.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
"""
@add_start_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Dict ='rag'
lowercase : Dict =True
def __init__( self, lowerCAmelCase=None, lowerCAmelCase=True, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=" / ", lowerCAmelCase=" // ", lowerCAmelCase=5, lowerCAmelCase=300, lowerCAmelCase=768, lowerCAmelCase=8, lowerCAmelCase="wiki_dpr", lowerCAmelCase="train", lowerCAmelCase="compressed", lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=False, lowerCAmelCase=False, lowerCAmelCase=0.0, lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase=False, lowerCAmelCase=False, lowerCAmelCase=True, lowerCAmelCase=None, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(
bos_token_id=lowerCAmelCase, pad_token_id=lowerCAmelCase, eos_token_id=lowerCAmelCase, decoder_start_token_id=lowerCAmelCase, forced_eos_token_id=lowerCAmelCase, is_encoder_decoder=lowerCAmelCase, prefix=lowerCAmelCase, vocab_size=lowerCAmelCase, **lowerCAmelCase, )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
lowerCamelCase_ =kwargs.pop('''question_encoder''' )
lowerCamelCase_ =question_encoder_config.pop('''model_type''' )
lowerCamelCase_ =kwargs.pop('''generator''' )
lowerCamelCase_ =decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
lowerCamelCase_ =AutoConfig.for_model(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =AutoConfig.for_model(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =reduce_loss
lowerCamelCase_ =label_smoothing
lowerCamelCase_ =exclude_bos_score
lowerCamelCase_ =do_marginalize
lowerCamelCase_ =title_sep
lowerCamelCase_ =doc_sep
lowerCamelCase_ =n_docs
lowerCamelCase_ =max_combined_length
lowerCamelCase_ =dataset
lowerCamelCase_ =dataset_split
lowerCamelCase_ =index_name
lowerCamelCase_ =retrieval_vector_size
lowerCamelCase_ =retrieval_batch_size
lowerCamelCase_ =passages_path
lowerCamelCase_ =index_path
lowerCamelCase_ =use_dummy_dataset
lowerCamelCase_ =output_retrieved
lowerCamelCase_ =do_deduplication
lowerCamelCase_ =use_cache
if self.forced_eos_token_id is None:
lowerCamelCase_ =getattr(self.generator, '''forced_eos_token_id''', lowerCAmelCase )
@classmethod
def lowercase__ ( cls, lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return cls(question_encoder=question_encoder_config.to_dict(), generator=generator_config.to_dict(), **lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =copy.deepcopy(self.__dict__ )
lowerCamelCase_ =self.question_encoder.to_dict()
lowerCamelCase_ =self.generator.to_dict()
lowerCamelCase_ =self.__class__.model_type
return output
| 75 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def a_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase_ ='''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
lowerCamelCase_ =Image.open(requests.get(__snake_case , stream=__snake_case ).raw ).convert('''RGB''' )
return image
def a_ ( __snake_case : Tuple ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''') )
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''') )
# fmt: on
return rename_keys
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =dct.pop(__snake_case )
lowerCamelCase_ =val
def a_ ( __snake_case : str , __snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
lowerCamelCase_ =torch.cat((q_bias, torch.zeros_like(__snake_case , requires_grad=__snake_case ), v_bias) )
lowerCamelCase_ =qkv_bias
def a_ ( __snake_case : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ =364 if '''coco''' in model_name else 224
lowerCamelCase_ =InstructBlipVisionConfig(image_size=__snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xxl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf''' , vocab_size=3_2001 ).to_dict()
elif "vicuna-13b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf''' , vocab_size=3_2001 ).to_dict()
else:
raise ValueError('''Model name not supported''' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
lowerCamelCase_ =InstructBlipQFormerConfig(vocab_size=3_0523 ).to_dict()
lowerCamelCase_ =InstructBlipConfig(vision_config=__snake_case , text_config=__snake_case , qformer_config=__snake_case )
return config, image_size
@torch.no_grad()
def a_ ( __snake_case : Optional[int] , __snake_case : str=None , __snake_case : List[Any]=False ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =AutoTokenizer.from_pretrained('''bert-base-uncased''' , truncation_side='''left''' )
qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''} )
if "t5" in model_name:
lowerCamelCase_ =TaTokenizerFast.from_pretrained('''google/flan-t5-xl''' , truncation_side='''left''' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
lowerCamelCase_ =LlamaTokenizerFast.from_pretrained(
'''huggyllama/llama-7b''' , truncation_side='''left''' , bos_token='''</s>''' , unk_token='''</s>''' )
tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''} )
lowerCamelCase_, lowerCamelCase_ =get_blipa_config(__snake_case )
lowerCamelCase_ =InstructBlipForConditionalGeneration(__snake_case ).eval()
lowerCamelCase_ ={
'''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
'''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
'''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
'''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
}
lowerCamelCase_, lowerCamelCase_ =model_name_to_original[model_name]
# load original model
print('''Loading original model...''' )
lowerCamelCase_ ='''cuda:1''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_ ='''cuda:2''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =load_model_and_preprocess(
name=__snake_case , model_type=__snake_case , is_eval=__snake_case , device=__snake_case )
original_model.eval()
print('''Done!''' )
# update state dict keys
lowerCamelCase_ =original_model.state_dict()
lowerCamelCase_ =create_rename_keys(__snake_case )
for src, dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
lowerCamelCase_ =state_dict.pop(__snake_case )
if key.startswith('''Qformer.bert''' ):
lowerCamelCase_ =key.replace('''Qformer.bert''' , '''qformer''' )
if "attention.self" in key:
lowerCamelCase_ =key.replace('''self''' , '''attention''' )
if "llm_proj" in key:
lowerCamelCase_ =key.replace('''llm_proj''' , '''language_projection''' )
if "t5_proj" in key:
lowerCamelCase_ =key.replace('''t5_proj''' , '''language_projection''' )
if key.startswith('''llm_model''' ):
lowerCamelCase_ =key.replace('''llm_model''' , '''language_model''' )
if key.startswith('''t5''' ):
lowerCamelCase_ =key.replace('''t5''' , '''language''' )
lowerCamelCase_ =val
# read in qv biases
read_in_q_v_bias(__snake_case , __snake_case )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(__snake_case , strict=__snake_case )
lowerCamelCase_ =load_demo_image()
lowerCamelCase_ ='''What is unusual about this image?'''
# create processor
lowerCamelCase_ =BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size} , image_mean=__snake_case , image_std=__snake_case )
lowerCamelCase_ =InstructBlipProcessor(
image_processor=__snake_case , tokenizer=__snake_case , qformer_tokenizer=__snake_case , )
lowerCamelCase_ =processor(images=__snake_case , text=__snake_case , return_tensors='''pt''' ).to(__snake_case )
# make sure processor creates exact same pixel values
lowerCamelCase_ =vis_processors['''eval'''](__snake_case ).unsqueeze(0 ).to(__snake_case )
lowerCamelCase_ =inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , __snake_case )
original_model.to(__snake_case )
hf_model.to(__snake_case )
with torch.no_grad():
if "vicuna" in model_name:
lowerCamelCase_ =original_model({'''image''': original_pixel_values, '''text_input''': [prompt]} ).logits
lowerCamelCase_ =hf_model(**__snake_case ).logits
else:
lowerCamelCase_ =original_model(
{'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']} ).logits
lowerCamelCase_ =tokenizer('''\n''' , return_tensors='''pt''' ).input_ids.to(__snake_case )
lowerCamelCase_ =label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
lowerCamelCase_ =hf_model(**__snake_case , labels=__snake_case ).logits
print('''First values of original logits:''' , original_logits[0, :3, :3] )
print('''First values of HF logits:''' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
lowerCamelCase_ =1e-4 if '''vicuna''' in model_name else 1e-5
assert torch.allclose(original_logits.to(logits.device ) , __snake_case , atol=__snake_case )
print('''Looks ok!''' )
print('''Generating with original model...''' )
lowerCamelCase_ =original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('''Generating with HF model...''' )
lowerCamelCase_ =hf_model.generate(
**__snake_case , do_sample=__snake_case , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
lowerCamelCase_ =2
print('''Original generation:''' , __snake_case )
lowerCamelCase_ =processor.batch_decode(__snake_case , skip_special_tokens=__snake_case )
lowerCamelCase_ =[text.strip() for text in output_text]
print('''HF generation:''' , __snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__snake_case )
hf_model.save_pretrained(__snake_case )
if push_to_hub:
processor.push_to_hub(F'''Salesforce/{model_name}''' )
hf_model.push_to_hub(F'''Salesforce/{model_name}''' )
if __name__ == "__main__":
a_ : Any = argparse.ArgumentParser()
a_ : Any = [
"""instructblip-vicuna-7b""",
"""instructblip-vicuna-13b""",
"""instructblip-flan-t5-xl""",
"""instructblip-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""instructblip-flan-t5-xl""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
a_ : str = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 75 | 1 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Optional[Any]=None ) -> List[str]:
"""simple docstring"""
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
lowerCamelCase_ =nn.Parameter(__snake_case )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
lowerCamelCase_ =nn.Parameter(__snake_case )
def a_ ( __snake_case : List[str] , __snake_case : List[str] , __snake_case : Any ) -> List[Any]:
"""simple docstring"""
# set torch weights for 1-to-1 comparison
lowerCamelCase_ =np.asarray(weights[0] )
lowerCamelCase_ =np.asarray(weights[1] )
lowerCamelCase_ =np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.output.dense , torch.tensor(__snake_case ).view(-1 , __snake_case ).contiguous().transpose(0 , 1 ) , )
def a_ ( __snake_case : Optional[int] , __snake_case : Tuple , __snake_case : Optional[Any] ) -> Dict:
"""simple docstring"""
# set torch weights for 1-to-1 comparison
lowerCamelCase_ =np.asarray(weights[0] )
lowerCamelCase_ =np.asarray(weights[1] )
lowerCamelCase_ =np.asarray(weights[2] )
lowerCamelCase_ =np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.output.dense , torch.tensor(__snake_case ).view(-1 , __snake_case ).contiguous().transpose(0 , 1 ) , )
def a_ ( __snake_case : Tuple , __snake_case : List[Any] , __snake_case : List[str] ) -> List[str]:
"""simple docstring"""
# layernorm 1
lowerCamelCase_ =weights[0][0][0]
lowerCamelCase_ =np.asarray(layer_norm_a[0] )
lowerCamelCase_ =np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(__snake_case ) , torch.tensor(__snake_case ) , )
# lsh weights + output
lowerCamelCase_ =weights[0][1]
if len(__snake_case ) < 4:
set_layer_weights_in_torch_lsh(__snake_case , torch_block.attention , __snake_case )
else:
set_layer_weights_in_torch_local(__snake_case , torch_block.attention , __snake_case )
# intermediate weighs
lowerCamelCase_ =weights[2][0][1][2]
# Chunked Feed Forward
if len(__snake_case ) == 4:
lowerCamelCase_ =intermediate_weights[2]
# layernorm 2
lowerCamelCase_ =np.asarray(intermediate_weights[0][0] )
lowerCamelCase_ =np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(__snake_case ) , torch.tensor(__snake_case ) , )
# intermediate dense
lowerCamelCase_ =np.asarray(intermediate_weights[1][0] )
lowerCamelCase_ =np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(__snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(__snake_case ) , )
# intermediate out
lowerCamelCase_ =np.asarray(intermediate_weights[4][0] )
lowerCamelCase_ =np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(__snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(__snake_case ) , )
def a_ ( __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
# reformer model
lowerCamelCase_ =torch_model.reformer
# word embeds
lowerCamelCase_ =np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(__snake_case ) , )
if isinstance(weights[3] , __snake_case ):
lowerCamelCase_ =torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowerCamelCase_ =np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
lowerCamelCase_ =nn.Parameter(torch.tensor(__snake_case ) )
lowerCamelCase_ =weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
__snake_case ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowerCamelCase_ =trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(__snake_case , __snake_case , __snake_case )
# output layer norm
lowerCamelCase_ =np.asarray(weights[7][0] )
lowerCamelCase_ =np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(__snake_case ) , torch.tensor(__snake_case ) , )
# output embeddings
lowerCamelCase_ =np.asarray(weights[9][0] )
lowerCamelCase_ =np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(__snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(__snake_case ) , )
def a_ ( __snake_case : str , __snake_case : Tuple , __snake_case : Dict ) -> Dict:
"""simple docstring"""
# Initialise PyTorch model
lowerCamelCase_ =ReformerConfig.from_json_file(__snake_case )
print(F'''Building PyTorch model from configuration: {config}''' )
lowerCamelCase_ =ReformerModelWithLMHead(__snake_case )
with open(__snake_case , '''rb''' ) as f:
lowerCamelCase_ =pickle.load(__snake_case )['''weights''']
set_model_weights_in_torch(__snake_case , __snake_case , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , __snake_case )
if __name__ == "__main__":
a_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained Reformer model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
a_ : Union[str, Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 75 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return 0.0
def a_ ( __snake_case : np.ndarray , __snake_case : int ) -> tuple[int | float, int | float]:
"""simple docstring"""
lowerCamelCase_ =min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
lowerCamelCase_ =max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.abs(np.fft.fft(__snake_case ) )
lowerCamelCase_ =20 * np.logaa(__snake_case )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
lowerCamelCase_ =get_bounds(__snake_case , __snake_case )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(__snake_case )
plt.show()
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.angle(np.fft.fft(__snake_case ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(__snake_case , -2 * pi ) )
plt.show()
| 75 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
a_ : Dict = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Union[str, Any] = ["""DPTFeatureExtractor"""]
a_ : int = ["""DPTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Union[str, Any] = [
"""DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DPTForDepthEstimation""",
"""DPTForSemanticSegmentation""",
"""DPTModel""",
"""DPTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
a_ : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 75 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =FunnelTokenizer
lowercase : List[str] =FunnelTokenizerFast
lowercase : Union[str, Any] =True
lowercase : int =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =[
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return FunnelTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ='''UNwant\u00E9d,running'''
lowerCamelCase_ ='''unwanted, running'''
return input_text, output_text
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.tokenizer_class(self.vocab_file )
lowerCamelCase_ =tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(lowerCAmelCase, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [7, 4, 5, 10, 8, 9] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
lowerCamelCase_ =tokenizer('''UNwant\u00E9d,running''' )
lowerCamelCase_ =len(inputs['''input_ids'''] ) - 1
self.assertListEqual(inputs['''token_type_ids'''], [2] + [0] * sentence_len )
lowerCamelCase_ =tokenizer('''UNwant\u00E9d,running''', '''UNwant\u00E9d,running''' )
self.assertListEqual(inputs['''token_type_ids'''], [2] + [0] * sentence_len + [1] * sentence_len )
| 75 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : Tuple =StableDiffusionDiffEditPipeline
lowercase : List[str] =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
lowercase : Optional[int] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
lowercase : Tuple =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase : Union[str, Any] =frozenset([] )
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=32, attention_head_dim=(2, 4), use_linear_projection=lowerCAmelCase, )
lowerCamelCase_ =DDIMScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='''scaled_linear''', clip_sample=lowerCAmelCase, set_alpha_to_one=lowerCAmelCase, )
lowerCamelCase_ =DDIMInverseScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='''scaled_linear''', clip_sample=lowerCAmelCase, set_alpha_to_zero=lowerCAmelCase, )
torch.manual_seed(0 )
lowerCamelCase_ =AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, sample_size=128, )
torch.manual_seed(0 )
lowerCamelCase_ =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, hidden_act='''gelu''', projection_dim=512, )
lowerCamelCase_ =CLIPTextModel(lowerCAmelCase )
lowerCamelCase_ =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCamelCase_ ={
'''unet''': unet,
'''scheduler''': scheduler,
'''inverse_scheduler''': inverse_scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 16, 16), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowerCamelCase_ =floats_tensor((1, 2, 4, 16, 16), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''prompt''': '''a dog and a newt''',
'''mask_image''': mask,
'''image_latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowerCamelCase_ =image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase_ =Image.fromarray(np.uinta(lowerCAmelCase ) ).convert('''RGB''' )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''image''': image,
'''source_prompt''': '''a cat and a frog''',
'''target_prompt''': '''a dog and a newt''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''num_maps_per_mask''': 2,
'''mask_encode_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowerCamelCase_ =image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase_ =Image.fromarray(np.uinta(lowerCAmelCase ) ).convert('''RGB''' )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''image''': image,
'''prompt''': '''a cat and a frog''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''decode_latents''': True,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
if not hasattr(self.pipeline_class, '''_optional_components''' ):
return
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =pipe(**lowerCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =self.pipeline_class.from_pretrained(lowerCAmelCase )
pipe_loaded.to(lowerCAmelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCAmelCase, lowerCAmelCase ) is None, f'''`{optional_component}` did not stay set to None after loading.''', )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =pipe_loaded(**lowerCAmelCase )[0]
lowerCamelCase_ =np.abs(output - output_loaded ).max()
self.assertLess(lowerCAmelCase, 1e-4 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_mask_inputs(lowerCAmelCase )
lowerCamelCase_ =pipe.generate_mask(**lowerCAmelCase )
lowerCamelCase_ =mask[0, -3:, -3:]
self.assertEqual(mask.shape, (1, 16, 16) )
lowerCamelCase_ =np.array([0] * 9 )
lowerCamelCase_ =np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase, 1e-3 )
self.assertEqual(mask[0, -3, -4], 0 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inversion_inputs(lowerCAmelCase )
lowerCamelCase_ =pipe.invert(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -1, -3:, -3:]
self.assertEqual(image.shape, (2, 32, 32, 3) )
lowerCamelCase_ =np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9], )
lowerCamelCase_ =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase, 1e-3 )
def lowercase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ ={'''beta_start''': 0.0_0_0_8_5, '''beta_end''': 0.0_1_2, '''beta_schedule''': '''scaled_linear'''}
lowerCamelCase_ =DPMSolverMultistepScheduler(**lowerCAmelCase )
lowerCamelCase_ =DPMSolverMultistepInverseScheduler(**lowerCAmelCase )
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inversion_inputs(lowerCAmelCase )
lowerCamelCase_ =pipe.invert(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -1, -3:, -3:]
self.assertEqual(image.shape, (2, 32, 32, 3) )
lowerCamelCase_ =np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9], )
lowerCamelCase_ =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCAmelCase, 1e-3 )
@require_torch_gpu
@slow
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''' )
lowerCamelCase_ =raw_image.convert('''RGB''' ).resize((768, 768) )
lowerCamelCase_ =raw_image
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =torch.manual_seed(0 )
lowerCamelCase_ =StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =DDIMScheduler.from_config(pipe.scheduler.config )
lowerCamelCase_ =DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ ='''a bowl of fruit'''
lowerCamelCase_ ='''a bowl of pears'''
lowerCamelCase_ =pipe.generate_mask(
image=self.raw_image, source_prompt=lowerCAmelCase, target_prompt=lowerCAmelCase, generator=lowerCAmelCase, )
lowerCamelCase_ =pipe.invert(
prompt=lowerCAmelCase, image=self.raw_image, inpaint_strength=0.7, generator=lowerCAmelCase ).latents
lowerCamelCase_ =pipe(
prompt=lowerCAmelCase, mask_image=lowerCAmelCase, image_latents=lowerCAmelCase, generator=lowerCAmelCase, negative_prompt=lowerCAmelCase, inpaint_strength=0.7, output_type='''numpy''', ).images[0]
lowerCamelCase_ =(
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =torch.manual_seed(0 )
lowerCamelCase_ =StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowerCamelCase_ =DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ ='''a bowl of fruit'''
lowerCamelCase_ ='''a bowl of pears'''
lowerCamelCase_ =pipe.generate_mask(
image=self.raw_image, source_prompt=lowerCAmelCase, target_prompt=lowerCAmelCase, generator=lowerCAmelCase, )
lowerCamelCase_ =pipe.invert(
prompt=lowerCAmelCase, image=self.raw_image, inpaint_strength=0.7, generator=lowerCAmelCase, num_inference_steps=25, ).latents
lowerCamelCase_ =pipe(
prompt=lowerCAmelCase, mask_image=lowerCAmelCase, image_latents=lowerCAmelCase, generator=lowerCAmelCase, negative_prompt=lowerCAmelCase, inpaint_strength=0.7, num_inference_steps=25, output_type='''numpy''', ).images[0]
lowerCamelCase_ =(
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 75 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def a_ ( __snake_case : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ =checkpoints.load_tax_checkpoint(__snake_case )
lowerCamelCase_ =flatten_dict(__snake_case )
return flax_params
def a_ ( __snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ ={}
lowerCamelCase_ ={
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCamelCase_ ={
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCamelCase_ ='''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =flax_dict[key]
lowerCamelCase_ ={}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCamelCase_ =torch.from_numpy(converted_dict[key].T )
else:
lowerCamelCase_ =torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Any=False , __snake_case : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =get_flax_param(__snake_case )
if not use_large:
lowerCamelCase_ =PixaStructVisionConfig()
lowerCamelCase_ =PixaStructTextConfig()
else:
lowerCamelCase_ =PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowerCamelCase_ =PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowerCamelCase_ =PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__snake_case )
lowerCamelCase_ =PixaStructForConditionalGeneration(__snake_case )
lowerCamelCase_ =rename_and_convert_flax_params(__snake_case )
model.load_state_dict(__snake_case )
lowerCamelCase_ =AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCamelCase_ =PixaStructImageProcessor()
lowerCamelCase_ =PixaStructProcessor(image_processor=__snake_case , tokenizer=__snake_case )
if use_large:
lowerCamelCase_ =4096
lowerCamelCase_ =True
# mkdir if needed
os.makedirs(__snake_case , exist_ok=__snake_case )
model.save_pretrained(__snake_case )
processor.save_pretrained(__snake_case )
print('''Model saved in {}'''.format(__snake_case ) )
if __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
a_ : Tuple = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 75 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class __UpperCamelCase :
def __init__( self, lowerCAmelCase, lowerCAmelCase=13, lowerCAmelCase=7, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase=True, lowerCAmelCase=99, lowerCAmelCase=32, lowerCAmelCase=5, lowerCAmelCase=4, lowerCAmelCase=37, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=512, lowerCAmelCase=16, lowerCAmelCase=2, lowerCAmelCase=0.0_2, lowerCAmelCase=3, lowerCAmelCase=4, lowerCAmelCase=None, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =is_training
lowerCamelCase_ =use_input_mask
lowerCamelCase_ =use_token_type_ids
lowerCamelCase_ =use_labels
lowerCamelCase_ =vocab_size
lowerCamelCase_ =hidden_size
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =intermediate_size
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =attention_probs_dropout_prob
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =type_vocab_size
lowerCamelCase_ =type_sequence_label_size
lowerCamelCase_ =initializer_range
lowerCamelCase_ =num_labels
lowerCamelCase_ =num_choices
lowerCamelCase_ =scope
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase_ =None
if self.use_input_mask:
lowerCamelCase_ =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ =None
if self.use_token_type_ids:
lowerCamelCase_ =ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase_ =None
lowerCamelCase_ =None
lowerCamelCase_ =None
if self.use_labels:
lowerCamelCase_ =ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase_ =ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase_ =ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase_ =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase__ ( self ):
"""simple docstring"""
return LlamaConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=lowerCAmelCase, initializer_range=self.initializer_range, )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =LlamaModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase )
lowerCamelCase_ =model(lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =True
lowerCamelCase_ =LlamaModel(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(
lowerCAmelCase, attention_mask=lowerCAmelCase, encoder_hidden_states=lowerCAmelCase, encoder_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =model(
lowerCAmelCase, attention_mask=lowerCAmelCase, encoder_hidden_states=lowerCAmelCase, )
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =LlamaForCausalLM(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase, labels=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =True
lowerCamelCase_ =True
lowerCamelCase_ =LlamaForCausalLM(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
# first forward pass
lowerCamelCase_ =model(
lowerCAmelCase, attention_mask=lowerCAmelCase, encoder_hidden_states=lowerCAmelCase, encoder_attention_mask=lowerCAmelCase, use_cache=lowerCAmelCase, )
lowerCamelCase_ =outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase_ =ids_tensor((self.batch_size, 3), config.vocab_size )
lowerCamelCase_ =ids_tensor((self.batch_size, 3), vocab_size=2 )
# append to next input_ids and
lowerCamelCase_ =torch.cat([input_ids, next_tokens], dim=-1 )
lowerCamelCase_ =torch.cat([input_mask, next_mask], dim=-1 )
lowerCamelCase_ =model(
lowerCAmelCase, attention_mask=lowerCAmelCase, encoder_hidden_states=lowerCAmelCase, encoder_attention_mask=lowerCAmelCase, output_hidden_states=lowerCAmelCase, )['''hidden_states'''][0]
lowerCamelCase_ =model(
lowerCAmelCase, attention_mask=lowerCAmelCase, encoder_hidden_states=lowerCAmelCase, encoder_attention_mask=lowerCAmelCase, past_key_values=lowerCAmelCase, output_hidden_states=lowerCAmelCase, )['''hidden_states'''][0]
# select random slice
lowerCamelCase_ =ids_tensor((1,), output_from_past.shape[-1] ).item()
lowerCamelCase_ =output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase_ =output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =config_and_inputs
lowerCamelCase_ ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : str =(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
lowercase : Optional[Any] =(LlamaForCausalLM,) if is_torch_available() else ()
lowercase : int =(
{
'feature-extraction': LlamaModel,
'text-classification': LlamaForSequenceClassification,
'text-generation': LlamaForCausalLM,
'zero-shot': LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : str =False
lowercase : List[Any] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =LlamaModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, hidden_size=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCamelCase_ =type
self.model_tester.create_and_check_model(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ =3
lowerCamelCase_ =input_dict['''input_ids''']
lowerCamelCase_ =input_ids.ne(1 ).to(lowerCAmelCase )
lowerCamelCase_ =ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size )
lowerCamelCase_ =LlamaForSequenceClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase, labels=lowerCAmelCase )
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ =3
lowerCamelCase_ ='''single_label_classification'''
lowerCamelCase_ =input_dict['''input_ids''']
lowerCamelCase_ =input_ids.ne(1 ).to(lowerCAmelCase )
lowerCamelCase_ =ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size )
lowerCamelCase_ =LlamaForSequenceClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase, labels=lowerCAmelCase )
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ =3
lowerCamelCase_ ='''multi_label_classification'''
lowerCamelCase_ =input_dict['''input_ids''']
lowerCamelCase_ =input_ids.ne(1 ).to(lowerCAmelCase )
lowerCamelCase_ =ids_tensor(
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCamelCase_ =LlamaForSequenceClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase, labels=lowerCAmelCase )
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''LLaMA buffers include complex numbers, which breaks this test''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ =ids_tensor([1, 10], config.vocab_size )
lowerCamelCase_ =ids_tensor([1, int(config.max_position_embeddings * 1.5 )], config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase_ =LlamaModel(lowerCAmelCase )
original_model.to(lowerCAmelCase )
original_model.eval()
lowerCamelCase_ =original_model(lowerCAmelCase ).last_hidden_state
lowerCamelCase_ =original_model(lowerCAmelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase_ ={'''type''': scaling_type, '''factor''': 1_0.0}
lowerCamelCase_ =LlamaModel(lowerCAmelCase )
scaled_model.to(lowerCAmelCase )
scaled_model.eval()
lowerCamelCase_ =scaled_model(lowerCAmelCase ).last_hidden_state
lowerCamelCase_ =scaled_model(lowerCAmelCase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-5 ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowerCamelCase_ =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''', device_map='''auto''' )
lowerCamelCase_ =model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowerCamelCase_ =torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]] )
torch.testing.assert_close(out.mean(-1 ), lowerCAmelCase, atol=1e-2, rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowerCamelCase_ =torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30], lowerCAmelCase, atol=1e-5, rtol=1e-5 )
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowerCamelCase_ =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''', device_map='''auto''' )
lowerCamelCase_ =model(torch.tensor(lowerCAmelCase ) )
# Expected mean on dim = -1
lowerCamelCase_ =torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]] )
torch.testing.assert_close(out.mean(-1 ), lowerCAmelCase, atol=1e-2, rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowerCamelCase_ =torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30], lowerCAmelCase, atol=1e-5, rtol=1e-5 )
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowerCamelCase_ =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''', device_map='''auto''' )
lowerCamelCase_ =model(torch.tensor(lowerCAmelCase ) )
# Expected mean on dim = -1
lowerCamelCase_ =torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]] )
torch.testing.assert_close(out.mean(-1 ), lowerCAmelCase, atol=1e-2, rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowerCamelCase_ =torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ), lowerCAmelCase, atol=1e-2, rtol=1e-2 )
@unittest.skip(
'''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test''' )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
lowerCamelCase_ =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''', device_map='''auto''' )
lowerCamelCase_ =model(torch.tensor(lowerCAmelCase ) )
lowerCamelCase_ =torch.tensor(
[[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]], dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ), lowerCAmelCase, atol=1e-2, rtol=1e-2 )
# fmt: off
lowerCamelCase_ =torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30], lowerCAmelCase, atol=1e-5, rtol=1e-5 )
@unittest.skip('''Model is curently gated''' )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
lowerCamelCase_ ='''Simply put, the theory of relativity states that '''
lowerCamelCase_ =LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' )
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, return_tensors='''pt''' )
lowerCamelCase_ =LlamaForCausalLM.from_pretrained(
'''meta-llama/Llama-2-13b-chat-hf''', device_map='''sequential''', use_safetensors=lowerCAmelCase )
# greedy generation outputs
lowerCamelCase_ =model.generate(lowerCAmelCase, max_new_tokens=64, top_p=lowerCAmelCase, temperature=1, do_sample=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(generated_ids[0], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
| 75 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ : Union[str, Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Tuple =['pixel_values']
def __init__( self, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = True, lowerCAmelCase = 1 / 255, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = True, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =size if size is not None else {'''shortest_edge''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
lowerCamelCase_ =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase, param_name='''crop_size''' )
lowerCamelCase_ =do_resize
lowerCamelCase_ =size
lowerCamelCase_ =resample
lowerCamelCase_ =do_center_crop
lowerCamelCase_ =crop_size
lowerCamelCase_ =do_rescale
lowerCamelCase_ =rescale_factor
lowerCamelCase_ =do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCamelCase_ =image_std if image_std is not None else OPENAI_CLIP_STD
lowerCamelCase_ =do_convert_rgb
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowerCamelCase_ =get_resize_output_image_size(lowerCAmelCase, size=size['''shortest_edge'''], default_to_square=lowerCAmelCase )
return resize(lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowerCAmelCase, size=(size['''height'''], size['''width''']), data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return rescale(lowerCAmelCase, scale=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return normalize(lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = ChannelDimension.FIRST, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ =size if size is not None else self.size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =resample if resample is not None else self.resample
lowerCamelCase_ =do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase_ =crop_size if crop_size is not None else self.crop_size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''crop_size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ =rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ =do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ =image_std if image_std is not None else self.image_std
lowerCamelCase_ =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase_ =make_list_of_images(lowerCAmelCase )
if not valid_images(lowerCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase_ =[convert_to_rgb(lowerCAmelCase ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase_ =[to_numpy_array(lowerCAmelCase ) for image in images]
if do_resize:
lowerCamelCase_ =[self.resize(image=lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase ) for image in images]
if do_center_crop:
lowerCamelCase_ =[self.center_crop(image=lowerCAmelCase, size=lowerCAmelCase ) for image in images]
if do_rescale:
lowerCamelCase_ =[self.rescale(image=lowerCAmelCase, scale=lowerCAmelCase ) for image in images]
if do_normalize:
lowerCamelCase_ =[self.normalize(image=lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase ) for image in images]
lowerCamelCase_ =[to_channel_dimension_format(lowerCAmelCase, lowerCAmelCase ) for image in images]
lowerCamelCase_ ={'''pixel_values''': images}
return BatchFeature(data=lowerCAmelCase, tensor_type=lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
a_ : Dict = """1"""
a_ : Dict = """0"""
a_ : Tuple = """1"""
a_ : Optional[Any] = ort.SessionOptions()
a_ : Optional[Any] = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
a_ : Any = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
a_ : List[str] = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
a_ : Any = ort.RunOptions()
a_ : List[Any] = 1_28
a_ : Optional[int] = 1
a_ : Optional[int] = np.ones((batch, sequence), dtype=np.intaa)
a_ : Optional[int] = np.ones((batch, sequence), dtype=np.intaa)
a_ : str = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
a_ : List[str] = time.time()
a_ : Optional[int] = 20_00
a_ : Dict = {}
for iter in range(max_iters):
a_ : int = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 10_00 / max_iters))
| 75 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : str , __snake_case : list[str] | None = None , __snake_case : dict[str, float] | None = None , __snake_case : bool = False , ) -> tuple[int, float, str]:
"""simple docstring"""
lowerCamelCase_ =cipher_alphabet or [chr(__snake_case ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
lowerCamelCase_ ={
'''a''': 0.0_8_4_9_7,
'''b''': 0.0_1_4_9_2,
'''c''': 0.0_2_2_0_2,
'''d''': 0.0_4_2_5_3,
'''e''': 0.1_1_1_6_2,
'''f''': 0.0_2_2_2_8,
'''g''': 0.0_2_0_1_5,
'''h''': 0.0_6_0_9_4,
'''i''': 0.0_7_5_4_6,
'''j''': 0.0_0_1_5_3,
'''k''': 0.0_1_2_9_2,
'''l''': 0.0_4_0_2_5,
'''m''': 0.0_2_4_0_6,
'''n''': 0.0_6_7_4_9,
'''o''': 0.0_7_5_0_7,
'''p''': 0.0_1_9_2_9,
'''q''': 0.0_0_0_9_5,
'''r''': 0.0_7_5_8_7,
'''s''': 0.0_6_3_2_7,
'''t''': 0.0_9_3_5_6,
'''u''': 0.0_2_7_5_8,
'''v''': 0.0_0_9_7_8,
'''w''': 0.0_2_5_6_0,
'''x''': 0.0_0_1_5_0,
'''y''': 0.0_1_9_9_4,
'''z''': 0.0_0_0_7_7,
}
else:
# Custom frequencies dictionary
lowerCamelCase_ =frequencies_dict
if not case_sensitive:
lowerCamelCase_ =ciphertext.lower()
# Chi squared statistic values
lowerCamelCase_ ={}
# cycle through all of the shifts
for shift in range(len(__snake_case ) ):
lowerCamelCase_ =''''''
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
lowerCamelCase_ =(alphabet_letters.index(letter.lower() ) - shift) % len(
__snake_case )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
lowerCamelCase_ =0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
lowerCamelCase_ =letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.lower().count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
lowerCamelCase_ =(
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(__snake_case : int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
lowerCamelCase_ =min(
__snake_case , key=__snake_case , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
lowerCamelCase_
), (
lowerCamelCase_
),
) =chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 75 | 1 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = False, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(features=lowerCAmelCase, cache_dir=lowerCAmelCase, keep_in_memory=lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =Sql(
cache_dir=lowerCAmelCase, features=lowerCAmelCase, sql=lowerCAmelCase, con=lowerCAmelCase, **lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =None
lowerCamelCase_ =None
lowerCamelCase_ =None
lowerCamelCase_ =None
self.builder.download_and_prepare(
download_config=lowerCAmelCase, download_mode=lowerCAmelCase, verification_mode=lowerCAmelCase, base_path=lowerCAmelCase, )
# Build dataset for splits
lowerCamelCase_ =self.builder.as_dataset(
split='''train''', verification_mode=lowerCAmelCase, in_memory=self.keep_in_memory )
return dataset
class __UpperCamelCase :
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
lowerCamelCase_ =dataset
lowerCamelCase_ =name
lowerCamelCase_ =con
lowerCamelCase_ =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
lowerCamelCase_ =num_proc
lowerCamelCase_ =to_sql_kwargs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.to_sql_kwargs.pop('''sql''', lowerCAmelCase )
lowerCamelCase_ =self.to_sql_kwargs.pop('''con''', lowerCAmelCase )
lowerCamelCase_ =self.to_sql_kwargs.pop('''index''', lowerCAmelCase )
lowerCamelCase_ =self._write(index=lowerCAmelCase, **self.to_sql_kwargs )
return written
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =args
lowerCamelCase_ ={**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
lowerCamelCase_ =query_table(
table=self.dataset.data, key=slice(lowerCAmelCase, offset + self.batch_size ), indices=self.dataset._indices, )
lowerCamelCase_ =batch.to_pandas()
lowerCamelCase_ =df.to_sql(self.name, self.con, index=lowerCAmelCase, **lowerCAmelCase )
return num_rows or len(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0, len(self.dataset ), self.batch_size ), unit='''ba''', disable=not logging.is_progress_bar_enabled(), desc='''Creating SQL from Arrow format''', ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
lowerCamelCase_, lowerCamelCase_ =len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql, [(offset, index, to_sql_kwargs) for offset in range(0, lowerCAmelCase, lowerCAmelCase )], ), total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size, unit='''ba''', disable=not logging.is_progress_bar_enabled(), desc='''Creating SQL from Arrow format''', ):
written += num_rows
return written
| 75 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
a_ : List[Any] = (
"""https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)
a_ : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def a_ ( ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ ='''https://pypi.org/pypi/diffusers/json'''
lowerCamelCase_ =json.loads(request.urlopen(__snake_case ).read() )['''releases'''].keys()
return sorted(__snake_case , key=lambda __snake_case : version.Version(__snake_case ) )
def a_ ( ) -> str:
"""simple docstring"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__snake_case )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase_ =Path(__snake_case ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def a_ ( __snake_case : Union[str, os.PathLike] ) -> List[str]:
"""simple docstring"""
init_hf_modules()
lowerCamelCase_ =Path(__snake_case ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase_ =dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def a_ ( __snake_case : Tuple ) -> List[str]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowerCamelCase_ =f.read()
# Imports of the form `import .xxx`
lowerCamelCase_ =re.findall('''^\s*import\s+\.(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Unique-ify
return list(set(__snake_case ) )
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
lowerCamelCase_ =False
lowerCamelCase_ =[module_file]
lowerCamelCase_ =[]
# Let's recurse through all relative imports
while not no_change:
lowerCamelCase_ =[]
for f in files_to_check:
new_imports.extend(get_relative_imports(__snake_case ) )
lowerCamelCase_ =Path(__snake_case ).parent
lowerCamelCase_ =[str(module_path / m ) for m in new_imports]
lowerCamelCase_ =[f for f in new_import_files if f not in all_relative_imports]
lowerCamelCase_ =[F'''{f}.py''' for f in new_import_files]
lowerCamelCase_ =len(__snake_case ) == 0
all_relative_imports.extend(__snake_case )
return all_relative_imports
def a_ ( __snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowerCamelCase_ =f.read()
# Imports of the form `import xxx`
lowerCamelCase_ =re.findall('''^\s*import\s+(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Only keep the top-level module
lowerCamelCase_ =[imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
lowerCamelCase_ =list(set(__snake_case ) )
lowerCamelCase_ =[]
for imp in imports:
try:
importlib.import_module(__snake_case )
except ImportError:
missing_packages.append(__snake_case )
if len(__snake_case ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
F'''{', '.join(__snake_case )}. Run `pip install {' '.join(__snake_case )}`''' )
return get_relative_imports(__snake_case )
def a_ ( __snake_case : Tuple , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =module_path.replace(os.path.sep , '''.''' )
lowerCamelCase_ =importlib.import_module(__snake_case )
if class_name is None:
return find_pipeline_class(__snake_case )
return getattr(__snake_case , __snake_case )
def a_ ( __snake_case : Dict ) -> Any:
"""simple docstring"""
from ..pipelines import DiffusionPipeline
lowerCamelCase_ =dict(inspect.getmembers(__snake_case , inspect.isclass ) )
lowerCamelCase_ =None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __snake_case )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''' )
lowerCamelCase_ =cls
return pipeline_class
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =str(__snake_case )
lowerCamelCase_ =os.path.join(__snake_case , __snake_case )
if os.path.isfile(__snake_case ):
lowerCamelCase_ =module_file_or_url
lowerCamelCase_ ='''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
lowerCamelCase_ =get_diffusers_versions()
# cut ".dev0"
lowerCamelCase_ ='''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
lowerCamelCase_ =latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(F'''Defaulting to latest_version: {revision}.''' )
elif revision in available_versions:
lowerCamelCase_ =F'''v{revision}'''
elif revision == "main":
lowerCamelCase_ =revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {', '.join(available_versions + ['main'] )}.''' )
# community pipeline on GitHub
lowerCamelCase_ =COMMUNITY_PIPELINES_URL.format(revision=__snake_case , pipeline=__snake_case )
try:
lowerCamelCase_ =cached_download(
__snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowerCamelCase_ ='''git'''
lowerCamelCase_ =pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
else:
try:
# Load from URL or cache if already cached
lowerCamelCase_ =hf_hub_download(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowerCamelCase_ =os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
# Check we have all the requirements in our environment
lowerCamelCase_ =check_imports(__snake_case )
# Now we move the module inside our cached dynamic modules.
lowerCamelCase_ =DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__snake_case )
lowerCamelCase_ =Path(__snake_case ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__snake_case , submodule_path / module_file )
for module_needed in modules_needed:
lowerCamelCase_ =F'''{module_needed}.py'''
shutil.copy(os.path.join(__snake_case , __snake_case ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__snake_case , __snake_case ):
lowerCamelCase_ =use_auth_token
elif use_auth_token is True:
lowerCamelCase_ =HfFolder.get_token()
else:
lowerCamelCase_ =None
lowerCamelCase_ =model_info(__snake_case , revision=__snake_case , token=__snake_case ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
lowerCamelCase_ =submodule_path / commit_hash
lowerCamelCase_ =full_submodule + os.path.sep + commit_hash
create_dynamic_module(__snake_case )
if not (submodule_path / module_file).exists():
shutil.copy(__snake_case , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__snake_case , F'''{module_needed}.py''' , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return os.path.join(__snake_case , __snake_case )
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[str] = None , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =get_cached_module_file(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return get_class_in_module(__snake_case , final_module.replace('''.py''' , '''''' ) )
| 75 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ : Tuple = {
"""configuration_owlvit""": [
"""OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""OwlViTConfig""",
"""OwlViTOnnxConfig""",
"""OwlViTTextConfig""",
"""OwlViTVisionConfig""",
],
"""processing_owlvit""": ["""OwlViTProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Any = ["""OwlViTFeatureExtractor"""]
a_ : List[str] = ["""OwlViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = [
"""OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OwlViTModel""",
"""OwlViTPreTrainedModel""",
"""OwlViTTextModel""",
"""OwlViTVisionModel""",
"""OwlViTForObjectDetection""",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
a_ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 75 |
'''simple docstring'''
a_ : Any = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
a_ : Any = [
9_99,
9_76,
9_52,
9_28,
9_05,
8_82,
8_58,
8_57,
8_10,
7_62,
7_15,
7_14,
5_72,
4_29,
4_28,
2_86,
2_85,
2_38,
1_90,
1_43,
1_42,
1_18,
95,
71,
47,
24,
0,
]
a_ : Optional[Any] = [
9_99,
9_88,
9_77,
9_66,
9_55,
9_44,
9_33,
9_22,
9_11,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_50,
3_00,
2_99,
2_66,
2_33,
2_00,
1_99,
1_79,
1_59,
1_40,
1_20,
1_00,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
a_ : str = [
9_99,
9_95,
9_92,
9_89,
9_85,
9_81,
9_78,
9_75,
9_71,
9_67,
9_64,
9_61,
9_57,
9_56,
9_51,
9_47,
9_42,
9_37,
9_33,
9_28,
9_23,
9_19,
9_14,
9_13,
9_08,
9_03,
8_97,
8_92,
8_87,
8_81,
8_76,
8_71,
8_70,
8_64,
8_58,
8_52,
8_46,
8_40,
8_34,
8_28,
8_27,
8_20,
8_13,
8_06,
7_99,
7_92,
7_85,
7_84,
7_77,
7_70,
7_63,
7_56,
7_49,
7_42,
7_41,
7_33,
7_24,
7_16,
7_07,
6_99,
6_98,
6_88,
6_77,
6_66,
6_56,
6_55,
6_45,
6_34,
6_23,
6_13,
6_12,
5_98,
5_84,
5_70,
5_69,
5_55,
5_41,
5_27,
5_26,
5_05,
4_84,
4_83,
4_62,
4_40,
4_39,
3_96,
3_95,
3_52,
3_51,
3_08,
3_07,
2_64,
2_63,
2_20,
2_19,
1_76,
1_32,
88,
44,
0,
]
a_ : Optional[int] = [
9_99,
9_97,
9_95,
9_92,
9_90,
9_88,
9_86,
9_84,
9_81,
9_79,
9_77,
9_75,
9_72,
9_70,
9_68,
9_66,
9_64,
9_61,
9_59,
9_57,
9_56,
9_54,
9_51,
9_49,
9_46,
9_44,
9_41,
9_39,
9_36,
9_34,
9_31,
9_29,
9_26,
9_24,
9_21,
9_19,
9_16,
9_14,
9_13,
9_10,
9_07,
9_05,
9_02,
8_99,
8_96,
8_93,
8_91,
8_88,
8_85,
8_82,
8_79,
8_77,
8_74,
8_71,
8_70,
8_67,
8_64,
8_61,
8_58,
8_55,
8_52,
8_49,
8_46,
8_43,
8_40,
8_37,
8_34,
8_31,
8_28,
8_27,
8_24,
8_21,
8_17,
8_14,
8_11,
8_08,
8_04,
8_01,
7_98,
7_95,
7_91,
7_88,
7_85,
7_84,
7_80,
7_77,
7_74,
7_70,
7_66,
7_63,
7_60,
7_56,
7_52,
7_49,
7_46,
7_42,
7_41,
7_37,
7_33,
7_30,
7_26,
7_22,
7_18,
7_14,
7_10,
7_07,
7_03,
6_99,
6_98,
6_94,
6_90,
6_85,
6_81,
6_77,
6_73,
6_69,
6_64,
6_60,
6_56,
6_55,
6_50,
6_46,
6_41,
6_36,
6_32,
6_27,
6_22,
6_18,
6_13,
6_12,
6_07,
6_02,
5_96,
5_91,
5_86,
5_80,
5_75,
5_70,
5_69,
5_63,
5_57,
5_51,
5_45,
5_39,
5_33,
5_27,
5_26,
5_19,
5_12,
5_05,
4_98,
4_91,
4_84,
4_83,
4_74,
4_66,
4_57,
4_49,
4_40,
4_39,
4_28,
4_18,
4_07,
3_96,
3_95,
3_81,
3_66,
3_52,
3_51,
3_30,
3_08,
3_07,
2_86,
2_64,
2_63,
2_42,
2_20,
2_19,
1_76,
1_75,
1_32,
1_31,
88,
44,
0,
]
a_ : Dict = [
9_99,
9_91,
9_82,
9_74,
9_66,
9_58,
9_50,
9_41,
9_33,
9_25,
9_16,
9_08,
9_00,
8_99,
8_74,
8_50,
8_25,
8_00,
7_99,
7_00,
6_00,
5_00,
4_00,
3_00,
2_00,
1_00,
0,
]
a_ : Tuple = [
9_99,
9_92,
9_85,
9_78,
9_71,
9_64,
9_57,
9_49,
9_42,
9_35,
9_28,
9_21,
9_14,
9_07,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_00,
2_99,
2_00,
1_99,
1_00,
99,
0,
]
a_ : Any = [
9_99,
9_96,
9_92,
9_89,
9_85,
9_82,
9_79,
9_75,
9_72,
9_68,
9_65,
9_61,
9_58,
9_55,
9_51,
9_48,
9_44,
9_41,
9_38,
9_34,
9_31,
9_27,
9_24,
9_20,
9_17,
9_14,
9_10,
9_07,
9_03,
9_00,
8_99,
8_91,
8_84,
8_76,
8_69,
8_61,
8_53,
8_46,
8_38,
8_30,
8_23,
8_15,
8_08,
8_00,
7_99,
7_88,
7_77,
7_66,
7_55,
7_44,
7_33,
7_22,
7_11,
7_00,
6_99,
6_88,
6_77,
6_66,
6_55,
6_44,
6_33,
6_22,
6_11,
6_00,
5_99,
5_85,
5_71,
5_57,
5_42,
5_28,
5_14,
5_00,
4_99,
4_85,
4_71,
4_57,
4_42,
4_28,
4_14,
4_00,
3_99,
3_79,
3_59,
3_40,
3_20,
3_00,
2_99,
2_79,
2_59,
2_40,
2_20,
2_00,
1_99,
1_66,
1_33,
1_00,
99,
66,
33,
0,
]
| 75 | 1 |
'''simple docstring'''
def a_ ( __snake_case : int , __snake_case : int ) -> int:
"""simple docstring"""
while b:
lowerCamelCase_, lowerCamelCase_ =b, a % b
return a
def a_ ( __snake_case : int , __snake_case : int ) -> int:
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(__snake_case , a % b )
def a_ ( ) -> str:
"""simple docstring"""
print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}''' )
print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}''' )
print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}''' )
print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}''' )
print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}''' )
print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}''' )
print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}''' )
print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}''' )
print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}''' )
print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}''' )
if __name__ == "__main__":
main()
| 75 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""convert_funnel_original_tf_checkpoint_to_pytorch""": [],
"""tokenization_funnel""": ["""FunnelTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[str] = ["""FunnelTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[int] = [
"""FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FunnelBaseModel""",
"""FunnelForMaskedLM""",
"""FunnelForMultipleChoice""",
"""FunnelForPreTraining""",
"""FunnelForQuestionAnswering""",
"""FunnelForSequenceClassification""",
"""FunnelForTokenClassification""",
"""FunnelModel""",
"""FunnelPreTrainedModel""",
"""load_tf_weights_in_funnel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = [
"""TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFFunnelBaseModel""",
"""TFFunnelForMaskedLM""",
"""TFFunnelForMultipleChoice""",
"""TFFunnelForPreTraining""",
"""TFFunnelForQuestionAnswering""",
"""TFFunnelForSequenceClassification""",
"""TFFunnelForTokenClassification""",
"""TFFunnelModel""",
"""TFFunnelPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
a_ : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 75 | 1 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase :
def __init__( self, lowerCAmelCase, lowerCAmelCase=13, lowerCAmelCase=30, lowerCAmelCase=2, lowerCAmelCase=3, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=32, lowerCAmelCase=5, lowerCAmelCase=4, lowerCAmelCase=37, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=10, lowerCAmelCase=0.0_2, lowerCAmelCase=3, lowerCAmelCase=0.6, lowerCAmelCase=None, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =image_size
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =is_training
lowerCamelCase_ =use_labels
lowerCamelCase_ =hidden_size
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =intermediate_size
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =attention_probs_dropout_prob
lowerCamelCase_ =type_sequence_label_size
lowerCamelCase_ =initializer_range
lowerCamelCase_ =mask_ratio
lowerCamelCase_ =scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowerCamelCase_ =(image_size // patch_size) ** 2
lowerCamelCase_ =int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ =None
if self.use_labels:
lowerCamelCase_ =ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase_ =self.get_config()
return config, pixel_values, labels
def lowercase__ ( self ):
"""simple docstring"""
return ViTMAEConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=lowerCAmelCase, initializer_range=self.initializer_range, mask_ratio=self.mask_ratio, )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =ViTMAEModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =ViTMAEForPreTraining(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =model(lowerCAmelCase )
lowerCamelCase_ =(self.image_size // self.patch_size) ** 2
lowerCamelCase_ =self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
lowerCamelCase_ =1
lowerCamelCase_ =ViTMAEForPreTraining(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase_ =model(lowerCAmelCase )
lowerCamelCase_ =self.patch_size**2
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =config_and_inputs
lowerCamelCase_ ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : Optional[int] =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
lowercase : Dict ={'feature-extraction': ViTMAEModel} if is_torch_available() else {}
lowercase : Union[str, Any] =False
lowercase : str =False
lowercase : List[Any] =False
lowercase : str =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =ViTMAEModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, has_text_modality=lowerCAmelCase, hidden_size=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
lowerCamelCase_ =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase, nn.Linear ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
lowerCamelCase_ =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ =[*signature.parameters.keys()]
lowerCamelCase_ =['''pixel_values''']
self.assertListEqual(arg_names[:1], lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
np.random.seed(2 )
lowerCamelCase_ =int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
lowerCamelCase_ =np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowerCamelCase_ =torch.from_numpy(lowerCAmelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowerCamelCase_ =pt_noise
super().check_pt_tf_models(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowerCamelCase_ =model(**self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
lowerCamelCase_ =outputs[0].cpu().numpy()
lowerCamelCase_ =0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =model_class.from_pretrained(lowerCAmelCase )
model.to(lowerCAmelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowerCamelCase_ =model(**self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
# Make sure we don't have nans
lowerCamelCase_ =after_outputs[0].cpu().numpy()
lowerCamelCase_ =0
lowerCamelCase_ =np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCAmelCase, 1e-5 )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =ViTMAEModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def a_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def lowercase__ ( self ):
"""simple docstring"""
np.random.seed(2 )
lowerCamelCase_ =ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' ).to(lowerCAmelCase )
lowerCamelCase_ =self.default_image_processor
lowerCamelCase_ =prepare_img()
lowerCamelCase_ =image_processor(images=lowerCAmelCase, return_tensors='''pt''' ).to(lowerCAmelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowerCamelCase_ =ViTMAEConfig()
lowerCamelCase_ =int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
lowerCamelCase_ =np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
lowerCamelCase_ =model(**lowerCAmelCase, noise=torch.from_numpy(lowerCAmelCase ).to(device=lowerCAmelCase ) )
# verify the logits
lowerCamelCase_ =torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape, lowerCAmelCase )
lowerCamelCase_ =torch.tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice.to(lowerCAmelCase ), atol=1e-4 ) )
| 75 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
lowerCamelCase_ ={
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 16_000,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(self.tmpdirname, lowerCAmelCase )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
with open(self.feature_extraction_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
# load decoder from hub
lowerCamelCase_ ='''hf-internal-testing/ngram-beam-search-decoder'''
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.add_kwargs_tokens_map.copy()
kwargs.update(lowerCAmelCase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name, **lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer, lowerCAmelCase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor, lowerCAmelCase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels, decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set, decoder.model_container[decoder._model_key]._unigram_set, )
self.assertIsInstance(processor.decoder, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname, alpha=5.0, beta=3.0, score_boundary=-7.0, unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha, 5.0 )
self.assertEqual(processor.language_model.beta, 3.0 )
self.assertEqual(processor.language_model.score_boundary, -7.0 )
self.assertEqual(processor.language_model.unk_score_offset, 3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(lowerCAmelCase, '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=lowerCAmelCase, feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =floats_list((3, 1_000) )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =processor(lowerCAmelCase, return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ ='''This is a test string'''
lowerCamelCase_ =processor(text=lowerCAmelCase )
lowerCamelCase_ =tokenizer(lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def lowercase__ ( self, lowerCAmelCase=(2, 10, 16), lowerCAmelCase=77 ):
"""simple docstring"""
np.random.seed(lowerCAmelCase )
return np.random.rand(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits(shape=(10, 16), seed=13 )
lowerCamelCase_ =processor.decode(lowerCAmelCase )
lowerCamelCase_ =decoder.decode_beams(lowerCAmelCase )[0]
self.assertEqual(decoded_decoder[0], decoded_processor.text )
self.assertEqual('''</s> <s> </s>''', decoded_processor.text )
self.assertEqual(decoded_decoder[-2], decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1], decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase )
else:
with get_context(lowerCAmelCase ).Pool() as pool:
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =list(lowerCAmelCase )
with get_context('''fork''' ).Pool() as p:
lowerCamelCase_ =decoder.decode_beams_batch(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =[], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowerCAmelCase, decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''], decoded_processor.text )
self.assertListEqual(lowerCAmelCase, decoded_processor.logit_score )
self.assertListEqual(lowerCAmelCase, decoded_processor.lm_score )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =15
lowerCamelCase_ =-2_0.0
lowerCamelCase_ =-4.0
lowerCamelCase_ =processor.batch_decode(
lowerCAmelCase, beam_width=lowerCAmelCase, beam_prune_logp=lowerCAmelCase, token_min_logp=lowerCAmelCase, )
lowerCamelCase_ =decoded_processor_out.text
lowerCamelCase_ =list(lowerCAmelCase )
with get_context('''fork''' ).Pool() as pool:
lowerCamelCase_ =decoder.decode_beams_batch(
lowerCAmelCase, lowerCAmelCase, beam_width=lowerCAmelCase, beam_prune_logp=lowerCAmelCase, token_min_logp=lowerCAmelCase, )
lowerCamelCase_ =[d[0][0] for d in decoded_decoder_out]
lowerCamelCase_ =[d[0][2] for d in decoded_decoder_out]
lowerCamelCase_ =[d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''], lowerCAmelCase )
self.assertTrue(np.array_equal(lowerCAmelCase, decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7], lowerCAmelCase, atol=1e-3 ) )
self.assertTrue(np.array_equal(lowerCAmelCase, decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4], lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =2.0
lowerCamelCase_ =5.0
lowerCamelCase_ =-2_0.0
lowerCamelCase_ =True
lowerCamelCase_ =processor.batch_decode(
lowerCAmelCase, alpha=lowerCAmelCase, beta=lowerCAmelCase, unk_score_offset=lowerCAmelCase, lm_score_boundary=lowerCAmelCase, )
lowerCamelCase_ =decoded_processor_out.text
lowerCamelCase_ =list(lowerCAmelCase )
decoder.reset_params(
alpha=lowerCAmelCase, beta=lowerCAmelCase, unk_score_offset=lowerCAmelCase, lm_score_boundary=lowerCAmelCase, )
with get_context('''fork''' ).Pool() as pool:
lowerCamelCase_ =decoder.decode_beams_batch(
lowerCAmelCase, lowerCAmelCase, )
lowerCamelCase_ =[d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''], lowerCAmelCase )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha, 2.0 )
self.assertEqual(lm_model.beta, 5.0 )
self.assertEqual(lm_model.unk_score_offset, -2_0.0 )
self.assertEqual(lm_model.score_boundary, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
lowerCamelCase_ =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
lowerCamelCase_ =os.listdir(lowerCAmelCase )
lowerCamelCase_ =['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =snapshot_download('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
lowerCamelCase_ =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
lowerCamelCase_ =os.listdir(lowerCAmelCase )
lowerCamelCase_ =os.listdir(lowerCAmelCase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =floats_list((3, 1_000) )
lowerCamelCase_ =processor_wavaveca(lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =processor_auto(lowerCAmelCase, return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum(), input_auto[key].sum(), delta=1e-2 )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =processor_wavaveca.batch_decode(lowerCAmelCase )
lowerCamelCase_ =processor_auto.batch_decode(lowerCAmelCase )
self.assertListEqual(decoded_wavaveca.text, decoded_auto.text )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
self.assertListEqual(
processor.model_input_names, feature_extractor.model_input_names, msg='''`processor` and `feature_extractor` model input names do not match''', )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[d[key] for d in offsets]
return retrieved_list
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =self._get_dummy_logits()[0]
lowerCamelCase_ =processor.decode(lowerCAmelCase, output_word_offsets=lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ), 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(lowerCAmelCase, lowerCAmelCase ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''], '''word''' ) ), outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''word''' ), ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''start_offset''' ), [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''end_offset''' ), [1, 3, 5] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase, output_word_offsets=lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ), 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(lowerCAmelCase, lowerCAmelCase ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ) for o in outputs['''word_offsets''']], outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''word''' ), ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''start_offset''' ), [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''end_offset''' ), [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =load_dataset('''common_voice''', '''en''', split='''train''', streaming=lowerCAmelCase )
lowerCamelCase_ =ds.cast_column('''audio''', datasets.Audio(sampling_rate=16_000 ) )
lowerCamelCase_ =iter(lowerCAmelCase )
lowerCamelCase_ =next(lowerCAmelCase )
lowerCamelCase_ =AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
lowerCamelCase_ =WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
lowerCamelCase_ =processor(sample['''audio''']['''array'''], return_tensors='''pt''' ).input_values
with torch.no_grad():
lowerCamelCase_ =model(lowerCAmelCase ).logits.cpu().numpy()
lowerCamelCase_ =processor.decode(logits[0], output_word_offsets=lowerCAmelCase )
lowerCamelCase_ =model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
lowerCamelCase_ =[
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
lowerCamelCase_ ='''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ), lowerCAmelCase )
self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ), output.text )
# output times
lowerCamelCase_ =torch.tensor(self.get_from_offsets(lowerCAmelCase, '''start_time''' ) )
lowerCamelCase_ =torch.tensor(self.get_from_offsets(lowerCAmelCase, '''end_time''' ) )
# fmt: off
lowerCamelCase_ =torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
lowerCamelCase_ =torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=0.0_1 ) )
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=0.0_1 ) )
| 75 | 1 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a_ : Tuple = HUGGINGFACE_HUB_CACHE
a_ : Dict = """config.json"""
a_ : List[str] = """diffusion_pytorch_model.bin"""
a_ : Dict = """diffusion_flax_model.msgpack"""
a_ : str = """model.onnx"""
a_ : str = """diffusion_pytorch_model.safetensors"""
a_ : Any = """weights.pb"""
a_ : Optional[Any] = """https://huggingface.co"""
a_ : Union[str, Any] = default_cache_path
a_ : Optional[int] = """diffusers_modules"""
a_ : int = os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
a_ : Union[str, Any] = ["""fp16""", """non-ema"""]
a_ : str = """.self_attn"""
| 75 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =StableDiffusionInstructPixaPixPipeline
lowercase : List[Any] =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
lowercase : Optional[Any] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase : Union[str, Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase : List[Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=8, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=32, )
lowerCamelCase_ =PNDMScheduler(skip_prk_steps=lowerCAmelCase )
torch.manual_seed(0 )
lowerCamelCase_ =AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
torch.manual_seed(0 )
lowerCamelCase_ =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, )
lowerCamelCase_ =CLIPTextModel(lowerCAmelCase )
lowerCamelCase_ =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCamelCase_ ={
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowerCamelCase_ =image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase_ =Image.fromarray(np.uinta(lowerCAmelCase ) ).convert('''RGB''' )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ ='''french fries'''
lowerCamelCase_ =sd_pipe(**lowerCAmelCase, negative_prompt=lowerCAmelCase )
lowerCamelCase_ =output.images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =[inputs['''prompt''']] * 2
lowerCamelCase_ =np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
lowerCamelCase_ =torch.from_numpy(lowerCAmelCase ).unsqueeze(0 ).to(lowerCAmelCase )
lowerCamelCase_ =image / 2 + 0.5
lowerCamelCase_ =image.permute(0, 3, 1, 2 )
lowerCamelCase_ =image.repeat(2, 1, 1, 1 )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
lowerCamelCase_ =np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='''scaled_linear''' )
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
lowerCamelCase_ =[round(lowerCAmelCase, 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(lowerCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =VaeImageProcessor(do_resize=lowerCAmelCase, do_normalize=lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' ) )[0]
lowerCamelCase_ =components['''vae''']
lowerCamelCase_ =self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
lowerCamelCase_ =vae.encode(inputs[image_param] ).latent_dist.mode()
lowerCamelCase_ =pipe(**lowerCAmelCase )[0]
lowerCamelCase_ =np.abs(out - out_latents_inputs ).max()
self.assertLess(lowerCAmelCase, 1e-4, '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
lowerCamelCase_ ={
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0
def callback_fn(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ) -> None:
lowerCamelCase_ =True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowerCamelCase_ =False
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
pipe(**lowerCAmelCase, callback=lowerCAmelCase, callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowercase__ ( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCamelCase_ =inputs['''image'''].resize((504, 504) )
lowerCamelCase_ ='''timbrooks/instruct-pix2pix'''
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
lowerCAmelCase, safety_checker=lowerCAmelCase, )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =output.images[0]
lowerCamelCase_ =image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
lowerCamelCase_ =np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 75 | 1 |
'''simple docstring'''
import sys
a_ : Tuple = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def a_ ( __snake_case : str = N ) -> int:
"""simple docstring"""
lowerCamelCase_ =-sys.maxsize - 1
for i in range(len(__snake_case ) - 12 ):
lowerCamelCase_ =1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
lowerCamelCase_ =product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 75 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __UpperCamelCase :
lowercase : Union[str, Any] =XGLMConfig
lowercase : Optional[Any] ={}
lowercase : Optional[int] ='gelu'
def __init__( self, lowerCAmelCase, lowerCAmelCase=14, lowerCAmelCase=7, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=99, lowerCAmelCase=32, lowerCAmelCase=2, lowerCAmelCase=4, lowerCAmelCase=37, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=512, lowerCAmelCase=0.0_2, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =is_training
lowerCamelCase_ =use_input_mask
lowerCamelCase_ =use_labels
lowerCamelCase_ =vocab_size
lowerCamelCase_ =d_model
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =ffn_dim
lowerCamelCase_ =activation_function
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =initializer_range
lowerCamelCase_ =None
lowerCamelCase_ =0
lowerCamelCase_ =2
lowerCamelCase_ =1
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length], self.vocab_size ), clip_value_min=0, clip_value_max=3 )
lowerCamelCase_ =None
if self.use_input_mask:
lowerCamelCase_ =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ =self.get_config()
lowerCamelCase_ =floats_tensor([self.num_hidden_layers, self.num_attention_heads], 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size, d_model=self.hidden_size, num_layers=self.num_hidden_layers, attention_heads=self.num_attention_heads, ffn_dim=self.ffn_dim, activation_function=self.activation_function, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=lowerCAmelCase, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =config_and_inputs
lowerCamelCase_ ={
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : int =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowercase : Optional[Any] =(TFXGLMForCausalLM,) if is_tf_available() else ()
lowercase : Tuple =(
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowercase : Optional[Any] =False
lowercase : Optional[Any] =False
lowercase : Optional[int] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, n_embd=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =TFXGLMModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
def lowercase__ ( self ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def lowercase__ ( self, lowerCAmelCase=True ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =tf.convert_to_tensor([[2, 268, 9_865]], dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
lowerCamelCase_ =[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
# fmt: on
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist(), lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
tf.random.set_seed(0 )
lowerCamelCase_ =tokenizer('''Today is a nice day and''', return_tensors='''tf''' )
lowerCamelCase_ =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(''':/CPU:0''' ):
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, seed=[7, 0] )
lowerCamelCase_ =tokenizer.decode(output_ids[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =(
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ ='''left'''
# use different length sentences to test batching
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
lowerCamelCase_ =tokenizer(lowerCAmelCase, return_tensors='''tf''', padding=lowerCAmelCase )
lowerCamelCase_ =inputs['''input_ids''']
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, attention_mask=inputs['''attention_mask'''], max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[0], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[1], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer.batch_decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_non_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [non_padded_sentence, padded_sentence] )
| 75 | 1 |
'''simple docstring'''
from math import factorial
def a_ ( __snake_case : int = 100 ) -> int:
"""simple docstring"""
return sum(int(__snake_case ) for x in str(factorial(__snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 75 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __UpperCamelCase :
@staticmethod
def lowercase__ ( *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
lowercase : int =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =object_detector(examples[0], threshold=0.0 )
lowerCamelCase_ =len(lowerCAmelCase )
self.assertGreater(lowerCAmelCase, 0 )
self.assertEqual(
lowerCAmelCase, [
{
'''score''': ANY(lowerCAmelCase ),
'''label''': ANY(lowerCAmelCase ),
'''box''': {'''xmin''': ANY(lowerCAmelCase ), '''ymin''': ANY(lowerCAmelCase ), '''xmax''': ANY(lowerCAmelCase ), '''ymax''': ANY(lowerCAmelCase )},
}
for i in range(lowerCAmelCase )
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
]
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0.2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], top_k=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
], )
| 75 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Optional[int] =BioGptTokenizer
lowercase : List[Any] =False
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase_ =[
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file, '''w''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) )
with open(self.merges_file, '''w''' ) as fp:
fp.write('''\n'''.join(lowerCAmelCase ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ='''lower newer'''
lowerCamelCase_ ='''lower newer'''
return input_text, output_text
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BioGptTokenizer(self.vocab_file, self.merges_file )
lowerCamelCase_ ='''lower'''
lowerCamelCase_ =['''low''', '''er</w>''']
lowerCamelCase_ =tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =tokens + ['''<unk>''']
lowerCamelCase_ =[14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ), lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
lowerCamelCase_ =tokenizer.encode('''sequence builders''', add_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.encode('''multi-sequence build''', add_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
lowerCamelCase_ =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase, lowerCAmelCase )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 75 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[int] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a_ : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a_ : Optional[int] = {"""facebook/blenderbot_small-90M""": 5_12}
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =set()
lowerCamelCase_ =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ =char
lowerCamelCase_ =set(__snake_case )
return pairs
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[int] =VOCAB_FILES_NAMES
lowercase : Tuple =PRETRAINED_VOCAB_FILES_MAP
lowercase : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Dict =['input_ids', 'attention_mask']
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase="__start__", lowerCAmelCase="__end__", lowerCAmelCase="__unk__", lowerCAmelCase="__null__", **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(unk_token=lowerCAmelCase, bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, pad_token=lowerCAmelCase, **lowerCAmelCase )
with open(lowerCAmelCase, encoding='''utf-8''' ) as vocab_handle:
lowerCamelCase_ =json.load(lowerCAmelCase )
lowerCamelCase_ ={v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase, encoding='''utf-8''' ) as merges_handle:
lowerCamelCase_ =merges_handle.read().split('''\n''' )[1:-1]
lowerCamelCase_ =[tuple(merge.split() ) for merge in merges]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={}
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.encoder )
def lowercase__ ( self ):
"""simple docstring"""
return dict(self.encoder, **self.added_tokens_encoder )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
lowerCamelCase_ =re.sub('''([.,!?()])''', R''' \1''', lowerCAmelCase )
lowerCamelCase_ =re.sub('''(\')''', R''' \1 ''', lowerCAmelCase )
lowerCamelCase_ =re.sub(R'''\s{2,}''', ''' ''', lowerCAmelCase )
if "\n" in token:
lowerCamelCase_ =token.replace('''\n''', ''' __newln__''' )
lowerCamelCase_ =token.split(''' ''' )
lowerCamelCase_ =[]
for token in tokens:
if not len(lowerCAmelCase ):
continue
lowerCamelCase_ =token.lower()
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
lowerCamelCase_ =get_pairs(lowerCAmelCase )
if not pairs:
words.append(lowerCAmelCase )
continue
while True:
lowerCamelCase_ =min(lowerCAmelCase, key=lambda lowerCAmelCase : self.bpe_ranks.get(lowerCAmelCase, float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_, lowerCamelCase_ =bigram
lowerCamelCase_ =[]
lowerCamelCase_ =0
while i < len(lowerCAmelCase ):
try:
lowerCamelCase_ =word.index(lowerCAmelCase, lowerCAmelCase )
new_word.extend(word[i:j] )
lowerCamelCase_ =j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =new_word
if len(lowerCAmelCase ) == 1:
break
else:
lowerCamelCase_ =get_pairs(lowerCAmelCase )
lowerCamelCase_ ='''@@ '''.join(lowerCAmelCase )
lowerCamelCase_ =word[:-4]
lowerCamelCase_ =word
words.append(lowerCAmelCase )
return " ".join(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =re.findall(R'''\S+\n?''', lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =token.lower()
return self.encoder.get(lowerCAmelCase, self.encoder.get(self.unk_token ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase, self.unk_token )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''' '''.join(lowerCAmelCase ).replace('''@@ ''', '''''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=lowerCAmelCase, ensure_ascii=lowerCAmelCase ) + '''\n''' )
lowerCamelCase_ =0
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
lowerCamelCase_ =token_index
writer.write(''' '''.join(lowerCAmelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 75 | 1 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def a_ ( __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =[]
for line in lines:
lowerCamelCase_ =re.sub(r'''#.*''' , '''''' , __snake_case ) # remove comments
if line:
filtered_lines.append(__snake_case )
lowerCamelCase_ ='''\n'''.join(__snake_case )
# Make a hash from all this code
lowerCamelCase_ =full_str.encode('''utf-8''' )
return shaaaa(__snake_case ).hexdigest()
# get importable module names and hash for caching
a_ : int = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
a_ : Union[str, Any] = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
a_ : List[str] = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
a_ : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 75 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[str] ='efficientformer'
def __init__( self, lowerCAmelCase = [3, 2, 6, 4], lowerCAmelCase = [48, 96, 224, 448], lowerCAmelCase = [True, True, True, True], lowerCAmelCase = 448, lowerCAmelCase = 32, lowerCAmelCase = 4, lowerCAmelCase = 7, lowerCAmelCase = 5, lowerCAmelCase = 8, lowerCAmelCase = 4, lowerCAmelCase = 0.0, lowerCAmelCase = 16, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 2, lowerCAmelCase = 1, lowerCAmelCase = 0.0, lowerCAmelCase = 1, lowerCAmelCase = True, lowerCAmelCase = True, lowerCAmelCase = 1e-5, lowerCAmelCase = "gelu", lowerCAmelCase = 0.0_2, lowerCAmelCase = 1e-12, lowerCAmelCase = 224, lowerCAmelCase = 1e-05, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =hidden_sizes
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =initializer_range
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =depths
lowerCamelCase_ =mlp_expansion_ratio
lowerCamelCase_ =downsamples
lowerCamelCase_ =dim
lowerCamelCase_ =key_dim
lowerCamelCase_ =attention_ratio
lowerCamelCase_ =resolution
lowerCamelCase_ =pool_size
lowerCamelCase_ =downsample_patch_size
lowerCamelCase_ =downsample_stride
lowerCamelCase_ =downsample_pad
lowerCamelCase_ =drop_path_rate
lowerCamelCase_ =num_metaad_blocks
lowerCamelCase_ =distillation
lowerCamelCase_ =use_layer_scale
lowerCamelCase_ =layer_scale_init_value
lowerCamelCase_ =image_size
lowerCamelCase_ =batch_norm_eps
| 75 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def a_ ( __snake_case : dict , __snake_case : str , __snake_case : set , __snake_case : set , __snake_case : dict , __snake_case : dict , __snake_case : PriorityQueue , __snake_case : dict , __snake_case : float | int , ) -> float | int:
"""simple docstring"""
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
lowerCamelCase_ =cst_fwd.get(__snake_case , np.inf )
lowerCamelCase_ =cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
lowerCamelCase_ =new_cost_f
lowerCamelCase_ =v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
lowerCamelCase_ =cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def a_ ( __snake_case : str , __snake_case : str , __snake_case : dict , __snake_case : dict ) -> int:
"""simple docstring"""
lowerCamelCase_ =-1
lowerCamelCase_ =set()
lowerCamelCase_ =set()
lowerCamelCase_ ={source: 0}
lowerCamelCase_ ={destination: 0}
lowerCamelCase_ ={source: None}
lowerCamelCase_ ={destination: None}
lowerCamelCase_ =PriorityQueue()
lowerCamelCase_ =PriorityQueue()
lowerCamelCase_ =np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
lowerCamelCase_, lowerCamelCase_ =queue_forward.get()
visited_forward.add(__snake_case )
lowerCamelCase_, lowerCamelCase_ =queue_backward.get()
visited_backward.add(__snake_case )
lowerCamelCase_ =pass_and_relaxation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
lowerCamelCase_ =pass_and_relaxation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
lowerCamelCase_ =shortest_distance
return shortest_path_distance
a_ : Optional[int] = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
a_ : List[Any] = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
a_ : Union[str, Any] = random.Random()
def a_ ( __snake_case : int , __snake_case : int=1.0 , __snake_case : Tuple=None , __snake_case : Union[str, Any]=None ) -> str:
"""simple docstring"""
if rng is None:
lowerCamelCase_ =global_rng
lowerCamelCase_ =[]
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self, lowerCAmelCase, lowerCAmelCase=7, lowerCAmelCase=400, lowerCAmelCase=2_000, lowerCAmelCase=24, lowerCAmelCase=24, lowerCAmelCase=0.0, lowerCAmelCase=16_000, lowerCAmelCase=True, lowerCAmelCase=True, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =min_seq_length
lowerCamelCase_ =max_seq_length
lowerCamelCase_ =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase_ =feature_size
lowerCamelCase_ =num_mel_bins
lowerCamelCase_ =padding_value
lowerCamelCase_ =sampling_rate
lowerCamelCase_ =return_attention_mask
lowerCamelCase_ =do_normalize
def lowercase__ ( self ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowercase__ ( self, lowerCAmelCase=False, lowerCAmelCase=False ):
"""simple docstring"""
def _flatten(lowerCAmelCase ):
return list(itertools.chain(*lowerCAmelCase ) )
if equal_length:
lowerCamelCase_ =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCamelCase_ =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Any =SpeechaTextFeatureExtractor if is_speech_available() else None
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextFeatureExtractionTester(self )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
self.assertTrue(np.all(np.mean(lowerCAmelCase, axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase, axis=0 ) - 1 ) < 1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]
# Test feature size
lowerCamelCase_ =feature_extractor(lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
lowerCamelCase_ =feature_extractor(speech_inputs[0], return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(np_speech_inputs[0], return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test batched
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase_ =[floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCamelCase_ =np.asarray(lowerCAmelCase )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding=lowerCAmelCase, max_length=lowerCAmelCase, return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, max_length=lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''max_length''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24) )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=16, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24) )
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =np.random.rand(100, 32 ).astype(np.floataa )
lowerCamelCase_ =np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
from datasets import load_dataset
lowerCamelCase_ =load_dataset('''hf-internal-testing/librispeech_asr_dummy''', '''clean''', split='''validation''' )
# automatic decoding with librispeech
lowerCamelCase_ =ds.sort('''id''' ).select(range(lowerCAmelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =np.array([
-1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
-1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
-1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
] )
# fmt: on
lowerCamelCase_ =self._load_datasamples(1 )
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape, (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30], lowerCAmelCase, atol=1e-4 ) )
| 75 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
a_ : Tuple = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
requires_backends(self, '''vision''' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return super().__call__(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ={}
if "candidate_labels" in kwargs:
lowerCamelCase_ =kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
lowerCamelCase_ =kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase="This is a photo of {}." ):
"""simple docstring"""
lowerCamelCase_ =load_image(lowerCAmelCase )
lowerCamelCase_ =self.image_processor(images=[image], return_tensors=self.framework )
lowerCamelCase_ =candidate_labels
lowerCamelCase_ =[hypothesis_template.format(lowerCAmelCase ) for x in candidate_labels]
lowerCamelCase_ =self.tokenizer(lowerCAmelCase, return_tensors=self.framework, padding=lowerCAmelCase )
lowerCamelCase_ =[text_inputs]
return inputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_inputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0], lowerCAmelCase ):
lowerCamelCase_ =text_inputs[0]
else:
# Batching case.
lowerCamelCase_ =text_inputs[0][0]
lowerCamelCase_ =self.model(**lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ ={
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_outputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_outputs['''logits'''][0]
if self.framework == "pt":
lowerCamelCase_ =logits.softmax(dim=-1 ).squeeze(-1 )
lowerCamelCase_ =probs.tolist()
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =[scores]
elif self.framework == "tf":
lowerCamelCase_ =stable_softmax(lowerCAmelCase, axis=-1 )
lowerCamelCase_ =probs.numpy().tolist()
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowerCamelCase_ =[
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(lowerCAmelCase, lowerCAmelCase ), key=lambda lowerCAmelCase : -x[0] )
]
return result
| 75 |
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in table:
res += inp[i - 1]
return res
def a_ ( __snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
return data[1:] + data[0]
def a_ ( __snake_case : str , __snake_case : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def a_ ( __snake_case : Optional[Any] , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =int('''0b''' + data[0] + data[-1] , 2 )
lowerCamelCase_ =int('''0b''' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def a_ ( __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : int , __snake_case : Tuple , __snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =message[:4]
lowerCamelCase_ =message[4:]
lowerCamelCase_ =apply_table(__snake_case , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
lowerCamelCase_ =apply_sbox(__snake_case , temp[:4] ) # noqa: E741
lowerCamelCase_ =apply_sbox(__snake_case , temp[4:] )
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + l # noqa: E741
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + r
lowerCamelCase_ =apply_table(l + r , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
a_ : Any = input("""Enter 10 bit key: """)
a_ : Any = input("""Enter 8 bit message: """)
a_ : str = [6, 3, 7, 4, 8, 5, 10, 9]
a_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
a_ : str = [2, 4, 3, 1]
a_ : Optional[int] = [2, 6, 3, 1, 4, 8, 5, 7]
a_ : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
a_ : Union[str, Any] = [4, 1, 2, 3, 2, 3, 4, 1]
a_ : int = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
a_ : Any = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
a_ : List[Any] = apply_table(key, paa_table)
a_ : str = temp[:5]
a_ : Optional[Any] = temp[5:]
a_ : Tuple = left_shift(left)
a_ : Optional[Any] = left_shift(right)
a_ : str = apply_table(left + right, pa_table)
a_ : Optional[Any] = left_shift(left)
a_ : Tuple = left_shift(right)
a_ : Union[str, Any] = left_shift(left)
a_ : List[str] = left_shift(right)
a_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
a_ : Optional[int] = apply_table(message, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : str = temp[4:] + temp[:4]
a_ : List[str] = function(expansion, sa, sa, keya, temp)
a_ : Union[str, Any] = apply_table(temp, IP_inv)
print("""Cipher text is:""", CT)
# decryption
a_ : Optional[int] = apply_table(CT, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : int = temp[4:] + temp[:4]
a_ : int = function(expansion, sa, sa, keya, temp)
a_ : Optional[int] = apply_table(temp, IP_inv)
print("""Plain text after decypting is:""", PT)
| 75 | 1 |
'''simple docstring'''
def a_ ( __snake_case : int = 6008_5147_5143 ) -> int:
"""simple docstring"""
try:
lowerCamelCase_ =int(__snake_case )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowerCamelCase_ =2
lowerCamelCase_ =0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowerCamelCase_ =i
while n % i == 0:
lowerCamelCase_ =n // i
i += 1
return int(__snake_case )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 75 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
a_ : List[Any] = logging.get_logger(__name__)
a_ : Tuple = OrderedDict(
[
("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
("""beit""", """BeitFeatureExtractor"""),
("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
("""clap""", """ClapFeatureExtractor"""),
("""clip""", """CLIPFeatureExtractor"""),
("""clipseg""", """ViTFeatureExtractor"""),
("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
("""convnext""", """ConvNextFeatureExtractor"""),
("""cvt""", """ConvNextFeatureExtractor"""),
("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
("""data2vec-vision""", """BeitFeatureExtractor"""),
("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
("""deit""", """DeiTFeatureExtractor"""),
("""detr""", """DetrFeatureExtractor"""),
("""dinat""", """ViTFeatureExtractor"""),
("""donut-swin""", """DonutFeatureExtractor"""),
("""dpt""", """DPTFeatureExtractor"""),
("""encodec""", """EncodecFeatureExtractor"""),
("""flava""", """FlavaFeatureExtractor"""),
("""glpn""", """GLPNFeatureExtractor"""),
("""groupvit""", """CLIPFeatureExtractor"""),
("""hubert""", """Wav2Vec2FeatureExtractor"""),
("""imagegpt""", """ImageGPTFeatureExtractor"""),
("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
("""levit""", """LevitFeatureExtractor"""),
("""maskformer""", """MaskFormerFeatureExtractor"""),
("""mctct""", """MCTCTFeatureExtractor"""),
("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
("""mobilevit""", """MobileViTFeatureExtractor"""),
("""nat""", """ViTFeatureExtractor"""),
("""owlvit""", """OwlViTFeatureExtractor"""),
("""perceiver""", """PerceiverFeatureExtractor"""),
("""poolformer""", """PoolFormerFeatureExtractor"""),
("""regnet""", """ConvNextFeatureExtractor"""),
("""resnet""", """ConvNextFeatureExtractor"""),
("""segformer""", """SegformerFeatureExtractor"""),
("""sew""", """Wav2Vec2FeatureExtractor"""),
("""sew-d""", """Wav2Vec2FeatureExtractor"""),
("""speech_to_text""", """Speech2TextFeatureExtractor"""),
("""speecht5""", """SpeechT5FeatureExtractor"""),
("""swiftformer""", """ViTFeatureExtractor"""),
("""swin""", """ViTFeatureExtractor"""),
("""swinv2""", """ViTFeatureExtractor"""),
("""table-transformer""", """DetrFeatureExtractor"""),
("""timesformer""", """VideoMAEFeatureExtractor"""),
("""tvlt""", """TvltFeatureExtractor"""),
("""unispeech""", """Wav2Vec2FeatureExtractor"""),
("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
("""van""", """ConvNextFeatureExtractor"""),
("""videomae""", """VideoMAEFeatureExtractor"""),
("""vilt""", """ViltFeatureExtractor"""),
("""vit""", """ViTFeatureExtractor"""),
("""vit_mae""", """ViTFeatureExtractor"""),
("""vit_msn""", """ViTFeatureExtractor"""),
("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
("""wavlm""", """Wav2Vec2FeatureExtractor"""),
("""whisper""", """WhisperFeatureExtractor"""),
("""xclip""", """CLIPFeatureExtractor"""),
("""yolos""", """YolosFeatureExtractor"""),
]
)
a_ : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def a_ ( __snake_case : str ) -> Any:
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCamelCase_ =model_type_to_module_name(__snake_case )
lowerCamelCase_ =importlib.import_module(F'''.{module_name}''' , '''transformers.models''' )
try:
return getattr(__snake_case , __snake_case )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(__snake_case , '''__name__''' , __snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCamelCase_ =importlib.import_module('''transformers''' )
if hasattr(__snake_case , __snake_case ):
return getattr(__snake_case , __snake_case )
return None
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =get_file_from_repo(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(__snake_case , encoding='''utf-8''' ) as reader:
return json.load(__snake_case )
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(lowerCAmelCase )
def lowercase__ ( cls, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =kwargs.pop('''config''', lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''trust_remote_code''', lowerCAmelCase )
lowerCamelCase_ =True
lowerCamelCase_, lowerCamelCase_ =FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =config_dict.get('''feature_extractor_type''', lowerCAmelCase )
lowerCamelCase_ =None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''', {} ):
lowerCamelCase_ =config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =AutoConfig.from_pretrained(lowerCAmelCase, **lowerCAmelCase )
# It could be in `config.feature_extractor_type``
lowerCamelCase_ =getattr(lowerCAmelCase, '''feature_extractor_type''', lowerCAmelCase )
if hasattr(lowerCAmelCase, '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
lowerCamelCase_ =config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
lowerCamelCase_ =feature_extractor_class_from_name(lowerCAmelCase )
lowerCamelCase_ =feature_extractor_auto_map is not None
lowerCamelCase_ =feature_extractor_class is not None or type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
lowerCamelCase_ =resolve_trust_remote_code(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
if has_remote_code and trust_remote_code:
lowerCamelCase_ =get_class_from_dynamic_module(
lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''code_revision''', lowerCAmelCase )
if os.path.isdir(lowerCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
lowerCamelCase_ =FEATURE_EXTRACTOR_MAPPING[type(lowerCAmelCase )]
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
raise ValueError(
f'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
f'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase, lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
lowercase : Tuple ='pixel_values'
lowercase : Any =False
lowercase : int =TimmBackboneConfig
def __init__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(self, '''timm''' )
super().__init__(lowerCAmelCase )
lowerCamelCase_ =config
if config.backbone is None:
raise ValueError('''backbone is not set in the config. Please set it to a timm model name.''' )
if config.backbone not in timm.list_models():
raise ValueError(f'''backbone {config.backbone} is not supported by timm.''' )
if hasattr(lowerCAmelCase, '''out_features''' ) and config.out_features is not None:
raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.''' )
lowerCamelCase_ =getattr(lowerCAmelCase, '''use_pretrained_backbone''', lowerCAmelCase )
if pretrained is None:
raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.''' )
# We just take the final layer by default. This matches the default for the transformers models.
lowerCamelCase_ =config.out_indices if getattr(lowerCAmelCase, '''out_indices''', lowerCAmelCase ) is not None else (-1,)
lowerCamelCase_ =timm.create_model(
config.backbone, pretrained=lowerCAmelCase, features_only=config.features_only, in_chans=config.num_channels, out_indices=lowerCAmelCase, **lowerCAmelCase, )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
lowerCamelCase_ =self._backbone.return_layers
lowerCamelCase_ ={layer['''module''']: str(lowerCAmelCase ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(lowerCAmelCase )
@classmethod
def lowercase__ ( cls, lowerCAmelCase, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls, ['''vision''', '''timm'''] )
from ...models.timm_backbone import TimmBackboneConfig
lowerCamelCase_ =kwargs.pop('''config''', TimmBackboneConfig() )
lowerCamelCase_ =kwargs.pop('''use_timm_backbone''', lowerCAmelCase )
if not use_timm:
raise ValueError('''use_timm_backbone must be True for timm backbones''' )
lowerCamelCase_ =kwargs.pop('''num_channels''', config.num_channels )
lowerCamelCase_ =kwargs.pop('''features_only''', config.features_only )
lowerCamelCase_ =kwargs.pop('''use_pretrained_backbone''', config.use_pretrained_backbone )
lowerCamelCase_ =kwargs.pop('''out_indices''', config.out_indices )
lowerCamelCase_ =TimmBackboneConfig(
backbone=lowerCAmelCase, num_channels=lowerCAmelCase, features_only=lowerCAmelCase, use_pretrained_backbone=lowerCAmelCase, out_indices=lowerCAmelCase, )
return super()._from_config(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
pass
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase_ =output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError('''Cannot output attentions for timm backbones at the moment''' )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
lowerCamelCase_ =self._all_layers
lowerCamelCase_ =self._backbone(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =self._return_layers
lowerCamelCase_ =tuple(hidden_states[i] for i in self.out_indices )
else:
lowerCamelCase_ =self._backbone(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =None
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =tuple(lowerCAmelCase ) if hidden_states is not None else None
if not return_dict:
lowerCamelCase_ =(feature_maps,)
if output_hidden_states:
lowerCamelCase_ =output + (hidden_states,)
return output
return BackboneOutput(feature_maps=lowerCAmelCase, hidden_states=lowerCAmelCase, attentions=lowerCAmelCase )
| 75 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ : Optional[int] = logging.getLogger(__name__)
def a_ ( __snake_case : Union[str, Any] , __snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ =np.argmax(__snake_case , axis=1 )
return np.sum(outputs == labels )
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
with open(__snake_case , encoding='''utf_8''' ) as f:
lowerCamelCase_ =csv.reader(__snake_case )
lowerCamelCase_ =[]
next(__snake_case ) # skip the first line
for line in tqdm(__snake_case ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a_ ( __snake_case : str , __snake_case : Dict , __snake_case : List[str] , __snake_case : List[str] , __snake_case : List[Any] , __snake_case : Dict ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
for dataset in encoded_datasets:
lowerCamelCase_ =len(__snake_case )
lowerCamelCase_ =np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch, 2) , dtype=np.intaa )
lowerCamelCase_ =np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__snake_case ):
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =mc_label
lowerCamelCase_ =(input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__snake_case ) for t in all_inputs ) )
return tensor_datasets
def a_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=__snake_case , default='''openai-gpt''' , help='''pretrained model name''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' , action='''store_true''' , help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' , default=__snake_case , type=__snake_case , required=__snake_case , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument('''--train_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--eval_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--seed''' , type=__snake_case , default=42 )
parser.add_argument('''--num_train_epochs''' , type=__snake_case , default=3 )
parser.add_argument('''--train_batch_size''' , type=__snake_case , default=8 )
parser.add_argument('''--eval_batch_size''' , type=__snake_case , default=16 )
parser.add_argument('''--adam_epsilon''' , default=1e-8 , type=__snake_case , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' , type=__snake_case , default=1 )
parser.add_argument(
'''--max_steps''' , default=-1 , type=__snake_case , help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) , )
parser.add_argument(
'''--gradient_accumulation_steps''' , type=__snake_case , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--learning_rate''' , type=__snake_case , default=6.25e-5 )
parser.add_argument('''--warmup_steps''' , default=0 , type=__snake_case , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' , type=__snake_case , default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' , type=__snake_case , default=0.0_1 )
parser.add_argument('''--lm_coef''' , type=__snake_case , default=0.9 )
parser.add_argument('''--n_valid''' , type=__snake_case , default=374 )
parser.add_argument('''--server_ip''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
lowerCamelCase_ =parser.parse_args()
print(__snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowerCamelCase_ =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
lowerCamelCase_ =torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(__snake_case , __snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowerCamelCase_ =['''_start_''', '''_delimiter_''', '''_classify_''']
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(__snake_case )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(__snake_case )
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(__snake_case ) )
model.to(__snake_case )
# Load and encode the datasets
def tokenize_and_encode(__snake_case : Union[str, Any] ):
if isinstance(__snake_case , __snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__snake_case ) )
elif isinstance(__snake_case , __snake_case ):
return obj
return [tokenize_and_encode(__snake_case ) for o in obj]
logger.info('''Encoding dataset...''' )
lowerCamelCase_ =load_rocstories_dataset(args.train_dataset )
lowerCamelCase_ =load_rocstories_dataset(args.eval_dataset )
lowerCamelCase_ =(train_dataset, eval_dataset)
lowerCamelCase_ =tokenize_and_encode(__snake_case )
# Compute the max input length for the Transformer
lowerCamelCase_ =model.config.n_positions // 2 - 2
lowerCamelCase_ =max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowerCamelCase_ =min(__snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowerCamelCase_ =pre_process_datasets(__snake_case , __snake_case , __snake_case , *__snake_case )
lowerCamelCase_, lowerCamelCase_ =tensor_datasets[0], tensor_datasets[1]
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =RandomSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.train_batch_size )
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =SequentialSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowerCamelCase_ =args.max_steps
lowerCamelCase_ =args.max_steps // (len(__snake_case ) // args.gradient_accumulation_steps) + 1
else:
lowerCamelCase_ =len(__snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
lowerCamelCase_ =list(model.named_parameters() )
lowerCamelCase_ =['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
lowerCamelCase_ =[
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
lowerCamelCase_ =AdamW(__snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
lowerCamelCase_ =get_linear_schedule_with_warmup(
__snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=__snake_case )
if args.do_train:
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='''Epoch''' ):
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =tqdm(__snake_case , desc='''Training''' )
for step, batch in enumerate(__snake_case ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
lowerCamelCase_ =model(__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowerCamelCase_ =(
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowerCamelCase_ ='''Training loss: {:.2e} lr: {:.2e}'''.format(__snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowerCamelCase_ =model.module if hasattr(__snake_case , '''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
torch.save(model_to_save.state_dict() , __snake_case )
model_to_save.config.to_json_file(__snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(__snake_case )
if args.do_eval:
model.eval()
lowerCamelCase_, lowerCamelCase_ =0, 0
lowerCamelCase_, lowerCamelCase_ =0, 0
for batch in tqdm(__snake_case , desc='''Evaluating''' ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
with torch.no_grad():
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =model(
__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =mc_logits.detach().cpu().numpy()
lowerCamelCase_ =mc_labels.to('''cpu''' ).numpy()
lowerCamelCase_ =accuracy(__snake_case , __snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowerCamelCase_ =eval_loss / nb_eval_steps
lowerCamelCase_ =eval_accuracy / nb_eval_examples
lowerCamelCase_ =tr_loss / nb_tr_steps if args.do_train else None
lowerCamelCase_ ={'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
lowerCamelCase_ =os.path.join(args.output_dir , '''eval_results.txt''' )
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' , __snake_case , str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 75 | 1 |
'''simple docstring'''
def a_ ( __snake_case : int ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(__snake_case , __snake_case ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(__snake_case ).count('''1''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ =''''''
lowerCamelCase_ =[]
lowerCamelCase_ =0
lowerCamelCase_ =256
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =cva.imread(lowerCAmelCase, 0 )
lowerCamelCase_ =copy.deepcopy(self.img )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =plt.hist(self.img.ravel(), 256, [0, 256], label='''x''' )
lowerCamelCase_ =np.sum(lowerCAmelCase )
for i in range(len(lowerCAmelCase ) ):
lowerCamelCase_ =x[i] / self.k
self.sk += prk
lowerCamelCase_ =(self.L - 1) * self.sk
if self.rem != 0:
lowerCamelCase_ =int(last % last )
lowerCamelCase_ =int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowerCAmelCase )
lowerCamelCase_ =int(np.ma.count(self.img ) / self.img[1].size )
lowerCamelCase_ =self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCamelCase_ =self.img[j][i]
if num != self.last_list[num]:
lowerCamelCase_ =self.last_list[num]
cva.imwrite('''output_data/output.jpg''', self.img )
def lowercase__ ( self ):
"""simple docstring"""
plt.hist(self.img.ravel(), 256, [0, 256] )
def lowercase__ ( self ):
"""simple docstring"""
cva.imshow('''Output-Image''', self.img )
cva.imshow('''Input-Image''', self.original_image )
cva.waitKey(5_000 )
cva.destroyAllWindows()
if __name__ == "__main__":
a_ : str = os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
a_ : Optional[Any] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 75 | 1 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def a_ ( __snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =VideoMAEConfig()
set_architecture_configs(__snake_case , __snake_case )
if "finetuned" not in model_name:
lowerCamelCase_ =False
if "finetuned" in model_name:
lowerCamelCase_ ='''huggingface/label-files'''
if "kinetics" in model_name:
lowerCamelCase_ =400
lowerCamelCase_ ='''kinetics400-id2label.json'''
elif "ssv2" in model_name:
lowerCamelCase_ =174
lowerCamelCase_ ='''something-something-v2-id2label.json'''
else:
raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.''' )
lowerCamelCase_ =json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type='''dataset''' ) , '''r''' ) )
lowerCamelCase_ ={int(__snake_case ): v for k, v in idalabel.items()}
lowerCamelCase_ =idalabel
lowerCamelCase_ ={v: k for k, v in idalabel.items()}
return config
def a_ ( __snake_case : Optional[int] , __snake_case : Optional[int] ) -> Any:
"""simple docstring"""
if "small" in model_name:
lowerCamelCase_ =384
lowerCamelCase_ =1536
lowerCamelCase_ =12
lowerCamelCase_ =16
lowerCamelCase_ =12
lowerCamelCase_ =3
lowerCamelCase_ =192
lowerCamelCase_ =768
elif "large" in model_name:
lowerCamelCase_ =1024
lowerCamelCase_ =4096
lowerCamelCase_ =24
lowerCamelCase_ =16
lowerCamelCase_ =12
lowerCamelCase_ =8
lowerCamelCase_ =512
lowerCamelCase_ =2048
elif "huge" in model_name:
lowerCamelCase_ =1280
lowerCamelCase_ =5120
lowerCamelCase_ =32
lowerCamelCase_ =16
lowerCamelCase_ =12
lowerCamelCase_ =8
lowerCamelCase_ =640
lowerCamelCase_ =2560
elif "base" not in model_name:
raise ValueError('''Model name should include either "small", "base", "large", or "huge"''' )
def a_ ( __snake_case : Tuple ) -> Optional[Any]:
"""simple docstring"""
if "encoder." in name:
lowerCamelCase_ =name.replace('''encoder.''' , '''''' )
if "cls_token" in name:
lowerCamelCase_ =name.replace('''cls_token''' , '''videomae.embeddings.cls_token''' )
if "decoder_pos_embed" in name:
lowerCamelCase_ =name.replace('''decoder_pos_embed''' , '''decoder.decoder_pos_embed''' )
if "pos_embed" in name and "decoder" not in name:
lowerCamelCase_ =name.replace('''pos_embed''' , '''videomae.embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
lowerCamelCase_ =name.replace('''patch_embed.proj''' , '''videomae.embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowerCamelCase_ =name.replace('''patch_embed.norm''' , '''videomae.embeddings.norm''' )
if "decoder.blocks" in name:
lowerCamelCase_ =name.replace('''decoder.blocks''' , '''decoder.decoder_layers''' )
if "blocks" in name:
lowerCamelCase_ =name.replace('''blocks''' , '''videomae.encoder.layer''' )
if "attn.proj" in name:
lowerCamelCase_ =name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name and "bias" not in name:
lowerCamelCase_ =name.replace('''attn''' , '''attention.self''' )
if "attn" in name:
lowerCamelCase_ =name.replace('''attn''' , '''attention.attention''' )
if "norm1" in name:
lowerCamelCase_ =name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
lowerCamelCase_ =name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
lowerCamelCase_ =name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowerCamelCase_ =name.replace('''mlp.fc2''' , '''output.dense''' )
if "decoder_embed" in name:
lowerCamelCase_ =name.replace('''decoder_embed''' , '''decoder.decoder_embed''' )
if "decoder_norm" in name:
lowerCamelCase_ =name.replace('''decoder_norm''' , '''decoder.decoder_norm''' )
if "decoder_pred" in name:
lowerCamelCase_ =name.replace('''decoder_pred''' , '''decoder.decoder_pred''' )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
lowerCamelCase_ =name.replace('''norm.weight''' , '''videomae.layernorm.weight''' )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
lowerCamelCase_ =name.replace('''norm.bias''' , '''videomae.layernorm.bias''' )
if "head" in name and "decoder" not in name:
lowerCamelCase_ =name.replace('''head''' , '''classifier''' )
return name
def a_ ( __snake_case : List[Any] , __snake_case : str ) -> List[str]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCamelCase_ =orig_state_dict.pop(__snake_case )
if key.startswith('''encoder.''' ):
lowerCamelCase_ =key.replace('''encoder.''' , '''''' )
if "qkv" in key:
lowerCamelCase_ =key.split('''.''' )
if key.startswith('''decoder.blocks''' ):
lowerCamelCase_ =config.decoder_hidden_size
lowerCamelCase_ =int(key_split[2] )
lowerCamelCase_ ='''decoder.decoder_layers.'''
if "weight" in key:
lowerCamelCase_ =val[:dim, :]
lowerCamelCase_ =val[dim : dim * 2, :]
lowerCamelCase_ =val[-dim:, :]
else:
lowerCamelCase_ =config.hidden_size
lowerCamelCase_ =int(key_split[1] )
lowerCamelCase_ ='''videomae.encoder.layer.'''
if "weight" in key:
lowerCamelCase_ =val[:dim, :]
lowerCamelCase_ =val[dim : dim * 2, :]
lowerCamelCase_ =val[-dim:, :]
else:
lowerCamelCase_ =val
return orig_state_dict
def a_ ( ) -> int:
"""simple docstring"""
lowerCamelCase_ =hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
lowerCamelCase_ =np.load(__snake_case )
return list(__snake_case )
def a_ ( __snake_case : List[Any] , __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =get_videomae_config(__snake_case )
if "finetuned" in model_name:
lowerCamelCase_ =VideoMAEForVideoClassification(__snake_case )
else:
lowerCamelCase_ =VideoMAEForPreTraining(__snake_case )
# download original checkpoint, hosted on Google Drive
lowerCamelCase_ ='''pytorch_model.bin'''
gdown.cached_download(__snake_case , __snake_case , quiet=__snake_case )
lowerCamelCase_ =torch.load(__snake_case , map_location='''cpu''' )
if "model" in files:
lowerCamelCase_ =files['''model''']
else:
lowerCamelCase_ =files['''module''']
lowerCamelCase_ =convert_state_dict(__snake_case , __snake_case )
model.load_state_dict(__snake_case )
model.eval()
# verify model on basic input
lowerCamelCase_ =VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
lowerCamelCase_ =prepare_video()
lowerCamelCase_ =image_processor(__snake_case , return_tensors='''pt''' )
if "finetuned" not in model_name:
lowerCamelCase_ =hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' , filename='''bool_masked_pos.pt''' )
lowerCamelCase_ =torch.load(__snake_case )
lowerCamelCase_ =model(**__snake_case )
lowerCamelCase_ =outputs.logits
lowerCamelCase_ =[
'''videomae-small-finetuned-kinetics''',
'''videomae-small-finetuned-ssv2''',
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
'''videomae-base-short''',
'''videomae-base-short-finetuned-kinetics''',
'''videomae-base''',
'''videomae-base-finetuned-kinetics''',
'''videomae-large''',
'''videomae-large-finetuned-kinetics''',
'''videomae-huge-finetuned-kinetics''',
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
'''videomae-base-short-ssv2''',
'''videomae-base-short-finetuned-ssv2''',
'''videomae-base-ssv2''',
'''videomae-base-finetuned-ssv2''',
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
lowerCamelCase_ =torch.Size([1, 400] )
lowerCamelCase_ =torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7] )
elif model_name == "videomae-small-finetuned-ssv2":
lowerCamelCase_ =torch.Size([1, 174] )
lowerCamelCase_ =torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5] )
elif model_name == "videomae-base":
lowerCamelCase_ =torch.Size([1, 1408, 1536] )
lowerCamelCase_ =torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]] )
elif model_name == "videomae-base-short":
lowerCamelCase_ =torch.Size([1, 1408, 1536] )
lowerCamelCase_ =torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]] )
# we verified the loss both for normalized and unnormalized targets for this one
lowerCamelCase_ =torch.tensor([0.5_1_4_2] ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9] )
elif model_name == "videomae-large":
lowerCamelCase_ =torch.Size([1, 1408, 1536] )
lowerCamelCase_ =torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]] )
elif model_name == "videomae-large-finetuned-kinetics":
lowerCamelCase_ =torch.Size([1, 400] )
lowerCamelCase_ =torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5] )
elif model_name == "videomae-huge-finetuned-kinetics":
lowerCamelCase_ =torch.Size([1, 400] )
lowerCamelCase_ =torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4] )
elif model_name == "videomae-base-short-finetuned-kinetics":
lowerCamelCase_ =torch.Size([1, 400] )
lowerCamelCase_ =torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3] )
elif model_name == "videomae-base-finetuned-kinetics":
lowerCamelCase_ =torch.Size([1, 400] )
lowerCamelCase_ =torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1] )
elif model_name == "videomae-base-short-ssv2":
lowerCamelCase_ =torch.Size([1, 1408, 1536] )
lowerCamelCase_ =torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
lowerCamelCase_ =torch.Size([1, 174] )
lowerCamelCase_ =torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6] )
elif model_name == "videomae-base-ssv2":
lowerCamelCase_ =torch.Size([1, 1408, 1536] )
lowerCamelCase_ =torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]] )
elif model_name == "videomae-base-finetuned-ssv2":
lowerCamelCase_ =torch.Size([1, 174] )
lowerCamelCase_ =torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9] )
else:
raise ValueError(F'''Model name not supported. Should be one of {model_names}''' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , __snake_case , atol=1e-4 )
else:
print('''Logits:''' , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , __snake_case , atol=1e-4 )
print('''Logits ok!''' )
# verify loss, if applicable
if model_name == "videomae-base-short":
lowerCamelCase_ =outputs.loss
assert torch.allclose(__snake_case , __snake_case , atol=1e-4 )
print('''Loss ok!''' )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__snake_case )
model.save_pretrained(__snake_case )
if push_to_hub:
print('''Pushing to the hub...''' )
model.push_to_hub(__snake_case , organization='''nielsr''' )
if __name__ == "__main__":
a_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
a_ : List[Any] = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 75 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Any = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
# No specific FOR_XXX available yet
def __call__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return super().__call__(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ={}
if "candidate_labels" in kwargs:
lowerCamelCase_ =kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
lowerCamelCase_ =kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(lowerCAmelCase, lowerCAmelCase ):
if audio.startswith('''http://''' ) or audio.startswith('''https://''' ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
lowerCamelCase_ =requests.get(lowerCAmelCase ).content
else:
with open(lowerCAmelCase, '''rb''' ) as f:
lowerCamelCase_ =f.read()
if isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =ffmpeg_read(lowerCAmelCase, self.feature_extractor.sampling_rate )
if not isinstance(lowerCAmelCase, np.ndarray ):
raise ValueError('''We expect a numpy ndarray as input''' )
if len(audio.shape ) != 1:
raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''' )
lowerCamelCase_ =self.feature_extractor(
[audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='''pt''' )
lowerCamelCase_ =candidate_labels
lowerCamelCase_ =[hypothesis_template.format(lowerCAmelCase ) for x in candidate_labels]
lowerCamelCase_ =self.tokenizer(lowerCAmelCase, return_tensors=self.framework, padding=lowerCAmelCase )
lowerCamelCase_ =[text_inputs]
return inputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_inputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0], lowerCAmelCase ):
lowerCamelCase_ =text_inputs[0]
else:
# Batching case.
lowerCamelCase_ =text_inputs[0][0]
lowerCamelCase_ =self.model(**lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ ={
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_audio,
}
return model_outputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_outputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_outputs['''logits'''][0]
if self.framework == "pt":
lowerCamelCase_ =logits.softmax(dim=0 )
lowerCamelCase_ =probs.tolist()
else:
raise ValueError('''`tf` framework not supported.''' )
lowerCamelCase_ =[
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(lowerCAmelCase, lowerCAmelCase ), key=lambda lowerCAmelCase : -x[0] )
]
return result
| 75 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =path_or_paths
lowerCamelCase_ =split if split or isinstance(lowerCAmelCase, lowerCAmelCase ) else '''train'''
lowerCamelCase_ =features
lowerCamelCase_ =cache_dir
lowerCamelCase_ =keep_in_memory
lowerCamelCase_ =streaming
lowerCamelCase_ =num_proc
lowerCamelCase_ =kwargs
@abstractmethod
def lowercase__ ( self ):
"""simple docstring"""
pass
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =features
lowerCamelCase_ =cache_dir
lowerCamelCase_ =keep_in_memory
lowerCamelCase_ =streaming
lowerCamelCase_ =num_proc
lowerCamelCase_ =kwargs
@abstractmethod
def lowercase__ ( self ):
"""simple docstring"""
pass
| 75 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
a_ : Optional[Any] = 5
a_ : str = 10
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : int =SpeechaTextTokenizer
lowercase : int =False
lowercase : List[str] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =sp.SentencePieceProcessor()
spm_model.Load(lowerCAmelCase )
lowerCamelCase_ =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowerCAmelCase ) )]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =Path(self.tmpdirname )
save_json(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''<pad>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<s>''' )
self.assertEqual(vocab_keys[1], '''<pad>''' )
self.assertEqual(vocab_keys[-1], '''j''' )
self.assertEqual(len(lowerCAmelCase ), 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [289, 50, 14, 174, 386], )
lowerCamelCase_ =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.'''], )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ =tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.'''], )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase, model_name='''facebook/s2t-small-mustc-en-de-st''', revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''', )
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Tuple ='valhalla/s2t_mustc_multilinguial_medium'
lowercase : Dict ='C\'est trop cool'
lowercase : str ='Esto es genial'
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''], 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''], 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''], 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''], 11 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 10_000 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertIn(lowerCAmelCase, self.tokenizer.all_special_ids )
lowerCamelCase_ =[ES_CODE, 4, 1_601, 47, 7_647, 2]
lowerCamelCase_ =self.tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
lowerCamelCase_ =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0], lowerCAmelCase )
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE] )
lowerCamelCase_ ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE] )
| 75 | 1 |
'''simple docstring'''
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a_ ( __snake_case : Dataset , __snake_case : Dict[str, str] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =args.log_outputs
lowerCamelCase_ ='''_'''.join(args.dataset.split('''/''' ) + [args.config, args.split] )
# load metric
lowerCamelCase_ =load_metric('''wer''' )
lowerCamelCase_ =load_metric('''cer''' )
# compute metrics
lowerCamelCase_ =wer.compute(references=result['''target'''] , predictions=result['''prediction'''] )
lowerCamelCase_ =cer.compute(references=result['''target'''] , predictions=result['''prediction'''] )
# print & log results
lowerCamelCase_ =F'''WER: {wer_result}\nCER: {cer_result}'''
print(__snake_case )
with open(F'''{dataset_id}_eval_results.txt''' , '''w''' ) as f:
f.write(__snake_case )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
lowerCamelCase_ =F'''log_{dataset_id}_predictions.txt'''
lowerCamelCase_ =F'''log_{dataset_id}_targets.txt'''
with open(__snake_case , '''w''' ) as p, open(__snake_case , '''w''' ) as t:
# mapping function to write output
def write_to_file(__snake_case : List[Any] , __snake_case : Optional[int] ):
p.write(F'''{i}''' + '''\n''' )
p.write(batch['''prediction'''] + '''\n''' )
t.write(F'''{i}''' + '''\n''' )
t.write(batch['''target'''] + '''\n''' )
result.map(__snake_case , with_indices=__snake_case )
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
lowerCamelCase_ ='''[,?.!\-\;\:"“%‘”�—’…–]''' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
lowerCamelCase_ =re.sub(__snake_case , '''''' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
lowerCamelCase_ =['''\n\n''', '''\n''', ''' ''', ''' ''']
for t in token_sequences_to_ignore:
lowerCamelCase_ =''' '''.join(text.split(__snake_case ) )
return text
def a_ ( __snake_case : Any ) -> List[str]:
"""simple docstring"""
# load dataset
lowerCamelCase_ =load_dataset(args.dataset , args.config , split=args.split , use_auth_token=__snake_case )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
lowerCamelCase_ =AutoFeatureExtractor.from_pretrained(args.model_id )
lowerCamelCase_ =feature_extractor.sampling_rate
# resample audio
lowerCamelCase_ =dataset.cast_column('''audio''' , Audio(sampling_rate=__snake_case ) )
# load eval pipeline
if args.device is None:
lowerCamelCase_ =0 if torch.cuda.is_available() else -1
lowerCamelCase_ =pipeline('''automatic-speech-recognition''' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(__snake_case : List[str] ):
lowerCamelCase_ =asr(
batch['''audio''']['''array'''] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
lowerCamelCase_ =prediction['''text''']
lowerCamelCase_ =normalize_text(batch['''sentence'''] )
return batch
# run inference on all examples
lowerCamelCase_ =dataset.map(__snake_case , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(__snake_case , __snake_case )
if __name__ == "__main__":
a_ : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
)
parser.add_argument(
"""--dataset""",
type=str,
required=True,
help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
)
parser.add_argument(
"""--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'` for Common Voice"""
)
parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
parser.add_argument(
"""--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
)
parser.add_argument(
"""--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
)
parser.add_argument(
"""--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
)
parser.add_argument(
"""--device""",
type=int,
default=None,
help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
)
a_ : Optional[Any] = parser.parse_args()
main(args)
| 75 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def a_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase_ ='''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
lowerCamelCase_ =Image.open(requests.get(__snake_case , stream=__snake_case ).raw ).convert('''RGB''' )
return image
def a_ ( __snake_case : Tuple ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''') )
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''') )
# fmt: on
return rename_keys
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =dct.pop(__snake_case )
lowerCamelCase_ =val
def a_ ( __snake_case : str , __snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
lowerCamelCase_ =torch.cat((q_bias, torch.zeros_like(__snake_case , requires_grad=__snake_case ), v_bias) )
lowerCamelCase_ =qkv_bias
def a_ ( __snake_case : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ =364 if '''coco''' in model_name else 224
lowerCamelCase_ =InstructBlipVisionConfig(image_size=__snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xxl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf''' , vocab_size=3_2001 ).to_dict()
elif "vicuna-13b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf''' , vocab_size=3_2001 ).to_dict()
else:
raise ValueError('''Model name not supported''' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
lowerCamelCase_ =InstructBlipQFormerConfig(vocab_size=3_0523 ).to_dict()
lowerCamelCase_ =InstructBlipConfig(vision_config=__snake_case , text_config=__snake_case , qformer_config=__snake_case )
return config, image_size
@torch.no_grad()
def a_ ( __snake_case : Optional[int] , __snake_case : str=None , __snake_case : List[Any]=False ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =AutoTokenizer.from_pretrained('''bert-base-uncased''' , truncation_side='''left''' )
qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''} )
if "t5" in model_name:
lowerCamelCase_ =TaTokenizerFast.from_pretrained('''google/flan-t5-xl''' , truncation_side='''left''' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
lowerCamelCase_ =LlamaTokenizerFast.from_pretrained(
'''huggyllama/llama-7b''' , truncation_side='''left''' , bos_token='''</s>''' , unk_token='''</s>''' )
tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''} )
lowerCamelCase_, lowerCamelCase_ =get_blipa_config(__snake_case )
lowerCamelCase_ =InstructBlipForConditionalGeneration(__snake_case ).eval()
lowerCamelCase_ ={
'''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
'''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
'''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
'''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
}
lowerCamelCase_, lowerCamelCase_ =model_name_to_original[model_name]
# load original model
print('''Loading original model...''' )
lowerCamelCase_ ='''cuda:1''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_ ='''cuda:2''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =load_model_and_preprocess(
name=__snake_case , model_type=__snake_case , is_eval=__snake_case , device=__snake_case )
original_model.eval()
print('''Done!''' )
# update state dict keys
lowerCamelCase_ =original_model.state_dict()
lowerCamelCase_ =create_rename_keys(__snake_case )
for src, dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
lowerCamelCase_ =state_dict.pop(__snake_case )
if key.startswith('''Qformer.bert''' ):
lowerCamelCase_ =key.replace('''Qformer.bert''' , '''qformer''' )
if "attention.self" in key:
lowerCamelCase_ =key.replace('''self''' , '''attention''' )
if "llm_proj" in key:
lowerCamelCase_ =key.replace('''llm_proj''' , '''language_projection''' )
if "t5_proj" in key:
lowerCamelCase_ =key.replace('''t5_proj''' , '''language_projection''' )
if key.startswith('''llm_model''' ):
lowerCamelCase_ =key.replace('''llm_model''' , '''language_model''' )
if key.startswith('''t5''' ):
lowerCamelCase_ =key.replace('''t5''' , '''language''' )
lowerCamelCase_ =val
# read in qv biases
read_in_q_v_bias(__snake_case , __snake_case )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(__snake_case , strict=__snake_case )
lowerCamelCase_ =load_demo_image()
lowerCamelCase_ ='''What is unusual about this image?'''
# create processor
lowerCamelCase_ =BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size} , image_mean=__snake_case , image_std=__snake_case )
lowerCamelCase_ =InstructBlipProcessor(
image_processor=__snake_case , tokenizer=__snake_case , qformer_tokenizer=__snake_case , )
lowerCamelCase_ =processor(images=__snake_case , text=__snake_case , return_tensors='''pt''' ).to(__snake_case )
# make sure processor creates exact same pixel values
lowerCamelCase_ =vis_processors['''eval'''](__snake_case ).unsqueeze(0 ).to(__snake_case )
lowerCamelCase_ =inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , __snake_case )
original_model.to(__snake_case )
hf_model.to(__snake_case )
with torch.no_grad():
if "vicuna" in model_name:
lowerCamelCase_ =original_model({'''image''': original_pixel_values, '''text_input''': [prompt]} ).logits
lowerCamelCase_ =hf_model(**__snake_case ).logits
else:
lowerCamelCase_ =original_model(
{'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']} ).logits
lowerCamelCase_ =tokenizer('''\n''' , return_tensors='''pt''' ).input_ids.to(__snake_case )
lowerCamelCase_ =label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
lowerCamelCase_ =hf_model(**__snake_case , labels=__snake_case ).logits
print('''First values of original logits:''' , original_logits[0, :3, :3] )
print('''First values of HF logits:''' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
lowerCamelCase_ =1e-4 if '''vicuna''' in model_name else 1e-5
assert torch.allclose(original_logits.to(logits.device ) , __snake_case , atol=__snake_case )
print('''Looks ok!''' )
print('''Generating with original model...''' )
lowerCamelCase_ =original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('''Generating with HF model...''' )
lowerCamelCase_ =hf_model.generate(
**__snake_case , do_sample=__snake_case , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
lowerCamelCase_ =2
print('''Original generation:''' , __snake_case )
lowerCamelCase_ =processor.batch_decode(__snake_case , skip_special_tokens=__snake_case )
lowerCamelCase_ =[text.strip() for text in output_text]
print('''HF generation:''' , __snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__snake_case )
hf_model.save_pretrained(__snake_case )
if push_to_hub:
processor.push_to_hub(F'''Salesforce/{model_name}''' )
hf_model.push_to_hub(F'''Salesforce/{model_name}''' )
if __name__ == "__main__":
a_ : Any = argparse.ArgumentParser()
a_ : Any = [
"""instructblip-vicuna-7b""",
"""instructblip-vicuna-13b""",
"""instructblip-flan-t5-xl""",
"""instructblip-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""instructblip-flan-t5-xl""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
a_ : str = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 75 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __UpperCamelCase :
lowercase : Union[str, Any] =XGLMConfig
lowercase : Optional[Any] ={}
lowercase : Optional[int] ='gelu'
def __init__( self, lowerCAmelCase, lowerCAmelCase=14, lowerCAmelCase=7, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=99, lowerCAmelCase=32, lowerCAmelCase=2, lowerCAmelCase=4, lowerCAmelCase=37, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=512, lowerCAmelCase=0.0_2, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =is_training
lowerCamelCase_ =use_input_mask
lowerCamelCase_ =use_labels
lowerCamelCase_ =vocab_size
lowerCamelCase_ =d_model
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =ffn_dim
lowerCamelCase_ =activation_function
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =initializer_range
lowerCamelCase_ =None
lowerCamelCase_ =0
lowerCamelCase_ =2
lowerCamelCase_ =1
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length], self.vocab_size ), clip_value_min=0, clip_value_max=3 )
lowerCamelCase_ =None
if self.use_input_mask:
lowerCamelCase_ =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ =self.get_config()
lowerCamelCase_ =floats_tensor([self.num_hidden_layers, self.num_attention_heads], 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size, d_model=self.hidden_size, num_layers=self.num_hidden_layers, attention_heads=self.num_attention_heads, ffn_dim=self.ffn_dim, activation_function=self.activation_function, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=lowerCAmelCase, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =config_and_inputs
lowerCamelCase_ ={
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : int =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowercase : Optional[Any] =(TFXGLMForCausalLM,) if is_tf_available() else ()
lowercase : Tuple =(
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowercase : Optional[Any] =False
lowercase : Optional[Any] =False
lowercase : Optional[int] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, n_embd=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =TFXGLMModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
def lowercase__ ( self ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def lowercase__ ( self, lowerCAmelCase=True ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =tf.convert_to_tensor([[2, 268, 9_865]], dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
lowerCamelCase_ =[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
# fmt: on
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist(), lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
tf.random.set_seed(0 )
lowerCamelCase_ =tokenizer('''Today is a nice day and''', return_tensors='''tf''' )
lowerCamelCase_ =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(''':/CPU:0''' ):
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, seed=[7, 0] )
lowerCamelCase_ =tokenizer.decode(output_ids[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =(
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ ='''left'''
# use different length sentences to test batching
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
lowerCamelCase_ =tokenizer(lowerCAmelCase, return_tensors='''tf''', padding=lowerCAmelCase )
lowerCamelCase_ =inputs['''input_ids''']
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, attention_mask=inputs['''attention_mask'''], max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[0], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[1], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer.batch_decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_non_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [non_padded_sentence, padded_sentence] )
| 75 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return 0.0
def a_ ( __snake_case : np.ndarray , __snake_case : int ) -> tuple[int | float, int | float]:
"""simple docstring"""
lowerCamelCase_ =min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
lowerCamelCase_ =max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.abs(np.fft.fft(__snake_case ) )
lowerCamelCase_ =20 * np.logaa(__snake_case )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
lowerCamelCase_ =get_bounds(__snake_case , __snake_case )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(__snake_case )
plt.show()
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.angle(np.fft.fft(__snake_case ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(__snake_case , -2 * pi ) )
plt.show()
| 75 | 1 |
'''simple docstring'''
def a_ ( __snake_case : list[int] ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
lowerCamelCase_ =sum(__snake_case ) / len(__snake_case ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =FunnelTokenizer
lowercase : List[str] =FunnelTokenizerFast
lowercase : Union[str, Any] =True
lowercase : int =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =[
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return FunnelTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ='''UNwant\u00E9d,running'''
lowerCamelCase_ ='''unwanted, running'''
return input_text, output_text
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.tokenizer_class(self.vocab_file )
lowerCamelCase_ =tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(lowerCAmelCase, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [7, 4, 5, 10, 8, 9] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
lowerCamelCase_ =tokenizer('''UNwant\u00E9d,running''' )
lowerCamelCase_ =len(inputs['''input_ids'''] ) - 1
self.assertListEqual(inputs['''token_type_ids'''], [2] + [0] * sentence_len )
lowerCamelCase_ =tokenizer('''UNwant\u00E9d,running''', '''UNwant\u00E9d,running''' )
self.assertListEqual(inputs['''token_type_ids'''], [2] + [0] * sentence_len + [1] * sentence_len )
| 75 | 1 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =[2, 1, 2, -1]
lowerCamelCase_ =[1, 2, 3, 4]
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =len(self.first_signal )
lowerCamelCase_ =len(self.second_signal )
lowerCamelCase_ =max(lowerCAmelCase, lowerCAmelCase )
# create a zero matrix of max_length x max_length
lowerCamelCase_ =[[0] * max_length for i in range(lowerCAmelCase )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(lowerCAmelCase ):
lowerCamelCase_ =deque(self.second_signal )
rotated_signal.rotate(lowerCAmelCase )
for j, item in enumerate(lowerCAmelCase ):
matrix[i][j] += item
# multiply the matrix with the first signal
lowerCamelCase_ =np.matmul(np.transpose(lowerCAmelCase ), np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(lowerCAmelCase, 2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 75 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def a_ ( __snake_case : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ =checkpoints.load_tax_checkpoint(__snake_case )
lowerCamelCase_ =flatten_dict(__snake_case )
return flax_params
def a_ ( __snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ ={}
lowerCamelCase_ ={
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCamelCase_ ={
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCamelCase_ ='''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =flax_dict[key]
lowerCamelCase_ ={}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCamelCase_ =torch.from_numpy(converted_dict[key].T )
else:
lowerCamelCase_ =torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Any=False , __snake_case : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =get_flax_param(__snake_case )
if not use_large:
lowerCamelCase_ =PixaStructVisionConfig()
lowerCamelCase_ =PixaStructTextConfig()
else:
lowerCamelCase_ =PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowerCamelCase_ =PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowerCamelCase_ =PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__snake_case )
lowerCamelCase_ =PixaStructForConditionalGeneration(__snake_case )
lowerCamelCase_ =rename_and_convert_flax_params(__snake_case )
model.load_state_dict(__snake_case )
lowerCamelCase_ =AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCamelCase_ =PixaStructImageProcessor()
lowerCamelCase_ =PixaStructProcessor(image_processor=__snake_case , tokenizer=__snake_case )
if use_large:
lowerCamelCase_ =4096
lowerCamelCase_ =True
# mkdir if needed
os.makedirs(__snake_case , exist_ok=__snake_case )
model.save_pretrained(__snake_case )
processor.save_pretrained(__snake_case )
print('''Model saved in {}'''.format(__snake_case ) )
if __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
a_ : Tuple = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 75 | 1 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
a_ : List[str] = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __UpperCamelCase :
def __init__( self, lowerCAmelCase, lowerCAmelCase=16, lowerCAmelCase=13, lowerCAmelCase=7, lowerCAmelCase=14, lowerCAmelCase=10, lowerCAmelCase=19, lowerCAmelCase=5, lowerCAmelCase=4, lowerCAmelCase=True, lowerCAmelCase=16, lowerCAmelCase=2, lowerCAmelCase=4, lowerCAmelCase=4, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=[1, 2, 3, 4, 5], lowerCAmelCase=25, lowerCAmelCase=5, ):
"""simple docstring"""
lowerCamelCase_ =d_model
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =prediction_length
lowerCamelCase_ =context_length
lowerCamelCase_ =cardinality
lowerCamelCase_ =num_time_features
lowerCamelCase_ =lags_sequence
lowerCamelCase_ =embedding_dimension
lowerCamelCase_ =is_training
lowerCamelCase_ =hidden_size
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =intermediate_size
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =attention_probs_dropout_prob
lowerCamelCase_ =context_length
lowerCamelCase_ =prediction_length + label_length
lowerCamelCase_ =label_length
lowerCamelCase_ =moving_average
lowerCamelCase_ =autocorrelation_factor
def lowercase__ ( self ):
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, prediction_length=self.prediction_length, context_length=self.context_length, label_length=self.label_length, lags_sequence=self.lags_sequence, num_time_features=self.num_time_features, num_static_categorical_features=1, cardinality=[self.cardinality], embedding_dimension=[self.embedding_dimension], moving_average=self.moving_average, )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =config.context_length + max(config.lags_sequence )
lowerCamelCase_ =ids_tensor([self.batch_size, 1], config.cardinality[0] )
lowerCamelCase_ =floats_tensor([self.batch_size, _past_length, config.num_time_features] )
lowerCamelCase_ =floats_tensor([self.batch_size, _past_length] )
lowerCamelCase_ =floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
lowerCamelCase_ =floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
lowerCamelCase_ =floats_tensor([self.batch_size, config.prediction_length] )
lowerCamelCase_ ={
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_config()
lowerCamelCase_ =self.prepare_autoformer_inputs_dict(lowerCAmelCase )
return config, inputs_dict
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.prepare_config_and_inputs()
return config, inputs_dict
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =AutoformerModel(config=lowerCAmelCase ).to(lowerCAmelCase ).eval()
lowerCamelCase_ =model(**lowerCAmelCase )
lowerCamelCase_ =outputs.encoder_last_hidden_state
lowerCamelCase_ =outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ =model.get_encoder()
encoder.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =AutoformerEncoder.from_pretrained(lowerCAmelCase ).to(lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =model.create_network_inputs(**lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_ =model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
lowerCamelCase_ =torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]), dim=-1, )
lowerCamelCase_ =encoder(inputs_embeds=lowerCAmelCase )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
lowerCamelCase_ =(
torch.mean(transformer_inputs[:, : config.context_length, ...], dim=1 )
.unsqueeze(1 )
.repeat(1, config.prediction_length, 1 )
)
lowerCamelCase_ =torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]], device=enc_input.device, )
lowerCamelCase_ =torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros), dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
), dim=-1, )
lowerCamelCase_ =torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean), dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
), dim=-1, )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ =model.get_decoder()
decoder.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =AutoformerDecoder.from_pretrained(lowerCAmelCase ).to(lowerCAmelCase )
lowerCamelCase_ =decoder(
trend=lowerCAmelCase, inputs_embeds=lowerCAmelCase, encoder_hidden_states=lowerCAmelCase, )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : List[str] =(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
lowercase : str =(AutoformerForPrediction,) if is_torch_available() else ()
lowercase : int ={'feature-extraction': AutoformerModel} if is_torch_available() else {}
lowercase : Union[str, Any] =False
lowercase : Optional[Any] =False
lowercase : Optional[Any] =False
lowercase : Dict =False
lowercase : List[str] =False
lowercase : Any =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =AutoformerModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, has_text_modality=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_ =model_class.from_pretrained(lowerCAmelCase, output_loading_info=lowerCAmelCase )
self.assertEqual(info['''missing_keys'''], [] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =inspect.signature(getattr(lowerCAmelCase, '''forward''' ) )
# The main input is the name of the argument after `self`
lowerCamelCase_ =list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
lowerCamelCase_ =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ =[*signature.parameters.keys()]
lowerCamelCase_ =[
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(lowerCAmelCase )], lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase_ =True
lowerCamelCase_ =getattr(self.model_tester, '''seq_length''', lowerCAmelCase )
lowerCamelCase_ =getattr(self.model_tester, '''decoder_seq_length''', lowerCAmelCase )
lowerCamelCase_ =getattr(self.model_tester, '''encoder_seq_length''', lowerCAmelCase )
lowerCamelCase_ =getattr(self.model_tester, '''d_model''', lowerCAmelCase )
lowerCamelCase_ =getattr(self.model_tester, '''num_attention_heads''', lowerCAmelCase )
lowerCamelCase_ =d_model // num_attention_heads
for model_class in self.all_model_classes:
lowerCamelCase_ =True
lowerCamelCase_ =False
lowerCamelCase_ =True
lowerCamelCase_ =model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
with torch.no_grad():
lowerCamelCase_ =model(**self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
lowerCamelCase_ =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase ), self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCamelCase_ =True
lowerCamelCase_ =model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
with torch.no_grad():
lowerCamelCase_ =model(**self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
lowerCamelCase_ =outputs.encoder_attentions
self.assertEqual(len(lowerCAmelCase ), self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, encoder_seq_length, dim], )
lowerCamelCase_ =len(lowerCAmelCase )
lowerCamelCase_ =7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
# decoder attentions
lowerCamelCase_ =outputs.decoder_attentions
self.assertIsInstance(lowerCAmelCase, (list, tuple) )
self.assertEqual(len(lowerCAmelCase ), self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, decoder_seq_length, dim], )
# cross attentions
lowerCamelCase_ =outputs.cross_attentions
self.assertIsInstance(lowerCAmelCase, (list, tuple) )
self.assertEqual(len(lowerCAmelCase ), self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, decoder_seq_length, dim], )
# Check attention is always last and order is fine
lowerCamelCase_ =True
lowerCamelCase_ =True
lowerCamelCase_ =model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
with torch.no_grad():
lowerCamelCase_ =model(**self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
self.assertEqual(out_len + 2, len(lowerCAmelCase ) )
lowerCamelCase_ =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase ), self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, encoder_seq_length, dim], )
@is_flaky()
def lowercase__ ( self ):
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def a_ ( __snake_case : str="train-batch.pt" ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__snake_case , repo_type='''dataset''' )
lowerCamelCase_ =torch.load(__snake_case , map_location=__snake_case )
return batch
@require_torch
@slow
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(lowerCAmelCase )
lowerCamelCase_ =prepare_batch()
with torch.no_grad():
lowerCamelCase_ =model(
past_values=batch['''past_values'''], past_time_features=batch['''past_time_features'''], past_observed_mask=batch['''past_observed_mask'''], static_categorical_features=batch['''static_categorical_features'''], future_values=batch['''future_values'''], future_time_features=batch['''future_time_features'''], )[0]
lowerCamelCase_ =torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape, lowerCAmelCase )
lowerCamelCase_ =torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]], device=lowerCAmelCase )
self.assertTrue(torch.allclose(output[0, :3, :3], lowerCAmelCase, atol=lowerCAmelCase ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(lowerCAmelCase )
lowerCamelCase_ =prepare_batch('''val-batch.pt''' )
with torch.no_grad():
lowerCamelCase_ =model(
past_values=batch['''past_values'''], past_time_features=batch['''past_time_features'''], past_observed_mask=batch['''past_observed_mask'''], static_categorical_features=batch['''static_categorical_features'''], ).encoder_last_hidden_state
lowerCamelCase_ =torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape, lowerCAmelCase )
lowerCamelCase_ =torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]], device=lowerCAmelCase )
self.assertTrue(torch.allclose(output[0, :3, :3], lowerCAmelCase, atol=lowerCAmelCase ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(lowerCAmelCase )
lowerCamelCase_ =prepare_batch('''val-batch.pt''' )
with torch.no_grad():
lowerCamelCase_ =model.generate(
static_categorical_features=batch['''static_categorical_features'''], past_time_features=batch['''past_time_features'''], past_values=batch['''past_values'''], future_time_features=batch['''future_time_features'''], past_observed_mask=batch['''past_observed_mask'''], )
lowerCamelCase_ =torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape, lowerCAmelCase )
lowerCamelCase_ =torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6], device=lowerCAmelCase )
lowerCamelCase_ =outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:], lowerCAmelCase, rtol=1e-1 ) )
| 75 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ : Union[str, Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Tuple =['pixel_values']
def __init__( self, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = True, lowerCAmelCase = 1 / 255, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = True, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =size if size is not None else {'''shortest_edge''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
lowerCamelCase_ =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase, param_name='''crop_size''' )
lowerCamelCase_ =do_resize
lowerCamelCase_ =size
lowerCamelCase_ =resample
lowerCamelCase_ =do_center_crop
lowerCamelCase_ =crop_size
lowerCamelCase_ =do_rescale
lowerCamelCase_ =rescale_factor
lowerCamelCase_ =do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCamelCase_ =image_std if image_std is not None else OPENAI_CLIP_STD
lowerCamelCase_ =do_convert_rgb
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowerCamelCase_ =get_resize_output_image_size(lowerCAmelCase, size=size['''shortest_edge'''], default_to_square=lowerCAmelCase )
return resize(lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowerCAmelCase, size=(size['''height'''], size['''width''']), data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return rescale(lowerCAmelCase, scale=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return normalize(lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = ChannelDimension.FIRST, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ =size if size is not None else self.size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =resample if resample is not None else self.resample
lowerCamelCase_ =do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase_ =crop_size if crop_size is not None else self.crop_size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''crop_size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ =rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ =do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ =image_std if image_std is not None else self.image_std
lowerCamelCase_ =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase_ =make_list_of_images(lowerCAmelCase )
if not valid_images(lowerCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase_ =[convert_to_rgb(lowerCAmelCase ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase_ =[to_numpy_array(lowerCAmelCase ) for image in images]
if do_resize:
lowerCamelCase_ =[self.resize(image=lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase ) for image in images]
if do_center_crop:
lowerCamelCase_ =[self.center_crop(image=lowerCAmelCase, size=lowerCAmelCase ) for image in images]
if do_rescale:
lowerCamelCase_ =[self.rescale(image=lowerCAmelCase, scale=lowerCAmelCase ) for image in images]
if do_normalize:
lowerCamelCase_ =[self.normalize(image=lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase ) for image in images]
lowerCamelCase_ =[to_channel_dimension_format(lowerCAmelCase, lowerCAmelCase ) for image in images]
lowerCamelCase_ ={'''pixel_values''': images}
return BatchFeature(data=lowerCAmelCase, tensor_type=lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ : Tuple = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
a_ : Dict = direct_transformers_import(PATH_TO_TRANSFORMERS)
a_ : Any = transformers.models.auto.configuration_auto.CONFIG_MAPPING
a_ : Tuple = {
# used to compute the property `self.chunk_length`
"""EncodecConfig""": ["""overlap"""],
# used as `self.bert_model = BertModel(config, ...)`
"""DPRConfig""": True,
# not used in modeling files, but it's an important information
"""FSMTConfig""": ["""langs"""],
# used internally in the configuration class file
"""GPTNeoConfig""": ["""attention_types"""],
# used internally in the configuration class file
"""EsmConfig""": ["""is_folding_model"""],
# used during training (despite we don't have training script for these models yet)
"""Mask2FormerConfig""": ["""ignore_value"""],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"""OneFormerConfig""": ["""ignore_value""", """norm"""],
# used during preprocessing and collation, see `collating_graphormer.py`
"""GraphormerConfig""": ["""spatial_pos_max"""],
# used internally in the configuration class file
"""T5Config""": ["""feed_forward_proj"""],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"""MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
"""UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
# used internally in the configuration class file
"""LongT5Config""": ["""feed_forward_proj"""],
# used internally in the configuration class file
"""SwitchTransformersConfig""": ["""feed_forward_proj"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""BioGptConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""GLPNConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""SegformerConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""CvtConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""PerceiverConfig""": ["""layer_norm_eps"""],
# used internally to calculate the feature size
"""InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate the feature size
"""TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate the feature size
"""AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate `mlp_dim`
"""SamVisionConfig""": ["""mlp_ratio"""],
# For (head) training, but so far not implemented
"""ClapAudioConfig""": ["""num_classes"""],
# Not used, but providing useful information to users
"""SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
"""CLIPSegConfig""": True,
"""DeformableDetrConfig""": True,
"""DetaConfig""": True,
"""DinatConfig""": True,
"""DonutSwinConfig""": True,
"""EfficientFormerConfig""": True,
"""FSMTConfig""": True,
"""JukeboxConfig""": True,
"""LayoutLMv2Config""": True,
"""MaskFormerSwinConfig""": True,
"""MT5Config""": True,
"""NatConfig""": True,
"""OneFormerConfig""": True,
"""PerceiverConfig""": True,
"""RagConfig""": True,
"""SpeechT5Config""": True,
"""SwinConfig""": True,
"""Swin2SRConfig""": True,
"""Swinv2Config""": True,
"""SwitchTransformersConfig""": True,
"""TableTransformerConfig""": True,
"""TapasConfig""": True,
"""TransfoXLConfig""": True,
"""UniSpeechConfig""": True,
"""UniSpeechSatConfig""": True,
"""WavLMConfig""": True,
"""WhisperConfig""": True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
"""JukeboxPriorConfig""": True,
# TODO: @Younes (for `is_decoder`)
"""Pix2StructTextConfig""": True,
}
)
def a_ ( __snake_case : Any , __snake_case : Tuple , __snake_case : Dict , __snake_case : str ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
F'''config.{attribute}''' in modeling_source
or F'''getattr(config, "{attribute}"''' in modeling_source
or F'''getattr(self.config, "{attribute}"''' in modeling_source
):
lowerCamelCase_ =True
# Deal with multi-line cases
elif (
re.search(
rF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"''' , __snake_case , )
is not None
):
lowerCamelCase_ =True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
lowerCamelCase_ =True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
lowerCamelCase_ =[
'''bos_index''',
'''eos_index''',
'''pad_index''',
'''unk_index''',
'''mask_index''',
'''image_size''',
'''use_cache''',
'''out_features''',
'''out_indices''',
]
lowerCamelCase_ =['''encoder_no_repeat_ngram_size''']
# Special cases to be allowed
lowerCamelCase_ =True
if not attribute_used:
lowerCamelCase_ =False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
lowerCamelCase_ =True
elif attribute in ["tie_word_embeddings"] and default_value is False:
lowerCamelCase_ =True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
lowerCamelCase_ =True
elif attribute.endswith('''_token_id''' ):
lowerCamelCase_ =True
# configuration class specific cases
if not case_allowed:
lowerCamelCase_ =SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
lowerCamelCase_ =allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def a_ ( __snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =dict(inspect.signature(config_class.__init__ ).parameters )
lowerCamelCase_ =[x for x in list(signature.keys() ) if x not in ['''self''', '''kwargs''']]
lowerCamelCase_ =[signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
lowerCamelCase_ ={}
if len(config_class.attribute_map ) > 0:
lowerCamelCase_ ={v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
lowerCamelCase_ =inspect.getsourcefile(__snake_case )
lowerCamelCase_ =os.path.dirname(__snake_case )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
lowerCamelCase_ =[os.path.join(__snake_case , __snake_case ) for fn in os.listdir(__snake_case ) if fn.startswith('''modeling_''' )]
# Get the source code strings
lowerCamelCase_ =[]
for path in modeling_paths:
if os.path.isfile(__snake_case ):
with open(__snake_case ) as fp:
modeling_sources.append(fp.read() )
lowerCamelCase_ =[]
for config_param, default_value in zip(__snake_case , __snake_case ):
# `attributes` here is all the variant names for `config_param`
lowerCamelCase_ =[config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(__snake_case , __snake_case , __snake_case , __snake_case ):
unused_attributes.append(attributes[0] )
return sorted(__snake_case )
def a_ ( ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ ={}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
lowerCamelCase_ =[
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda __snake_case : inspect.isclass(__snake_case )
and issubclass(__snake_case , __snake_case )
and inspect.getmodule(__snake_case ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
lowerCamelCase_ =check_config_attributes_being_used(__snake_case )
if len(__snake_case ) > 0:
lowerCamelCase_ =unused_attributes
if len(__snake_case ) > 0:
lowerCamelCase_ ='''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
for name, attributes in configs_with_unused_attributes.items():
error += F'''{name}: {attributes}\n'''
raise ValueError(__snake_case )
if __name__ == "__main__":
check_config_attributes()
| 75 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : str , __snake_case : list[str] | None = None , __snake_case : dict[str, float] | None = None , __snake_case : bool = False , ) -> tuple[int, float, str]:
"""simple docstring"""
lowerCamelCase_ =cipher_alphabet or [chr(__snake_case ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
lowerCamelCase_ ={
'''a''': 0.0_8_4_9_7,
'''b''': 0.0_1_4_9_2,
'''c''': 0.0_2_2_0_2,
'''d''': 0.0_4_2_5_3,
'''e''': 0.1_1_1_6_2,
'''f''': 0.0_2_2_2_8,
'''g''': 0.0_2_0_1_5,
'''h''': 0.0_6_0_9_4,
'''i''': 0.0_7_5_4_6,
'''j''': 0.0_0_1_5_3,
'''k''': 0.0_1_2_9_2,
'''l''': 0.0_4_0_2_5,
'''m''': 0.0_2_4_0_6,
'''n''': 0.0_6_7_4_9,
'''o''': 0.0_7_5_0_7,
'''p''': 0.0_1_9_2_9,
'''q''': 0.0_0_0_9_5,
'''r''': 0.0_7_5_8_7,
'''s''': 0.0_6_3_2_7,
'''t''': 0.0_9_3_5_6,
'''u''': 0.0_2_7_5_8,
'''v''': 0.0_0_9_7_8,
'''w''': 0.0_2_5_6_0,
'''x''': 0.0_0_1_5_0,
'''y''': 0.0_1_9_9_4,
'''z''': 0.0_0_0_7_7,
}
else:
# Custom frequencies dictionary
lowerCamelCase_ =frequencies_dict
if not case_sensitive:
lowerCamelCase_ =ciphertext.lower()
# Chi squared statistic values
lowerCamelCase_ ={}
# cycle through all of the shifts
for shift in range(len(__snake_case ) ):
lowerCamelCase_ =''''''
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
lowerCamelCase_ =(alphabet_letters.index(letter.lower() ) - shift) % len(
__snake_case )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
lowerCamelCase_ =0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
lowerCamelCase_ =letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.lower().count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
lowerCamelCase_ =(
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(__snake_case : int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
lowerCamelCase_ =min(
__snake_case , key=__snake_case , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
lowerCamelCase_
), (
lowerCamelCase_
),
) =chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 75 | 1 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : list[list[int]] ) -> bool:
"""simple docstring"""
lowerCamelCase_ =len(__snake_case )
# We need to create solution object to save path.
lowerCamelCase_ =[[0 for _ in range(__snake_case )] for _ in range(__snake_case )]
lowerCamelCase_ =run_maze(__snake_case , 0 , 0 , __snake_case )
if solved:
print('''\n'''.join(str(__snake_case ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def a_ ( __snake_case : list[list[int]] , __snake_case : int , __snake_case : int , __snake_case : list[list[int]] ) -> bool:
"""simple docstring"""
lowerCamelCase_ =len(__snake_case )
# Final check point.
if i == j == (size - 1):
lowerCamelCase_ =1
return True
lowerCamelCase_ =(not i < 0) and (not j < 0) # Check lower bounds
lowerCamelCase_ =(i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
lowerCamelCase_ =(not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
lowerCamelCase_ =1
# check for directions
if (
run_maze(__snake_case , i + 1 , __snake_case , __snake_case )
or run_maze(__snake_case , __snake_case , j + 1 , __snake_case )
or run_maze(__snake_case , i - 1 , __snake_case , __snake_case )
or run_maze(__snake_case , __snake_case , j - 1 , __snake_case )
):
return True
lowerCamelCase_ =0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
a_ : List[Any] = (
"""https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)
a_ : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def a_ ( ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ ='''https://pypi.org/pypi/diffusers/json'''
lowerCamelCase_ =json.loads(request.urlopen(__snake_case ).read() )['''releases'''].keys()
return sorted(__snake_case , key=lambda __snake_case : version.Version(__snake_case ) )
def a_ ( ) -> str:
"""simple docstring"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__snake_case )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase_ =Path(__snake_case ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def a_ ( __snake_case : Union[str, os.PathLike] ) -> List[str]:
"""simple docstring"""
init_hf_modules()
lowerCamelCase_ =Path(__snake_case ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase_ =dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def a_ ( __snake_case : Tuple ) -> List[str]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowerCamelCase_ =f.read()
# Imports of the form `import .xxx`
lowerCamelCase_ =re.findall('''^\s*import\s+\.(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Unique-ify
return list(set(__snake_case ) )
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
lowerCamelCase_ =False
lowerCamelCase_ =[module_file]
lowerCamelCase_ =[]
# Let's recurse through all relative imports
while not no_change:
lowerCamelCase_ =[]
for f in files_to_check:
new_imports.extend(get_relative_imports(__snake_case ) )
lowerCamelCase_ =Path(__snake_case ).parent
lowerCamelCase_ =[str(module_path / m ) for m in new_imports]
lowerCamelCase_ =[f for f in new_import_files if f not in all_relative_imports]
lowerCamelCase_ =[F'''{f}.py''' for f in new_import_files]
lowerCamelCase_ =len(__snake_case ) == 0
all_relative_imports.extend(__snake_case )
return all_relative_imports
def a_ ( __snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowerCamelCase_ =f.read()
# Imports of the form `import xxx`
lowerCamelCase_ =re.findall('''^\s*import\s+(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Only keep the top-level module
lowerCamelCase_ =[imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
lowerCamelCase_ =list(set(__snake_case ) )
lowerCamelCase_ =[]
for imp in imports:
try:
importlib.import_module(__snake_case )
except ImportError:
missing_packages.append(__snake_case )
if len(__snake_case ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
F'''{', '.join(__snake_case )}. Run `pip install {' '.join(__snake_case )}`''' )
return get_relative_imports(__snake_case )
def a_ ( __snake_case : Tuple , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =module_path.replace(os.path.sep , '''.''' )
lowerCamelCase_ =importlib.import_module(__snake_case )
if class_name is None:
return find_pipeline_class(__snake_case )
return getattr(__snake_case , __snake_case )
def a_ ( __snake_case : Dict ) -> Any:
"""simple docstring"""
from ..pipelines import DiffusionPipeline
lowerCamelCase_ =dict(inspect.getmembers(__snake_case , inspect.isclass ) )
lowerCamelCase_ =None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __snake_case )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''' )
lowerCamelCase_ =cls
return pipeline_class
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =str(__snake_case )
lowerCamelCase_ =os.path.join(__snake_case , __snake_case )
if os.path.isfile(__snake_case ):
lowerCamelCase_ =module_file_or_url
lowerCamelCase_ ='''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
lowerCamelCase_ =get_diffusers_versions()
# cut ".dev0"
lowerCamelCase_ ='''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
lowerCamelCase_ =latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(F'''Defaulting to latest_version: {revision}.''' )
elif revision in available_versions:
lowerCamelCase_ =F'''v{revision}'''
elif revision == "main":
lowerCamelCase_ =revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {', '.join(available_versions + ['main'] )}.''' )
# community pipeline on GitHub
lowerCamelCase_ =COMMUNITY_PIPELINES_URL.format(revision=__snake_case , pipeline=__snake_case )
try:
lowerCamelCase_ =cached_download(
__snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowerCamelCase_ ='''git'''
lowerCamelCase_ =pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
else:
try:
# Load from URL or cache if already cached
lowerCamelCase_ =hf_hub_download(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowerCamelCase_ =os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
# Check we have all the requirements in our environment
lowerCamelCase_ =check_imports(__snake_case )
# Now we move the module inside our cached dynamic modules.
lowerCamelCase_ =DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__snake_case )
lowerCamelCase_ =Path(__snake_case ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__snake_case , submodule_path / module_file )
for module_needed in modules_needed:
lowerCamelCase_ =F'''{module_needed}.py'''
shutil.copy(os.path.join(__snake_case , __snake_case ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__snake_case , __snake_case ):
lowerCamelCase_ =use_auth_token
elif use_auth_token is True:
lowerCamelCase_ =HfFolder.get_token()
else:
lowerCamelCase_ =None
lowerCamelCase_ =model_info(__snake_case , revision=__snake_case , token=__snake_case ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
lowerCamelCase_ =submodule_path / commit_hash
lowerCamelCase_ =full_submodule + os.path.sep + commit_hash
create_dynamic_module(__snake_case )
if not (submodule_path / module_file).exists():
shutil.copy(__snake_case , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__snake_case , F'''{module_needed}.py''' , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return os.path.join(__snake_case , __snake_case )
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[str] = None , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =get_cached_module_file(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return get_class_in_module(__snake_case , final_module.replace('''.py''' , '''''' ) )
| 75 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Union[List[PIL.Image.Image], np.ndarray]
lowercase : Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version(""">=""", """0.0.12""")
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : np.ndarray
lowercase : List[bool]
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 75 |
'''simple docstring'''
a_ : Any = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
a_ : Any = [
9_99,
9_76,
9_52,
9_28,
9_05,
8_82,
8_58,
8_57,
8_10,
7_62,
7_15,
7_14,
5_72,
4_29,
4_28,
2_86,
2_85,
2_38,
1_90,
1_43,
1_42,
1_18,
95,
71,
47,
24,
0,
]
a_ : Optional[Any] = [
9_99,
9_88,
9_77,
9_66,
9_55,
9_44,
9_33,
9_22,
9_11,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_50,
3_00,
2_99,
2_66,
2_33,
2_00,
1_99,
1_79,
1_59,
1_40,
1_20,
1_00,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
a_ : str = [
9_99,
9_95,
9_92,
9_89,
9_85,
9_81,
9_78,
9_75,
9_71,
9_67,
9_64,
9_61,
9_57,
9_56,
9_51,
9_47,
9_42,
9_37,
9_33,
9_28,
9_23,
9_19,
9_14,
9_13,
9_08,
9_03,
8_97,
8_92,
8_87,
8_81,
8_76,
8_71,
8_70,
8_64,
8_58,
8_52,
8_46,
8_40,
8_34,
8_28,
8_27,
8_20,
8_13,
8_06,
7_99,
7_92,
7_85,
7_84,
7_77,
7_70,
7_63,
7_56,
7_49,
7_42,
7_41,
7_33,
7_24,
7_16,
7_07,
6_99,
6_98,
6_88,
6_77,
6_66,
6_56,
6_55,
6_45,
6_34,
6_23,
6_13,
6_12,
5_98,
5_84,
5_70,
5_69,
5_55,
5_41,
5_27,
5_26,
5_05,
4_84,
4_83,
4_62,
4_40,
4_39,
3_96,
3_95,
3_52,
3_51,
3_08,
3_07,
2_64,
2_63,
2_20,
2_19,
1_76,
1_32,
88,
44,
0,
]
a_ : Optional[int] = [
9_99,
9_97,
9_95,
9_92,
9_90,
9_88,
9_86,
9_84,
9_81,
9_79,
9_77,
9_75,
9_72,
9_70,
9_68,
9_66,
9_64,
9_61,
9_59,
9_57,
9_56,
9_54,
9_51,
9_49,
9_46,
9_44,
9_41,
9_39,
9_36,
9_34,
9_31,
9_29,
9_26,
9_24,
9_21,
9_19,
9_16,
9_14,
9_13,
9_10,
9_07,
9_05,
9_02,
8_99,
8_96,
8_93,
8_91,
8_88,
8_85,
8_82,
8_79,
8_77,
8_74,
8_71,
8_70,
8_67,
8_64,
8_61,
8_58,
8_55,
8_52,
8_49,
8_46,
8_43,
8_40,
8_37,
8_34,
8_31,
8_28,
8_27,
8_24,
8_21,
8_17,
8_14,
8_11,
8_08,
8_04,
8_01,
7_98,
7_95,
7_91,
7_88,
7_85,
7_84,
7_80,
7_77,
7_74,
7_70,
7_66,
7_63,
7_60,
7_56,
7_52,
7_49,
7_46,
7_42,
7_41,
7_37,
7_33,
7_30,
7_26,
7_22,
7_18,
7_14,
7_10,
7_07,
7_03,
6_99,
6_98,
6_94,
6_90,
6_85,
6_81,
6_77,
6_73,
6_69,
6_64,
6_60,
6_56,
6_55,
6_50,
6_46,
6_41,
6_36,
6_32,
6_27,
6_22,
6_18,
6_13,
6_12,
6_07,
6_02,
5_96,
5_91,
5_86,
5_80,
5_75,
5_70,
5_69,
5_63,
5_57,
5_51,
5_45,
5_39,
5_33,
5_27,
5_26,
5_19,
5_12,
5_05,
4_98,
4_91,
4_84,
4_83,
4_74,
4_66,
4_57,
4_49,
4_40,
4_39,
4_28,
4_18,
4_07,
3_96,
3_95,
3_81,
3_66,
3_52,
3_51,
3_30,
3_08,
3_07,
2_86,
2_64,
2_63,
2_42,
2_20,
2_19,
1_76,
1_75,
1_32,
1_31,
88,
44,
0,
]
a_ : Dict = [
9_99,
9_91,
9_82,
9_74,
9_66,
9_58,
9_50,
9_41,
9_33,
9_25,
9_16,
9_08,
9_00,
8_99,
8_74,
8_50,
8_25,
8_00,
7_99,
7_00,
6_00,
5_00,
4_00,
3_00,
2_00,
1_00,
0,
]
a_ : Tuple = [
9_99,
9_92,
9_85,
9_78,
9_71,
9_64,
9_57,
9_49,
9_42,
9_35,
9_28,
9_21,
9_14,
9_07,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_00,
2_99,
2_00,
1_99,
1_00,
99,
0,
]
a_ : Any = [
9_99,
9_96,
9_92,
9_89,
9_85,
9_82,
9_79,
9_75,
9_72,
9_68,
9_65,
9_61,
9_58,
9_55,
9_51,
9_48,
9_44,
9_41,
9_38,
9_34,
9_31,
9_27,
9_24,
9_20,
9_17,
9_14,
9_10,
9_07,
9_03,
9_00,
8_99,
8_91,
8_84,
8_76,
8_69,
8_61,
8_53,
8_46,
8_38,
8_30,
8_23,
8_15,
8_08,
8_00,
7_99,
7_88,
7_77,
7_66,
7_55,
7_44,
7_33,
7_22,
7_11,
7_00,
6_99,
6_88,
6_77,
6_66,
6_55,
6_44,
6_33,
6_22,
6_11,
6_00,
5_99,
5_85,
5_71,
5_57,
5_42,
5_28,
5_14,
5_00,
4_99,
4_85,
4_71,
4_57,
4_42,
4_28,
4_14,
4_00,
3_99,
3_79,
3_59,
3_40,
3_20,
3_00,
2_99,
2_79,
2_59,
2_40,
2_20,
2_00,
1_99,
1_66,
1_33,
1_00,
99,
66,
33,
0,
]
| 75 | 1 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
a_ : str = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@require_tf
@slow
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = True, ):
"""simple docstring"""
lowerCamelCase_ =[file for file in os.listdir(lowerCAmelCase ) if os.path.isfile(os.path.join(lowerCAmelCase, lowerCAmelCase ) )]
if identifier is not None:
lowerCamelCase_ =[file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(lowerCAmelCase, lowerCAmelCase ):
for n_ in n_identifier:
lowerCamelCase_ =[file for file in files if n_ not in file]
else:
lowerCamelCase_ =[file for file in files if n_identifier not in file]
lowerCamelCase_ =ignore_files or []
ignore_files.append('''__init__.py''' )
lowerCamelCase_ =[file for file in files if file not in ignore_files]
for file in files:
# Open all files
print('''Testing''', lowerCAmelCase )
if only_modules:
lowerCamelCase_ =file.split('''.''' )[0]
try:
lowerCamelCase_ =getattr(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =doctest.DocTestSuite(lowerCAmelCase )
lowerCamelCase_ =unittest.TextTestRunner().run(lowerCAmelCase )
self.assertIs(len(result.failures ), 0 )
except AttributeError:
logger.info(f'''{module_identifier} is not a module.''' )
else:
lowerCamelCase_ =doctest.testfile(str('''..''' / directory / file ), optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed, 0 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =Path('''src/transformers''' )
lowerCamelCase_ ='''modeling'''
lowerCamelCase_ =[
'''modeling_ctrl.py''',
'''modeling_tf_ctrl.py''',
]
self.analyze_directory(lowerCAmelCase, identifier=lowerCAmelCase, ignore_files=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =Path('''src/transformers''' )
lowerCamelCase_ ='''tokenization'''
self.analyze_directory(lowerCAmelCase, identifier=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =Path('''src/transformers''' )
lowerCamelCase_ ='''configuration'''
self.analyze_directory(lowerCAmelCase, identifier=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =Path('''src/transformers''' )
lowerCamelCase_ =['''configuration''', '''modeling''', '''tokenization''']
self.analyze_directory(lowerCAmelCase, n_identifier=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =Path('''docs/source''' )
lowerCamelCase_ =['''favicon.ico''']
self.analyze_directory(lowerCAmelCase, ignore_files=lowerCAmelCase, only_modules=lowerCAmelCase )
| 75 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""convert_funnel_original_tf_checkpoint_to_pytorch""": [],
"""tokenization_funnel""": ["""FunnelTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[str] = ["""FunnelTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[int] = [
"""FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FunnelBaseModel""",
"""FunnelForMaskedLM""",
"""FunnelForMultipleChoice""",
"""FunnelForPreTraining""",
"""FunnelForQuestionAnswering""",
"""FunnelForSequenceClassification""",
"""FunnelForTokenClassification""",
"""FunnelModel""",
"""FunnelPreTrainedModel""",
"""load_tf_weights_in_funnel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = [
"""TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFFunnelBaseModel""",
"""TFFunnelForMaskedLM""",
"""TFFunnelForMultipleChoice""",
"""TFFunnelForPreTraining""",
"""TFFunnelForQuestionAnswering""",
"""TFFunnelForSequenceClassification""",
"""TFFunnelForTokenClassification""",
"""TFFunnelModel""",
"""TFFunnelPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
a_ : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 75 | 1 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
a_ : int = logging.get_logger(__name__)
def a_ ( __snake_case : bool , __snake_case : bool ) -> List[str]:
"""simple docstring"""
def run_func(__snake_case : List[str] ):
@wraps(__snake_case )
def run_in_eager_mode(*__snake_case : Tuple , **__snake_case : Tuple ):
return func(*__snake_case , **__snake_case )
@wraps(__snake_case )
@tf.function(experimental_compile=__snake_case )
def run_in_graph_mode(*__snake_case : int , **__snake_case : Union[str, Any] ):
return func(*__snake_case , **__snake_case )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
'''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.''' )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def a_ ( __snake_case : int , __snake_case : int , __snake_case : int ) -> ["tf.Tensor"]:
"""simple docstring"""
lowerCamelCase_ =random.Random()
lowerCamelCase_ =[rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(__snake_case , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : TensorFlowBenchmarkArguments
lowercase : PretrainedConfig
lowercase : str ="TensorFlow"
@property
def lowercase__ ( self ):
"""simple docstring"""
return tf.__version__
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
lowerCamelCase_ =self._prepare_inference_func(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
return self._measure_speed(_inference )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
lowerCamelCase_ =self._prepare_train_func(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
return self._measure_speed(_train )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx], lowerCAmelCase )
lowerCamelCase_ =self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
lowerCamelCase_ =self._prepare_inference_func(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
return self._measure_memory(_inference )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx], lowerCAmelCase )
lowerCamelCase_ =self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
lowerCamelCase_ =self._prepare_train_func(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
return self._measure_memory(_train )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
lowerCamelCase_ =(
hasattr(lowerCAmelCase, '''architectures''' )
and isinstance(config.architectures, lowerCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
lowerCamelCase_ ='''TF''' + config.architectures[0] # prepend 'TF' for tensorflow model
lowerCamelCase_ =__import__('''transformers''', fromlist=[model_class] )
lowerCamelCase_ =getattr(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =model_cls(lowerCAmelCase )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''' )
else:
lowerCamelCase_ =TF_MODEL_MAPPING[config.__class__](lowerCAmelCase )
# encoder-decoder has vocab size saved differently
lowerCamelCase_ =config.vocab_size if hasattr(lowerCAmelCase, '''vocab_size''' ) else config.encoder.vocab_size
lowerCamelCase_ =random_input_ids(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla )
def encoder_decoder_forward():
return model(lowerCAmelCase, decoder_input_ids=lowerCAmelCase, training=lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla )
def encoder_forward():
return model(lowerCAmelCase, training=lowerCAmelCase )
lowerCamelCase_ =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.''' )
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
lowerCamelCase_ =(
hasattr(lowerCAmelCase, '''architectures''' )
and isinstance(config.architectures, lowerCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
lowerCamelCase_ ='''TF''' + config.architectures[0] # prepend 'TF' for tensorflow model
lowerCamelCase_ =__import__('''transformers''', fromlist=[model_class] )
lowerCamelCase_ =getattr(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =model_cls(lowerCAmelCase )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''' )
else:
lowerCamelCase_ =TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase )
# encoder-decoder has vocab size saved differently
lowerCamelCase_ =config.vocab_size if hasattr(lowerCAmelCase, '''vocab_size''' ) else config.encoder.vocab_size
lowerCamelCase_ =random_input_ids(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla )
def encoder_decoder_train():
lowerCamelCase_ =model(lowerCAmelCase, decoder_input_ids=lowerCAmelCase, labels=lowerCAmelCase, training=lowerCAmelCase )[0]
lowerCamelCase_ =tf.gradients(lowerCAmelCase, model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla )
def encoder_train():
lowerCamelCase_ =model(lowerCAmelCase, labels=lowerCAmelCase, training=lowerCAmelCase )[0]
lowerCamelCase_ =tf.gradients(lowerCAmelCase, model.trainable_variables )
return gradients
lowerCamelCase_ =encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation''' )
timeit.repeat(lowerCAmelCase, repeat=1, number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
lowerCamelCase_ =timeit.repeat(
lowerCAmelCase, repeat=self.args.repeat, number=10, )
return min(lowerCAmelCase ) / 1_0.0
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
logger.info(
'''Note that TensorFlow allocates more memory than '''
'''it might need to speed up computation. '''
'''The memory reported here corresponds to the memory '''
'''reported by `nvidia-smi`, which can vary depending '''
'''on total available memory on the GPU that is used.''' )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
'''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
''' consumption line by line.''' )
lowerCamelCase_ =start_memory_tracing('''transformers''' )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
'''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
''' with `args.memory=False`''' )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
'''py3nvml not installed, we won\'t log GPU memory usage. '''
'''Install py3nvml (pip install py3nvml) to log information about GPU.''' )
lowerCamelCase_ ='''N/A'''
else:
logger.info(
'''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
''' running on the same GPU.''' )
# init nvml
nvml.nvmlInit()
func()
lowerCamelCase_ =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
lowerCamelCase_ =nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase )
lowerCamelCase_ =meminfo.used
lowerCamelCase_ =Memory(lowerCAmelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
'''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
''' TensorFlow.''' )
lowerCamelCase_ =None
else:
lowerCamelCase_ =measure_peak_memory_cpu(lowerCAmelCase )
lowerCamelCase_ =Memory(lowerCAmelCase ) if isinstance(lowerCAmelCase, lowerCAmelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
lowerCamelCase_ =stop_memory_tracing(lowerCAmelCase )
if memory is None:
lowerCamelCase_ =summary.total
else:
lowerCamelCase_ =None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 75 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
lowerCamelCase_ ={
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 16_000,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(self.tmpdirname, lowerCAmelCase )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
with open(self.feature_extraction_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
# load decoder from hub
lowerCamelCase_ ='''hf-internal-testing/ngram-beam-search-decoder'''
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.add_kwargs_tokens_map.copy()
kwargs.update(lowerCAmelCase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name, **lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer, lowerCAmelCase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor, lowerCAmelCase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels, decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set, decoder.model_container[decoder._model_key]._unigram_set, )
self.assertIsInstance(processor.decoder, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname, alpha=5.0, beta=3.0, score_boundary=-7.0, unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha, 5.0 )
self.assertEqual(processor.language_model.beta, 3.0 )
self.assertEqual(processor.language_model.score_boundary, -7.0 )
self.assertEqual(processor.language_model.unk_score_offset, 3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(lowerCAmelCase, '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=lowerCAmelCase, feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =floats_list((3, 1_000) )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =processor(lowerCAmelCase, return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ ='''This is a test string'''
lowerCamelCase_ =processor(text=lowerCAmelCase )
lowerCamelCase_ =tokenizer(lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def lowercase__ ( self, lowerCAmelCase=(2, 10, 16), lowerCAmelCase=77 ):
"""simple docstring"""
np.random.seed(lowerCAmelCase )
return np.random.rand(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits(shape=(10, 16), seed=13 )
lowerCamelCase_ =processor.decode(lowerCAmelCase )
lowerCamelCase_ =decoder.decode_beams(lowerCAmelCase )[0]
self.assertEqual(decoded_decoder[0], decoded_processor.text )
self.assertEqual('''</s> <s> </s>''', decoded_processor.text )
self.assertEqual(decoded_decoder[-2], decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1], decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase )
else:
with get_context(lowerCAmelCase ).Pool() as pool:
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =list(lowerCAmelCase )
with get_context('''fork''' ).Pool() as p:
lowerCamelCase_ =decoder.decode_beams_batch(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =[], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowerCAmelCase, decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''], decoded_processor.text )
self.assertListEqual(lowerCAmelCase, decoded_processor.logit_score )
self.assertListEqual(lowerCAmelCase, decoded_processor.lm_score )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =15
lowerCamelCase_ =-2_0.0
lowerCamelCase_ =-4.0
lowerCamelCase_ =processor.batch_decode(
lowerCAmelCase, beam_width=lowerCAmelCase, beam_prune_logp=lowerCAmelCase, token_min_logp=lowerCAmelCase, )
lowerCamelCase_ =decoded_processor_out.text
lowerCamelCase_ =list(lowerCAmelCase )
with get_context('''fork''' ).Pool() as pool:
lowerCamelCase_ =decoder.decode_beams_batch(
lowerCAmelCase, lowerCAmelCase, beam_width=lowerCAmelCase, beam_prune_logp=lowerCAmelCase, token_min_logp=lowerCAmelCase, )
lowerCamelCase_ =[d[0][0] for d in decoded_decoder_out]
lowerCamelCase_ =[d[0][2] for d in decoded_decoder_out]
lowerCamelCase_ =[d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''], lowerCAmelCase )
self.assertTrue(np.array_equal(lowerCAmelCase, decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7], lowerCAmelCase, atol=1e-3 ) )
self.assertTrue(np.array_equal(lowerCAmelCase, decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4], lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =2.0
lowerCamelCase_ =5.0
lowerCamelCase_ =-2_0.0
lowerCamelCase_ =True
lowerCamelCase_ =processor.batch_decode(
lowerCAmelCase, alpha=lowerCAmelCase, beta=lowerCAmelCase, unk_score_offset=lowerCAmelCase, lm_score_boundary=lowerCAmelCase, )
lowerCamelCase_ =decoded_processor_out.text
lowerCamelCase_ =list(lowerCAmelCase )
decoder.reset_params(
alpha=lowerCAmelCase, beta=lowerCAmelCase, unk_score_offset=lowerCAmelCase, lm_score_boundary=lowerCAmelCase, )
with get_context('''fork''' ).Pool() as pool:
lowerCamelCase_ =decoder.decode_beams_batch(
lowerCAmelCase, lowerCAmelCase, )
lowerCamelCase_ =[d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''], lowerCAmelCase )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha, 2.0 )
self.assertEqual(lm_model.beta, 5.0 )
self.assertEqual(lm_model.unk_score_offset, -2_0.0 )
self.assertEqual(lm_model.score_boundary, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
lowerCamelCase_ =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
lowerCamelCase_ =os.listdir(lowerCAmelCase )
lowerCamelCase_ =['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =snapshot_download('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
lowerCamelCase_ =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
lowerCamelCase_ =os.listdir(lowerCAmelCase )
lowerCamelCase_ =os.listdir(lowerCAmelCase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =floats_list((3, 1_000) )
lowerCamelCase_ =processor_wavaveca(lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =processor_auto(lowerCAmelCase, return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum(), input_auto[key].sum(), delta=1e-2 )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =processor_wavaveca.batch_decode(lowerCAmelCase )
lowerCamelCase_ =processor_auto.batch_decode(lowerCAmelCase )
self.assertListEqual(decoded_wavaveca.text, decoded_auto.text )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
self.assertListEqual(
processor.model_input_names, feature_extractor.model_input_names, msg='''`processor` and `feature_extractor` model input names do not match''', )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[d[key] for d in offsets]
return retrieved_list
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =self._get_dummy_logits()[0]
lowerCamelCase_ =processor.decode(lowerCAmelCase, output_word_offsets=lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ), 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(lowerCAmelCase, lowerCAmelCase ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''], '''word''' ) ), outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''word''' ), ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''start_offset''' ), [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''end_offset''' ), [1, 3, 5] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase, output_word_offsets=lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ), 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(lowerCAmelCase, lowerCAmelCase ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ) for o in outputs['''word_offsets''']], outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''word''' ), ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''start_offset''' ), [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''end_offset''' ), [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =load_dataset('''common_voice''', '''en''', split='''train''', streaming=lowerCAmelCase )
lowerCamelCase_ =ds.cast_column('''audio''', datasets.Audio(sampling_rate=16_000 ) )
lowerCamelCase_ =iter(lowerCAmelCase )
lowerCamelCase_ =next(lowerCAmelCase )
lowerCamelCase_ =AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
lowerCamelCase_ =WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
lowerCamelCase_ =processor(sample['''audio''']['''array'''], return_tensors='''pt''' ).input_values
with torch.no_grad():
lowerCamelCase_ =model(lowerCAmelCase ).logits.cpu().numpy()
lowerCamelCase_ =processor.decode(logits[0], output_word_offsets=lowerCAmelCase )
lowerCamelCase_ =model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
lowerCamelCase_ =[
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
lowerCamelCase_ ='''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ), lowerCAmelCase )
self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ), output.text )
# output times
lowerCamelCase_ =torch.tensor(self.get_from_offsets(lowerCAmelCase, '''start_time''' ) )
lowerCamelCase_ =torch.tensor(self.get_from_offsets(lowerCAmelCase, '''end_time''' ) )
# fmt: off
lowerCamelCase_ =torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
lowerCamelCase_ =torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=0.0_1 ) )
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=0.0_1 ) )
| 75 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : Any = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
a_ : Dict = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
a_ : Union[str, Any] = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
a_ : Tuple = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
a_ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 75 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =StableDiffusionInstructPixaPixPipeline
lowercase : List[Any] =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
lowercase : Optional[Any] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase : Union[str, Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase : List[Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=8, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=32, )
lowerCamelCase_ =PNDMScheduler(skip_prk_steps=lowerCAmelCase )
torch.manual_seed(0 )
lowerCamelCase_ =AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
torch.manual_seed(0 )
lowerCamelCase_ =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, )
lowerCamelCase_ =CLIPTextModel(lowerCAmelCase )
lowerCamelCase_ =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCamelCase_ ={
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowerCamelCase_ =image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase_ =Image.fromarray(np.uinta(lowerCAmelCase ) ).convert('''RGB''' )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ ='''french fries'''
lowerCamelCase_ =sd_pipe(**lowerCAmelCase, negative_prompt=lowerCAmelCase )
lowerCamelCase_ =output.images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =[inputs['''prompt''']] * 2
lowerCamelCase_ =np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
lowerCamelCase_ =torch.from_numpy(lowerCAmelCase ).unsqueeze(0 ).to(lowerCAmelCase )
lowerCamelCase_ =image / 2 + 0.5
lowerCamelCase_ =image.permute(0, 3, 1, 2 )
lowerCamelCase_ =image.repeat(2, 1, 1, 1 )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
lowerCamelCase_ =np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='''scaled_linear''' )
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
lowerCamelCase_ =[round(lowerCAmelCase, 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(lowerCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =VaeImageProcessor(do_resize=lowerCAmelCase, do_normalize=lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' ) )[0]
lowerCamelCase_ =components['''vae''']
lowerCamelCase_ =self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
lowerCamelCase_ =vae.encode(inputs[image_param] ).latent_dist.mode()
lowerCamelCase_ =pipe(**lowerCAmelCase )[0]
lowerCamelCase_ =np.abs(out - out_latents_inputs ).max()
self.assertLess(lowerCAmelCase, 1e-4, '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
lowerCamelCase_ ={
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0
def callback_fn(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ) -> None:
lowerCamelCase_ =True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowerCamelCase_ =False
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
pipe(**lowerCAmelCase, callback=lowerCAmelCase, callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowercase__ ( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCamelCase_ =inputs['''image'''].resize((504, 504) )
lowerCamelCase_ ='''timbrooks/instruct-pix2pix'''
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
lowerCAmelCase, safety_checker=lowerCAmelCase, )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =output.images[0]
lowerCamelCase_ =image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
lowerCamelCase_ =np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 75 | 1 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Tuple = logging.get_logger(__name__)
a_ : Tuple = """▁"""
a_ : int = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
a_ : List[Any] = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
a_ : Dict = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
a_ : List[str] = {
"""ernie-m-base""": 5_14,
"""ernie-m-large""": 5_14,
}
a_ : Optional[int] = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[str] =["input_ids"]
lowercase : List[Any] =VOCAB_FILES_NAMES
lowercase : Dict =PRETRAINED_INIT_CONFIGURATION
lowercase : Union[str, Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Optional[Any] =PRETRAINED_VOCAB_FILES_MAP
lowercase : List[Any] =RESOURCE_FILES_NAMES
def __init__( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase=False, lowerCAmelCase="utf8", lowerCAmelCase="[UNK]", lowerCAmelCase="[SEP]", lowerCAmelCase="[PAD]", lowerCAmelCase="[CLS]", lowerCAmelCase="[MASK]", lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowerCAmelCase, unk_token=lowerCAmelCase, sep_token=lowerCAmelCase, pad_token=lowerCAmelCase, cls_token=lowerCAmelCase, mask_token=lowerCAmelCase, vocab_file=lowerCAmelCase, encoding=lowerCAmelCase, sp_model_kwargs=self.sp_model_kwargs, **lowerCAmelCase, )
lowerCamelCase_ =do_lower_case
lowerCamelCase_ =sentencepiece_model_ckpt
lowerCamelCase_ =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowerCamelCase_ =self.load_vocab(filepath=lowerCAmelCase )
else:
lowerCamelCase_ ={self.sp_model.id_to_piece(lowerCAmelCase ): id for id in range(self.sp_model.get_piece_size() )}
lowerCamelCase_ ={v: k for k, v in self.vocab.items()}
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if text is None:
return None
lowerCamelCase_ =self.tokenize(lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_ ='''''', []
for i, ch in enumerate(lowerCAmelCase ):
if ch in self.SP_CHAR_MAPPING:
lowerCamelCase_ =self.SP_CHAR_MAPPING.get(lowerCAmelCase )
else:
lowerCamelCase_ =unicodedata.normalize('''NFKC''', lowerCAmelCase )
if self.is_whitespace(lowerCAmelCase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(lowerCAmelCase ) )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =normalized_text, [], 0
if self.do_lower_case:
lowerCamelCase_ =text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowerCamelCase_ =token[1:]
lowerCamelCase_ =text[offset:].index(lowerCAmelCase ) + offset
lowerCamelCase_ =start + len(lowerCAmelCase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowerCamelCase_ =end
return token_mapping
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.vocab )
def lowercase__ ( self ):
"""simple docstring"""
return dict(self.vocab, **self.added_tokens_encoder )
def __getstate__( self ):
"""simple docstring"""
lowerCamelCase_ =self.__dict__.copy()
lowerCamelCase_ =None
return state
def __setstate__( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
lowerCamelCase_ ={}
lowerCamelCase_ =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return "".join((self.SP_CHAR_MAPPING.get(lowerCAmelCase, lowerCAmelCase ) for c in text) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=False, lowerCAmelCase=64, lowerCAmelCase=0.1 ):
"""simple docstring"""
if self.sp_model_kwargs.get('''enable_sampling''' ) is True:
lowerCamelCase_ =True
if self.sp_model_kwargs.get('''alpha''' ) is not None:
lowerCamelCase_ =self.sp_model_kwargs.get('''alpha''' )
if self.sp_model_kwargs.get('''nbest_size''' ) is not None:
lowerCamelCase_ =self.sp_model_kwargs.get('''nbest_size''' )
if not enable_sampling:
lowerCamelCase_ =self.sp_model.EncodeAsPieces(lowerCAmelCase )
else:
lowerCamelCase_ =self.sp_model.SampleEncodeAsPieces(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =[]
for pi, piece in enumerate(lowerCAmelCase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(lowerCAmelCase ) and pi != 0:
new_pieces.append(lowerCAmelCase )
continue
else:
continue
lowerCamelCase_ =0
for i, chunk in enumerate(lowerCAmelCase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(lowerCAmelCase ) or self.is_punct(lowerCAmelCase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(lowerCAmelCase )
lowerCamelCase_ =i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCamelCase_ =i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCamelCase_ =i
if len(lowerCAmelCase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''''''.join(lowerCAmelCase ).replace(lowerCAmelCase, ''' ''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.convert_ids_to_tokens(lowerCAmelCase )
lowerCamelCase_ =''''''.join(lowerCAmelCase ).replace(lowerCAmelCase, ''' ''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.vocab.get(lowerCAmelCase, self.vocab.get(self.unk_token ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.reverse_vocab.get(lowerCAmelCase, self.unk_token )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ =[self.cls_token_id]
lowerCamelCase_ =[self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None ):
"""simple docstring"""
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase=False ):
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(lowerCAmelCase )) + [1, 1] + ([0] * len(lowerCAmelCase )) + [1]
return [1] + ([0] * len(lowerCAmelCase )) + [1]
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if token_ids_a is None:
# [CLS] X [SEP]
return (len(lowerCAmelCase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(lowerCAmelCase ) + 1) + [1] * (len(lowerCAmelCase ) + 3)
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if "\u4e00" <= char <= "\u9fff":
return True
return False
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(lowerCAmelCase ) == 1:
lowerCamelCase_ =unicodedata.category(lowerCAmelCase )
if cat == "Zs":
return True
return False
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ={}
with io.open(lowerCAmelCase, '''r''', encoding='''utf-8''' ) as f:
for index, line in enumerate(lowerCAmelCase ):
lowerCamelCase_ =line.rstrip('''\n''' )
lowerCamelCase_ =int(lowerCAmelCase )
return token_to_idx
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
lowerCamelCase_ =0
if os.path.isdir(lowerCAmelCase ):
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
lowerCamelCase_ =(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
''' Please check that the vocabulary is not corrupted!''' )
lowerCamelCase_ =token_index
writer.write(token + '''\n''' )
index += 1
lowerCamelCase_ =os.path.join(lowerCAmelCase, '''sentencepiece.bpe.model''' )
with open(lowerCAmelCase, '''wb''' ) as fi:
lowerCamelCase_ =self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase )
return (vocab_file,)
| 75 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __UpperCamelCase :
lowercase : Union[str, Any] =XGLMConfig
lowercase : Optional[Any] ={}
lowercase : Optional[int] ='gelu'
def __init__( self, lowerCAmelCase, lowerCAmelCase=14, lowerCAmelCase=7, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=99, lowerCAmelCase=32, lowerCAmelCase=2, lowerCAmelCase=4, lowerCAmelCase=37, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=512, lowerCAmelCase=0.0_2, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =is_training
lowerCamelCase_ =use_input_mask
lowerCamelCase_ =use_labels
lowerCamelCase_ =vocab_size
lowerCamelCase_ =d_model
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =ffn_dim
lowerCamelCase_ =activation_function
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =initializer_range
lowerCamelCase_ =None
lowerCamelCase_ =0
lowerCamelCase_ =2
lowerCamelCase_ =1
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length], self.vocab_size ), clip_value_min=0, clip_value_max=3 )
lowerCamelCase_ =None
if self.use_input_mask:
lowerCamelCase_ =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ =self.get_config()
lowerCamelCase_ =floats_tensor([self.num_hidden_layers, self.num_attention_heads], 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size, d_model=self.hidden_size, num_layers=self.num_hidden_layers, attention_heads=self.num_attention_heads, ffn_dim=self.ffn_dim, activation_function=self.activation_function, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=lowerCAmelCase, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =config_and_inputs
lowerCamelCase_ ={
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : int =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowercase : Optional[Any] =(TFXGLMForCausalLM,) if is_tf_available() else ()
lowercase : Tuple =(
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowercase : Optional[Any] =False
lowercase : Optional[Any] =False
lowercase : Optional[int] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, n_embd=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =TFXGLMModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
def lowercase__ ( self ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def lowercase__ ( self, lowerCAmelCase=True ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =tf.convert_to_tensor([[2, 268, 9_865]], dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
lowerCamelCase_ =[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
# fmt: on
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist(), lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
tf.random.set_seed(0 )
lowerCamelCase_ =tokenizer('''Today is a nice day and''', return_tensors='''tf''' )
lowerCamelCase_ =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(''':/CPU:0''' ):
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, seed=[7, 0] )
lowerCamelCase_ =tokenizer.decode(output_ids[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =(
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ ='''left'''
# use different length sentences to test batching
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
lowerCamelCase_ =tokenizer(lowerCAmelCase, return_tensors='''tf''', padding=lowerCAmelCase )
lowerCamelCase_ =inputs['''input_ids''']
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, attention_mask=inputs['''attention_mask'''], max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[0], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[1], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer.batch_decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_non_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [non_padded_sentence, padded_sentence] )
| 75 | 1 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
a_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(*lowerCAmelCase, **lowerCAmelCase )
requires_backends(self, '''decord''' )
self.check_model_type(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None ):
"""simple docstring"""
lowerCamelCase_ ={}
if frame_sampling_rate is not None:
lowerCamelCase_ =frame_sampling_rate
if num_frames is not None:
lowerCamelCase_ =num_frames
lowerCamelCase_ ={}
if top_k is not None:
lowerCamelCase_ =top_k
return preprocess_params, {}, postprocess_params
def __call__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return super().__call__(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase=1 ):
"""simple docstring"""
if num_frames is None:
lowerCamelCase_ =self.model.config.num_frames
if video.startswith('''http://''' ) or video.startswith('''https://''' ):
lowerCamelCase_ =BytesIO(requests.get(lowerCAmelCase ).content )
lowerCamelCase_ =VideoReader(lowerCAmelCase )
videoreader.seek(0 )
lowerCamelCase_ =0
lowerCamelCase_ =num_frames * frame_sampling_rate - 1
lowerCamelCase_ =np.linspace(lowerCAmelCase, lowerCAmelCase, num=lowerCAmelCase, dtype=np.intaa )
lowerCamelCase_ =videoreader.get_batch(lowerCAmelCase ).asnumpy()
lowerCamelCase_ =list(lowerCAmelCase )
lowerCamelCase_ =self.image_processor(lowerCAmelCase, return_tensors=self.framework )
return model_inputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.model(**lowerCAmelCase )
return model_outputs
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=5 ):
"""simple docstring"""
if top_k > self.model.config.num_labels:
lowerCamelCase_ =self.model.config.num_labels
if self.framework == "pt":
lowerCamelCase_ =model_outputs.logits.softmax(-1 )[0]
lowerCamelCase_, lowerCamelCase_ =probs.topk(lowerCAmelCase )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowerCamelCase_ =scores.tolist()
lowerCamelCase_ =ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase, lowerCAmelCase )]
| 75 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __UpperCamelCase :
@staticmethod
def lowercase__ ( *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
lowercase : int =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =object_detector(examples[0], threshold=0.0 )
lowerCamelCase_ =len(lowerCAmelCase )
self.assertGreater(lowerCAmelCase, 0 )
self.assertEqual(
lowerCAmelCase, [
{
'''score''': ANY(lowerCAmelCase ),
'''label''': ANY(lowerCAmelCase ),
'''box''': {'''xmin''': ANY(lowerCAmelCase ), '''ymin''': ANY(lowerCAmelCase ), '''xmax''': ANY(lowerCAmelCase ), '''ymax''': ANY(lowerCAmelCase )},
}
for i in range(lowerCAmelCase )
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
]
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0.2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], top_k=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
], )
| 75 | 1 |
'''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
a_ : Optional[Any] = """"""
if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):
class __UpperCamelCase ( tr.AbstractTransform ):
def __init__( self, lowerCAmelCase = " " ):
"""simple docstring"""
lowerCamelCase_ =sentence_delimiter
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return list(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
for sent_idx, sentence in enumerate(lowerCAmelCase ):
chars.extend(self.process_string(lowerCAmelCase ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCAmelCase ) - 1:
chars.append(self.sentence_delimiter )
return chars
a_ : Tuple = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
a_ : Optional[Any] = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
a_ : List[Any] = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""
a_ : int = """\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.
CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.
Character error rate can be computed as:
CER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).
CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""
a_ : List[Any] = """
Computes CER score of transcribed segments against references.
Args:
references: list of references for each speech input.
predictions: list of transcribtions to score.
concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
(float): the character error rate
Examples:
>>> predictions = [\"this is the prediction\", \"there is an other sample\"]
>>> references = [\"this is the reference\", \"there is another one\"]
>>> cer = datasets.load_metric(\"cer\")
>>> cer_score = cer.compute(predictions=predictions, references=references)
>>> print(cer_score)
0.34146341463414637
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def lowercase__ ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'''predictions''': datasets.Value('''string''', id='''sequence''' ),
'''references''': datasets.Value('''string''', id='''sequence''' ),
} ), codebase_urls=['''https://github.com/jitsi/jiwer/'''], reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
'''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
], )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase=False ):
"""simple docstring"""
if concatenate_texts:
return jiwer.compute_measures(
lowerCAmelCase, lowerCAmelCase, truth_transform=lowerCAmelCase, hypothesis_transform=lowerCAmelCase, )["wer"]
lowerCamelCase_ =0
lowerCamelCase_ =0
for prediction, reference in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =jiwer.compute_measures(
lowerCAmelCase, lowerCAmelCase, truth_transform=lowerCAmelCase, hypothesis_transform=lowerCAmelCase, )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 75 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[int] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a_ : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a_ : Optional[int] = {"""facebook/blenderbot_small-90M""": 5_12}
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =set()
lowerCamelCase_ =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ =char
lowerCamelCase_ =set(__snake_case )
return pairs
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[int] =VOCAB_FILES_NAMES
lowercase : Tuple =PRETRAINED_VOCAB_FILES_MAP
lowercase : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Dict =['input_ids', 'attention_mask']
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase="__start__", lowerCAmelCase="__end__", lowerCAmelCase="__unk__", lowerCAmelCase="__null__", **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(unk_token=lowerCAmelCase, bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, pad_token=lowerCAmelCase, **lowerCAmelCase )
with open(lowerCAmelCase, encoding='''utf-8''' ) as vocab_handle:
lowerCamelCase_ =json.load(lowerCAmelCase )
lowerCamelCase_ ={v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase, encoding='''utf-8''' ) as merges_handle:
lowerCamelCase_ =merges_handle.read().split('''\n''' )[1:-1]
lowerCamelCase_ =[tuple(merge.split() ) for merge in merges]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={}
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.encoder )
def lowercase__ ( self ):
"""simple docstring"""
return dict(self.encoder, **self.added_tokens_encoder )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
lowerCamelCase_ =re.sub('''([.,!?()])''', R''' \1''', lowerCAmelCase )
lowerCamelCase_ =re.sub('''(\')''', R''' \1 ''', lowerCAmelCase )
lowerCamelCase_ =re.sub(R'''\s{2,}''', ''' ''', lowerCAmelCase )
if "\n" in token:
lowerCamelCase_ =token.replace('''\n''', ''' __newln__''' )
lowerCamelCase_ =token.split(''' ''' )
lowerCamelCase_ =[]
for token in tokens:
if not len(lowerCAmelCase ):
continue
lowerCamelCase_ =token.lower()
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
lowerCamelCase_ =get_pairs(lowerCAmelCase )
if not pairs:
words.append(lowerCAmelCase )
continue
while True:
lowerCamelCase_ =min(lowerCAmelCase, key=lambda lowerCAmelCase : self.bpe_ranks.get(lowerCAmelCase, float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_, lowerCamelCase_ =bigram
lowerCamelCase_ =[]
lowerCamelCase_ =0
while i < len(lowerCAmelCase ):
try:
lowerCamelCase_ =word.index(lowerCAmelCase, lowerCAmelCase )
new_word.extend(word[i:j] )
lowerCamelCase_ =j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =new_word
if len(lowerCAmelCase ) == 1:
break
else:
lowerCamelCase_ =get_pairs(lowerCAmelCase )
lowerCamelCase_ ='''@@ '''.join(lowerCAmelCase )
lowerCamelCase_ =word[:-4]
lowerCamelCase_ =word
words.append(lowerCAmelCase )
return " ".join(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =re.findall(R'''\S+\n?''', lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =token.lower()
return self.encoder.get(lowerCAmelCase, self.encoder.get(self.unk_token ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase, self.unk_token )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''' '''.join(lowerCAmelCase ).replace('''@@ ''', '''''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=lowerCAmelCase, ensure_ascii=lowerCAmelCase ) + '''\n''' )
lowerCamelCase_ =0
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
lowerCamelCase_ =token_index
writer.write(''' '''.join(lowerCAmelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 75 | 1 |
'''simple docstring'''
from __future__ import annotations
a_ : Tuple = list[list[int]]
# assigning initial values to the grid
a_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
a_ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( __snake_case : Matrix , __snake_case : int , __snake_case : int , __snake_case : int ) -> bool:
"""simple docstring"""
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( __snake_case : Matrix ) -> tuple[int, int] | None:
"""simple docstring"""
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( __snake_case : Matrix ) -> Matrix | None:
"""simple docstring"""
if location := find_empty_location(__snake_case ):
lowerCamelCase_, lowerCamelCase_ =location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(__snake_case , __snake_case , __snake_case , __snake_case ):
lowerCamelCase_ =digit
if sudoku(__snake_case ) is not None:
return grid
lowerCamelCase_ =0
return None
def a_ ( __snake_case : Matrix ) -> None:
"""simple docstring"""
for row in grid:
for cell in row:
print(__snake_case , end=''' ''' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("""\nExample grid:\n""" + """=""" * 20)
print_solution(example_grid)
print("""\nExample grid solution:""")
a_ : Tuple = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("""Cannot find a solution.""")
| 75 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[str] ='efficientformer'
def __init__( self, lowerCAmelCase = [3, 2, 6, 4], lowerCAmelCase = [48, 96, 224, 448], lowerCAmelCase = [True, True, True, True], lowerCAmelCase = 448, lowerCAmelCase = 32, lowerCAmelCase = 4, lowerCAmelCase = 7, lowerCAmelCase = 5, lowerCAmelCase = 8, lowerCAmelCase = 4, lowerCAmelCase = 0.0, lowerCAmelCase = 16, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 2, lowerCAmelCase = 1, lowerCAmelCase = 0.0, lowerCAmelCase = 1, lowerCAmelCase = True, lowerCAmelCase = True, lowerCAmelCase = 1e-5, lowerCAmelCase = "gelu", lowerCAmelCase = 0.0_2, lowerCAmelCase = 1e-12, lowerCAmelCase = 224, lowerCAmelCase = 1e-05, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =hidden_sizes
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =initializer_range
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =depths
lowerCamelCase_ =mlp_expansion_ratio
lowerCamelCase_ =downsamples
lowerCamelCase_ =dim
lowerCamelCase_ =key_dim
lowerCamelCase_ =attention_ratio
lowerCamelCase_ =resolution
lowerCamelCase_ =pool_size
lowerCamelCase_ =downsample_patch_size
lowerCamelCase_ =downsample_stride
lowerCamelCase_ =downsample_pad
lowerCamelCase_ =drop_path_rate
lowerCamelCase_ =num_metaad_blocks
lowerCamelCase_ =distillation
lowerCamelCase_ =use_layer_scale
lowerCamelCase_ =layer_scale_init_value
lowerCamelCase_ =image_size
lowerCamelCase_ =batch_norm_eps
| 75 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def a_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase_ ='''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
lowerCamelCase_ =Image.open(requests.get(__snake_case , stream=__snake_case ).raw ).convert('''RGB''' )
return image
def a_ ( __snake_case : Tuple ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''') )
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''') )
# fmt: on
return rename_keys
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =dct.pop(__snake_case )
lowerCamelCase_ =val
def a_ ( __snake_case : str , __snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
lowerCamelCase_ =torch.cat((q_bias, torch.zeros_like(__snake_case , requires_grad=__snake_case ), v_bias) )
lowerCamelCase_ =qkv_bias
def a_ ( __snake_case : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ =364 if '''coco''' in model_name else 224
lowerCamelCase_ =InstructBlipVisionConfig(image_size=__snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xxl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf''' , vocab_size=3_2001 ).to_dict()
elif "vicuna-13b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf''' , vocab_size=3_2001 ).to_dict()
else:
raise ValueError('''Model name not supported''' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
lowerCamelCase_ =InstructBlipQFormerConfig(vocab_size=3_0523 ).to_dict()
lowerCamelCase_ =InstructBlipConfig(vision_config=__snake_case , text_config=__snake_case , qformer_config=__snake_case )
return config, image_size
@torch.no_grad()
def a_ ( __snake_case : Optional[int] , __snake_case : str=None , __snake_case : List[Any]=False ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =AutoTokenizer.from_pretrained('''bert-base-uncased''' , truncation_side='''left''' )
qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''} )
if "t5" in model_name:
lowerCamelCase_ =TaTokenizerFast.from_pretrained('''google/flan-t5-xl''' , truncation_side='''left''' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
lowerCamelCase_ =LlamaTokenizerFast.from_pretrained(
'''huggyllama/llama-7b''' , truncation_side='''left''' , bos_token='''</s>''' , unk_token='''</s>''' )
tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''} )
lowerCamelCase_, lowerCamelCase_ =get_blipa_config(__snake_case )
lowerCamelCase_ =InstructBlipForConditionalGeneration(__snake_case ).eval()
lowerCamelCase_ ={
'''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
'''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
'''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
'''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
}
lowerCamelCase_, lowerCamelCase_ =model_name_to_original[model_name]
# load original model
print('''Loading original model...''' )
lowerCamelCase_ ='''cuda:1''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_ ='''cuda:2''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =load_model_and_preprocess(
name=__snake_case , model_type=__snake_case , is_eval=__snake_case , device=__snake_case )
original_model.eval()
print('''Done!''' )
# update state dict keys
lowerCamelCase_ =original_model.state_dict()
lowerCamelCase_ =create_rename_keys(__snake_case )
for src, dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
lowerCamelCase_ =state_dict.pop(__snake_case )
if key.startswith('''Qformer.bert''' ):
lowerCamelCase_ =key.replace('''Qformer.bert''' , '''qformer''' )
if "attention.self" in key:
lowerCamelCase_ =key.replace('''self''' , '''attention''' )
if "llm_proj" in key:
lowerCamelCase_ =key.replace('''llm_proj''' , '''language_projection''' )
if "t5_proj" in key:
lowerCamelCase_ =key.replace('''t5_proj''' , '''language_projection''' )
if key.startswith('''llm_model''' ):
lowerCamelCase_ =key.replace('''llm_model''' , '''language_model''' )
if key.startswith('''t5''' ):
lowerCamelCase_ =key.replace('''t5''' , '''language''' )
lowerCamelCase_ =val
# read in qv biases
read_in_q_v_bias(__snake_case , __snake_case )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(__snake_case , strict=__snake_case )
lowerCamelCase_ =load_demo_image()
lowerCamelCase_ ='''What is unusual about this image?'''
# create processor
lowerCamelCase_ =BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size} , image_mean=__snake_case , image_std=__snake_case )
lowerCamelCase_ =InstructBlipProcessor(
image_processor=__snake_case , tokenizer=__snake_case , qformer_tokenizer=__snake_case , )
lowerCamelCase_ =processor(images=__snake_case , text=__snake_case , return_tensors='''pt''' ).to(__snake_case )
# make sure processor creates exact same pixel values
lowerCamelCase_ =vis_processors['''eval'''](__snake_case ).unsqueeze(0 ).to(__snake_case )
lowerCamelCase_ =inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , __snake_case )
original_model.to(__snake_case )
hf_model.to(__snake_case )
with torch.no_grad():
if "vicuna" in model_name:
lowerCamelCase_ =original_model({'''image''': original_pixel_values, '''text_input''': [prompt]} ).logits
lowerCamelCase_ =hf_model(**__snake_case ).logits
else:
lowerCamelCase_ =original_model(
{'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']} ).logits
lowerCamelCase_ =tokenizer('''\n''' , return_tensors='''pt''' ).input_ids.to(__snake_case )
lowerCamelCase_ =label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
lowerCamelCase_ =hf_model(**__snake_case , labels=__snake_case ).logits
print('''First values of original logits:''' , original_logits[0, :3, :3] )
print('''First values of HF logits:''' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
lowerCamelCase_ =1e-4 if '''vicuna''' in model_name else 1e-5
assert torch.allclose(original_logits.to(logits.device ) , __snake_case , atol=__snake_case )
print('''Looks ok!''' )
print('''Generating with original model...''' )
lowerCamelCase_ =original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('''Generating with HF model...''' )
lowerCamelCase_ =hf_model.generate(
**__snake_case , do_sample=__snake_case , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
lowerCamelCase_ =2
print('''Original generation:''' , __snake_case )
lowerCamelCase_ =processor.batch_decode(__snake_case , skip_special_tokens=__snake_case )
lowerCamelCase_ =[text.strip() for text in output_text]
print('''HF generation:''' , __snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__snake_case )
hf_model.save_pretrained(__snake_case )
if push_to_hub:
processor.push_to_hub(F'''Salesforce/{model_name}''' )
hf_model.push_to_hub(F'''Salesforce/{model_name}''' )
if __name__ == "__main__":
a_ : Any = argparse.ArgumentParser()
a_ : Any = [
"""instructblip-vicuna-7b""",
"""instructblip-vicuna-13b""",
"""instructblip-flan-t5-xl""",
"""instructblip-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""instructblip-flan-t5-xl""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
a_ : str = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 75 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
a_ : Union[str, Any] = random.Random()
def a_ ( __snake_case : int , __snake_case : int=1.0 , __snake_case : Tuple=None , __snake_case : Union[str, Any]=None ) -> str:
"""simple docstring"""
if rng is None:
lowerCamelCase_ =global_rng
lowerCamelCase_ =[]
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self, lowerCAmelCase, lowerCAmelCase=7, lowerCAmelCase=400, lowerCAmelCase=2_000, lowerCAmelCase=24, lowerCAmelCase=24, lowerCAmelCase=0.0, lowerCAmelCase=16_000, lowerCAmelCase=True, lowerCAmelCase=True, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =min_seq_length
lowerCamelCase_ =max_seq_length
lowerCamelCase_ =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase_ =feature_size
lowerCamelCase_ =num_mel_bins
lowerCamelCase_ =padding_value
lowerCamelCase_ =sampling_rate
lowerCamelCase_ =return_attention_mask
lowerCamelCase_ =do_normalize
def lowercase__ ( self ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowercase__ ( self, lowerCAmelCase=False, lowerCAmelCase=False ):
"""simple docstring"""
def _flatten(lowerCAmelCase ):
return list(itertools.chain(*lowerCAmelCase ) )
if equal_length:
lowerCamelCase_ =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCamelCase_ =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Any =SpeechaTextFeatureExtractor if is_speech_available() else None
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextFeatureExtractionTester(self )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
self.assertTrue(np.all(np.mean(lowerCAmelCase, axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase, axis=0 ) - 1 ) < 1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]
# Test feature size
lowerCamelCase_ =feature_extractor(lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
lowerCamelCase_ =feature_extractor(speech_inputs[0], return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(np_speech_inputs[0], return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test batched
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase_ =[floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCamelCase_ =np.asarray(lowerCAmelCase )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding=lowerCAmelCase, max_length=lowerCAmelCase, return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, max_length=lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''max_length''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24) )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=16, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24) )
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =np.random.rand(100, 32 ).astype(np.floataa )
lowerCamelCase_ =np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
from datasets import load_dataset
lowerCamelCase_ =load_dataset('''hf-internal-testing/librispeech_asr_dummy''', '''clean''', split='''validation''' )
# automatic decoding with librispeech
lowerCamelCase_ =ds.sort('''id''' ).select(range(lowerCAmelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =np.array([
-1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
-1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
-1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
] )
# fmt: on
lowerCamelCase_ =self._load_datasamples(1 )
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape, (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30], lowerCAmelCase, atol=1e-4 ) )
| 75 | 1 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
lowerCamelCase_ =len(lowerCAmelCase ) - 1
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowerCamelCase_ =[]
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree, lowerCAmelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(lowerCAmelCase ), 5 ) == 1
return output_values
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowerCamelCase_ =self.basis_function(lowerCAmelCase )
lowerCamelCase_ =0.0
lowerCamelCase_ =0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowercase__ ( self, lowerCAmelCase = 0.0_1 ):
"""simple docstring"""
from matplotlib import pyplot as plt # type: ignore
lowerCamelCase_ =[] # x coordinates of points to plot
lowerCamelCase_ =[] # y coordinates of points to plot
lowerCamelCase_ =0.0
while t <= 1:
lowerCamelCase_ =self.bezier_curve_function(lowerCAmelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
lowerCamelCase_ =[i[0] for i in self.list_of_points]
lowerCamelCase_ =[i[1] for i in self.list_of_points]
plt.plot(
lowerCAmelCase, lowerCAmelCase, color='''blue''', label='''Curve of Degree ''' + str(self.degree ), )
plt.scatter(lowerCAmelCase, lowerCAmelCase, color='''red''', label='''Control Points''' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 75 |
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in table:
res += inp[i - 1]
return res
def a_ ( __snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
return data[1:] + data[0]
def a_ ( __snake_case : str , __snake_case : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def a_ ( __snake_case : Optional[Any] , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =int('''0b''' + data[0] + data[-1] , 2 )
lowerCamelCase_ =int('''0b''' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def a_ ( __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : int , __snake_case : Tuple , __snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =message[:4]
lowerCamelCase_ =message[4:]
lowerCamelCase_ =apply_table(__snake_case , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
lowerCamelCase_ =apply_sbox(__snake_case , temp[:4] ) # noqa: E741
lowerCamelCase_ =apply_sbox(__snake_case , temp[4:] )
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + l # noqa: E741
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + r
lowerCamelCase_ =apply_table(l + r , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
a_ : Any = input("""Enter 10 bit key: """)
a_ : Any = input("""Enter 8 bit message: """)
a_ : str = [6, 3, 7, 4, 8, 5, 10, 9]
a_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
a_ : str = [2, 4, 3, 1]
a_ : Optional[int] = [2, 6, 3, 1, 4, 8, 5, 7]
a_ : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
a_ : Union[str, Any] = [4, 1, 2, 3, 2, 3, 4, 1]
a_ : int = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
a_ : Any = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
a_ : List[Any] = apply_table(key, paa_table)
a_ : str = temp[:5]
a_ : Optional[Any] = temp[5:]
a_ : Tuple = left_shift(left)
a_ : Optional[Any] = left_shift(right)
a_ : str = apply_table(left + right, pa_table)
a_ : Optional[Any] = left_shift(left)
a_ : Tuple = left_shift(right)
a_ : Union[str, Any] = left_shift(left)
a_ : List[str] = left_shift(right)
a_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
a_ : Optional[int] = apply_table(message, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : str = temp[4:] + temp[:4]
a_ : List[str] = function(expansion, sa, sa, keya, temp)
a_ : Union[str, Any] = apply_table(temp, IP_inv)
print("""Cipher text is:""", CT)
# decryption
a_ : Optional[int] = apply_table(CT, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : int = temp[4:] + temp[:4]
a_ : int = function(expansion, sa, sa, keya, temp)
a_ : Optional[int] = apply_table(temp, IP_inv)
print("""Plain text after decypting is:""", PT)
| 75 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : str = logging.get_logger(__name__)
a_ : Union[str, Any] = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[Any] ='speech_to_text_2'
lowercase : Tuple =['past_key_values']
lowercase : Optional[int] ={'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self, lowerCAmelCase=10_000, lowerCAmelCase=6, lowerCAmelCase=2_048, lowerCAmelCase=4, lowerCAmelCase=0.0, lowerCAmelCase=True, lowerCAmelCase="relu", lowerCAmelCase=256, lowerCAmelCase=0.1, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=0.0_2, lowerCAmelCase=2, lowerCAmelCase=True, lowerCAmelCase=1, lowerCAmelCase=0, lowerCAmelCase=2, lowerCAmelCase=1_024, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =vocab_size
lowerCamelCase_ =d_model
lowerCamelCase_ =decoder_ffn_dim
lowerCamelCase_ =decoder_layers
lowerCamelCase_ =decoder_attention_heads
lowerCamelCase_ =dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =activation_function
lowerCamelCase_ =init_std
lowerCamelCase_ =decoder_layerdrop
lowerCamelCase_ =use_cache
lowerCamelCase_ =decoder_layers
lowerCamelCase_ =scale_embedding # scale factor will be sqrt(d_model) if True
lowerCamelCase_ =max_target_positions
super().__init__(
pad_token_id=lowerCAmelCase, bos_token_id=lowerCAmelCase, eos_token_id=lowerCAmelCase, decoder_start_token_id=lowerCAmelCase, **lowerCAmelCase, )
| 75 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
a_ : List[Any] = logging.get_logger(__name__)
a_ : Tuple = OrderedDict(
[
("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
("""beit""", """BeitFeatureExtractor"""),
("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
("""clap""", """ClapFeatureExtractor"""),
("""clip""", """CLIPFeatureExtractor"""),
("""clipseg""", """ViTFeatureExtractor"""),
("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
("""convnext""", """ConvNextFeatureExtractor"""),
("""cvt""", """ConvNextFeatureExtractor"""),
("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
("""data2vec-vision""", """BeitFeatureExtractor"""),
("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
("""deit""", """DeiTFeatureExtractor"""),
("""detr""", """DetrFeatureExtractor"""),
("""dinat""", """ViTFeatureExtractor"""),
("""donut-swin""", """DonutFeatureExtractor"""),
("""dpt""", """DPTFeatureExtractor"""),
("""encodec""", """EncodecFeatureExtractor"""),
("""flava""", """FlavaFeatureExtractor"""),
("""glpn""", """GLPNFeatureExtractor"""),
("""groupvit""", """CLIPFeatureExtractor"""),
("""hubert""", """Wav2Vec2FeatureExtractor"""),
("""imagegpt""", """ImageGPTFeatureExtractor"""),
("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
("""levit""", """LevitFeatureExtractor"""),
("""maskformer""", """MaskFormerFeatureExtractor"""),
("""mctct""", """MCTCTFeatureExtractor"""),
("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
("""mobilevit""", """MobileViTFeatureExtractor"""),
("""nat""", """ViTFeatureExtractor"""),
("""owlvit""", """OwlViTFeatureExtractor"""),
("""perceiver""", """PerceiverFeatureExtractor"""),
("""poolformer""", """PoolFormerFeatureExtractor"""),
("""regnet""", """ConvNextFeatureExtractor"""),
("""resnet""", """ConvNextFeatureExtractor"""),
("""segformer""", """SegformerFeatureExtractor"""),
("""sew""", """Wav2Vec2FeatureExtractor"""),
("""sew-d""", """Wav2Vec2FeatureExtractor"""),
("""speech_to_text""", """Speech2TextFeatureExtractor"""),
("""speecht5""", """SpeechT5FeatureExtractor"""),
("""swiftformer""", """ViTFeatureExtractor"""),
("""swin""", """ViTFeatureExtractor"""),
("""swinv2""", """ViTFeatureExtractor"""),
("""table-transformer""", """DetrFeatureExtractor"""),
("""timesformer""", """VideoMAEFeatureExtractor"""),
("""tvlt""", """TvltFeatureExtractor"""),
("""unispeech""", """Wav2Vec2FeatureExtractor"""),
("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
("""van""", """ConvNextFeatureExtractor"""),
("""videomae""", """VideoMAEFeatureExtractor"""),
("""vilt""", """ViltFeatureExtractor"""),
("""vit""", """ViTFeatureExtractor"""),
("""vit_mae""", """ViTFeatureExtractor"""),
("""vit_msn""", """ViTFeatureExtractor"""),
("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
("""wavlm""", """Wav2Vec2FeatureExtractor"""),
("""whisper""", """WhisperFeatureExtractor"""),
("""xclip""", """CLIPFeatureExtractor"""),
("""yolos""", """YolosFeatureExtractor"""),
]
)
a_ : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def a_ ( __snake_case : str ) -> Any:
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCamelCase_ =model_type_to_module_name(__snake_case )
lowerCamelCase_ =importlib.import_module(F'''.{module_name}''' , '''transformers.models''' )
try:
return getattr(__snake_case , __snake_case )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(__snake_case , '''__name__''' , __snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCamelCase_ =importlib.import_module('''transformers''' )
if hasattr(__snake_case , __snake_case ):
return getattr(__snake_case , __snake_case )
return None
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =get_file_from_repo(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(__snake_case , encoding='''utf-8''' ) as reader:
return json.load(__snake_case )
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(lowerCAmelCase )
def lowercase__ ( cls, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =kwargs.pop('''config''', lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''trust_remote_code''', lowerCAmelCase )
lowerCamelCase_ =True
lowerCamelCase_, lowerCamelCase_ =FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =config_dict.get('''feature_extractor_type''', lowerCAmelCase )
lowerCamelCase_ =None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''', {} ):
lowerCamelCase_ =config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =AutoConfig.from_pretrained(lowerCAmelCase, **lowerCAmelCase )
# It could be in `config.feature_extractor_type``
lowerCamelCase_ =getattr(lowerCAmelCase, '''feature_extractor_type''', lowerCAmelCase )
if hasattr(lowerCAmelCase, '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
lowerCamelCase_ =config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
lowerCamelCase_ =feature_extractor_class_from_name(lowerCAmelCase )
lowerCamelCase_ =feature_extractor_auto_map is not None
lowerCamelCase_ =feature_extractor_class is not None or type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
lowerCamelCase_ =resolve_trust_remote_code(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
if has_remote_code and trust_remote_code:
lowerCamelCase_ =get_class_from_dynamic_module(
lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''code_revision''', lowerCAmelCase )
if os.path.isdir(lowerCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
lowerCamelCase_ =FEATURE_EXTRACTOR_MAPPING[type(lowerCAmelCase )]
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
raise ValueError(
f'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
f'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase, lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =8
# DPR tok
lowerCamelCase_ =[
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowerCamelCase_ =os.path.join(self.tmpdirname, '''dpr_tokenizer''' )
os.makedirs(lowerCAmelCase, exist_ok=lowerCAmelCase )
lowerCamelCase_ =os.path.join(lowerCAmelCase, DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
lowerCamelCase_ =[
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
lowerCamelCase_ ={'''unk_token''': '''<unk>'''}
lowerCamelCase_ =os.path.join(self.tmpdirname, '''bart_tokenizer''' )
os.makedirs(lowerCAmelCase, exist_ok=lowerCAmelCase )
lowerCamelCase_ =os.path.join(lowerCAmelCase, BART_VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(lowerCAmelCase, BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
with open(self.merges_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowerCAmelCase ) )
def lowercase__ ( self ):
"""simple docstring"""
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname, '''dpr_tokenizer''' ) )
def lowercase__ ( self ):
"""simple docstring"""
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname, '''dpr_tokenizer''' ) )
def lowercase__ ( self ):
"""simple docstring"""
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname, '''bart_tokenizer''' ) )
def lowercase__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''', string_factory='''Flat''', metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_dataset()
lowerCamelCase_ =RagConfig(
retrieval_vector_size=self.retrieval_vector_size, question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict(), )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
lowerCamelCase_ =dataset
lowerCamelCase_ =RagRetriever(
lowerCAmelCase, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), )
return retriever
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_dataset()
lowerCamelCase_ =RagConfig(
retrieval_vector_size=self.retrieval_vector_size, question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict(), index_name='''custom''', )
if from_disk:
lowerCamelCase_ =os.path.join(self.tmpdirname, '''dataset''' )
lowerCamelCase_ =os.path.join(self.tmpdirname, '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname, '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname, '''dataset''' ) )
del dataset
lowerCamelCase_ =RagRetriever(
lowerCAmelCase, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), )
else:
lowerCamelCase_ =RagRetriever(
lowerCAmelCase, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), index=CustomHFIndex(config.retrieval_vector_size, lowerCAmelCase ), )
return retriever
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''', string_factory='''Flat''', metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCamelCase_ =os.path.join(self.tmpdirname, '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''', index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''], open(index_file_name + '''.index_meta.dpr''', '''wb''' ) )
lowerCamelCase_ =os.path.join(self.tmpdirname, '''psgs_w100.tsv.pkl''' )
lowerCamelCase_ ={sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(lowerCAmelCase, open(lowerCAmelCase, '''wb''' ) )
lowerCamelCase_ =RagConfig(
retrieval_vector_size=self.retrieval_vector_size, question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict(), index_name='''legacy''', index_path=self.tmpdirname, )
lowerCamelCase_ =RagRetriever(
lowerCAmelCase, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =1
lowerCamelCase_ =self.get_dummy_canonical_hf_index_retriever()
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =retriever.retrieve(lowerCAmelCase, n_docs=lowerCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCAmelCase ), 2 )
self.assertEqual(sorted(doc_dicts[0] ), ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ), lowerCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0], '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0], '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
lowerCamelCase_ =self.get_dummy_dataset()
retriever.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =RagRetriever.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_ =retriever.retrieve(lowerCAmelCase, n_docs=1 )
self.assertTrue(out is not None )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =1
lowerCamelCase_ =self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase )
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =retriever.retrieve(lowerCAmelCase, n_docs=lowerCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCAmelCase ), 2 )
self.assertEqual(sorted(doc_dicts[0] ), ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ), lowerCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0], '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0], '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =RagRetriever.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_ =retriever.retrieve(lowerCAmelCase, n_docs=1 )
self.assertTrue(out is not None )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =1
lowerCamelCase_ =self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase )
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =retriever.retrieve(lowerCAmelCase, n_docs=lowerCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCAmelCase ), 2 )
self.assertEqual(sorted(doc_dicts[0] ), ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ), lowerCAmelCase )
self.assertEqual(doc_dicts[0]['''id'''][0], '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0], '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =RagRetriever.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_ =retriever.retrieve(lowerCAmelCase, n_docs=1 )
self.assertTrue(out is not None )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =1
lowerCamelCase_ =self.get_dummy_legacy_index_retriever()
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =retriever.retrieve(lowerCAmelCase, n_docs=lowerCAmelCase )
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCAmelCase ), 2 )
self.assertEqual(sorted(doc_dicts[0] ), ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ), lowerCAmelCase )
self.assertEqual(doc_dicts[0]['''text'''][0], '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0], '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =RagRetriever.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_ =retriever.retrieve(lowerCAmelCase, n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =1
lowerCamelCase_ =self.get_dummy_canonical_hf_index_retriever()
lowerCamelCase_ =[[5, 7], [10, 11]]
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_ =retriever(lowerCAmelCase, lowerCAmelCase, prefix=retriever.config.generator.prefix, n_docs=lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =(
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase, np.ndarray )
lowerCamelCase_ =retriever(
lowerCAmelCase, lowerCAmelCase, prefix=retriever.config.generator.prefix, n_docs=lowerCAmelCase, return_tensors='''pt''', )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowerCAmelCase, torch.Tensor )
self.assertIsInstance(lowerCAmelCase, torch.Tensor )
self.assertIsInstance(lowerCAmelCase, torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dpr_ctx_encoder_tokenizer()
lowerCamelCase_ =1
lowerCamelCase_ =self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase )
retriever.set_ctx_encoder_tokenizer(lowerCAmelCase )
lowerCamelCase_ =[[5, 7], [10, 11]]
lowerCamelCase_ =np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )], dtype=np.floataa )
lowerCamelCase_ =retriever(lowerCAmelCase, lowerCAmelCase, prefix=retriever.config.generator.prefix, n_docs=lowerCAmelCase )
self.assertEqual(
len(lowerCAmelCase ), 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ), lowerCAmelCase ) # check for doc token related keys in dictionary.
| 75 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ : Optional[int] = logging.getLogger(__name__)
def a_ ( __snake_case : Union[str, Any] , __snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ =np.argmax(__snake_case , axis=1 )
return np.sum(outputs == labels )
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
with open(__snake_case , encoding='''utf_8''' ) as f:
lowerCamelCase_ =csv.reader(__snake_case )
lowerCamelCase_ =[]
next(__snake_case ) # skip the first line
for line in tqdm(__snake_case ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a_ ( __snake_case : str , __snake_case : Dict , __snake_case : List[str] , __snake_case : List[str] , __snake_case : List[Any] , __snake_case : Dict ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
for dataset in encoded_datasets:
lowerCamelCase_ =len(__snake_case )
lowerCamelCase_ =np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch, 2) , dtype=np.intaa )
lowerCamelCase_ =np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__snake_case ):
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =mc_label
lowerCamelCase_ =(input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__snake_case ) for t in all_inputs ) )
return tensor_datasets
def a_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=__snake_case , default='''openai-gpt''' , help='''pretrained model name''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' , action='''store_true''' , help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' , default=__snake_case , type=__snake_case , required=__snake_case , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument('''--train_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--eval_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--seed''' , type=__snake_case , default=42 )
parser.add_argument('''--num_train_epochs''' , type=__snake_case , default=3 )
parser.add_argument('''--train_batch_size''' , type=__snake_case , default=8 )
parser.add_argument('''--eval_batch_size''' , type=__snake_case , default=16 )
parser.add_argument('''--adam_epsilon''' , default=1e-8 , type=__snake_case , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' , type=__snake_case , default=1 )
parser.add_argument(
'''--max_steps''' , default=-1 , type=__snake_case , help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) , )
parser.add_argument(
'''--gradient_accumulation_steps''' , type=__snake_case , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--learning_rate''' , type=__snake_case , default=6.25e-5 )
parser.add_argument('''--warmup_steps''' , default=0 , type=__snake_case , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' , type=__snake_case , default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' , type=__snake_case , default=0.0_1 )
parser.add_argument('''--lm_coef''' , type=__snake_case , default=0.9 )
parser.add_argument('''--n_valid''' , type=__snake_case , default=374 )
parser.add_argument('''--server_ip''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
lowerCamelCase_ =parser.parse_args()
print(__snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowerCamelCase_ =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
lowerCamelCase_ =torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(__snake_case , __snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowerCamelCase_ =['''_start_''', '''_delimiter_''', '''_classify_''']
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(__snake_case )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(__snake_case )
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(__snake_case ) )
model.to(__snake_case )
# Load and encode the datasets
def tokenize_and_encode(__snake_case : Union[str, Any] ):
if isinstance(__snake_case , __snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__snake_case ) )
elif isinstance(__snake_case , __snake_case ):
return obj
return [tokenize_and_encode(__snake_case ) for o in obj]
logger.info('''Encoding dataset...''' )
lowerCamelCase_ =load_rocstories_dataset(args.train_dataset )
lowerCamelCase_ =load_rocstories_dataset(args.eval_dataset )
lowerCamelCase_ =(train_dataset, eval_dataset)
lowerCamelCase_ =tokenize_and_encode(__snake_case )
# Compute the max input length for the Transformer
lowerCamelCase_ =model.config.n_positions // 2 - 2
lowerCamelCase_ =max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowerCamelCase_ =min(__snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowerCamelCase_ =pre_process_datasets(__snake_case , __snake_case , __snake_case , *__snake_case )
lowerCamelCase_, lowerCamelCase_ =tensor_datasets[0], tensor_datasets[1]
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =RandomSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.train_batch_size )
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =SequentialSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowerCamelCase_ =args.max_steps
lowerCamelCase_ =args.max_steps // (len(__snake_case ) // args.gradient_accumulation_steps) + 1
else:
lowerCamelCase_ =len(__snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
lowerCamelCase_ =list(model.named_parameters() )
lowerCamelCase_ =['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
lowerCamelCase_ =[
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
lowerCamelCase_ =AdamW(__snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
lowerCamelCase_ =get_linear_schedule_with_warmup(
__snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=__snake_case )
if args.do_train:
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='''Epoch''' ):
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =tqdm(__snake_case , desc='''Training''' )
for step, batch in enumerate(__snake_case ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
lowerCamelCase_ =model(__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowerCamelCase_ =(
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowerCamelCase_ ='''Training loss: {:.2e} lr: {:.2e}'''.format(__snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowerCamelCase_ =model.module if hasattr(__snake_case , '''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
torch.save(model_to_save.state_dict() , __snake_case )
model_to_save.config.to_json_file(__snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(__snake_case )
if args.do_eval:
model.eval()
lowerCamelCase_, lowerCamelCase_ =0, 0
lowerCamelCase_, lowerCamelCase_ =0, 0
for batch in tqdm(__snake_case , desc='''Evaluating''' ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
with torch.no_grad():
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =model(
__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =mc_logits.detach().cpu().numpy()
lowerCamelCase_ =mc_labels.to('''cpu''' ).numpy()
lowerCamelCase_ =accuracy(__snake_case , __snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowerCamelCase_ =eval_loss / nb_eval_steps
lowerCamelCase_ =eval_accuracy / nb_eval_examples
lowerCamelCase_ =tr_loss / nb_tr_steps if args.do_train else None
lowerCamelCase_ ={'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
lowerCamelCase_ =os.path.join(args.output_dir , '''eval_results.txt''' )
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' , __snake_case , str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 75 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_50, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_00, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_00, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split(), encoding='''utf-8''', check=lowerCAmelCase, )
assert hasattr(self, '''env''' )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
# distributed data settings
lowerCamelCase_ ={'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None
# creates estimator
return HuggingFace(
entry_point=self.script, source_dir=self.env.test_path, role=self.env.role, image_uri=self.env.image_uri, base_job_name=lowerCAmelCase, instance_count=lowerCAmelCase, instance_type=self.instance_type, debugger_hook_config=lowerCAmelCase, hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}, metric_definitions=self.env.metric_definitions, distribution=lowerCAmelCase, py_version='''py36''', )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
TrainingJobAnalytics(lowerCAmelCase ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''' )
@parameterized.expand([(2,)] )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.create_estimator(lowerCAmelCase )
# run training
estimator.fit()
# result dataframe
lowerCamelCase_ =TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCamelCase_ =list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
lowerCamelCase_ =list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCamelCase_ =(
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''', 999_999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'''{estimator.latest_training_job.name}.json''', '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}, lowerCAmelCase )
| 75 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ =''''''
lowerCamelCase_ =[]
lowerCamelCase_ =0
lowerCamelCase_ =256
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =cva.imread(lowerCAmelCase, 0 )
lowerCamelCase_ =copy.deepcopy(self.img )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =plt.hist(self.img.ravel(), 256, [0, 256], label='''x''' )
lowerCamelCase_ =np.sum(lowerCAmelCase )
for i in range(len(lowerCAmelCase ) ):
lowerCamelCase_ =x[i] / self.k
self.sk += prk
lowerCamelCase_ =(self.L - 1) * self.sk
if self.rem != 0:
lowerCamelCase_ =int(last % last )
lowerCamelCase_ =int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowerCAmelCase )
lowerCamelCase_ =int(np.ma.count(self.img ) / self.img[1].size )
lowerCamelCase_ =self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCamelCase_ =self.img[j][i]
if num != self.last_list[num]:
lowerCamelCase_ =self.last_list[num]
cva.imwrite('''output_data/output.jpg''', self.img )
def lowercase__ ( self ):
"""simple docstring"""
plt.hist(self.img.ravel(), 256, [0, 256] )
def lowercase__ ( self ):
"""simple docstring"""
cva.imshow('''Output-Image''', self.img )
cva.imshow('''Input-Image''', self.original_image )
cva.waitKey(5_000 )
cva.destroyAllWindows()
if __name__ == "__main__":
a_ : str = os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
a_ : Optional[Any] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 75 | 1 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def a_ ( __snake_case : str , __snake_case : complex , __snake_case : str = "x" , __snake_case : float = 10**-10 , __snake_case : int = 1 , ) -> complex:
"""simple docstring"""
lowerCamelCase_ =symbols(__snake_case )
lowerCamelCase_ =lambdify(__snake_case , __snake_case )
lowerCamelCase_ =lambdify(__snake_case , diff(__snake_case , __snake_case ) )
lowerCamelCase_ =starting_point
while True:
if diff_function(__snake_case ) != 0:
lowerCamelCase_ =prev_guess - multiplicity * func(__snake_case ) / diff_function(
__snake_case )
else:
raise ZeroDivisionError('''Could not find root''' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
lowerCamelCase_ =next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")
# Find root of polynomial
# Find fourth Root of 5
print(F"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")
# Find value of e
print(
"""The root of log(y) - 1 = 0 is """,
F"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
)
# Exponential Roots
print(
"""The root of exp(x) - 1 = 0 is""",
F"""{newton_raphson("exp(x) - 1", 10, precision=0.0_05)}""",
)
# Find root of cos(x)
print(F"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")
| 75 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Any = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
# No specific FOR_XXX available yet
def __call__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return super().__call__(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ={}
if "candidate_labels" in kwargs:
lowerCamelCase_ =kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
lowerCamelCase_ =kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(lowerCAmelCase, lowerCAmelCase ):
if audio.startswith('''http://''' ) or audio.startswith('''https://''' ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
lowerCamelCase_ =requests.get(lowerCAmelCase ).content
else:
with open(lowerCAmelCase, '''rb''' ) as f:
lowerCamelCase_ =f.read()
if isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =ffmpeg_read(lowerCAmelCase, self.feature_extractor.sampling_rate )
if not isinstance(lowerCAmelCase, np.ndarray ):
raise ValueError('''We expect a numpy ndarray as input''' )
if len(audio.shape ) != 1:
raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''' )
lowerCamelCase_ =self.feature_extractor(
[audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='''pt''' )
lowerCamelCase_ =candidate_labels
lowerCamelCase_ =[hypothesis_template.format(lowerCAmelCase ) for x in candidate_labels]
lowerCamelCase_ =self.tokenizer(lowerCAmelCase, return_tensors=self.framework, padding=lowerCAmelCase )
lowerCamelCase_ =[text_inputs]
return inputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_inputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0], lowerCAmelCase ):
lowerCamelCase_ =text_inputs[0]
else:
# Batching case.
lowerCamelCase_ =text_inputs[0][0]
lowerCamelCase_ =self.model(**lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ ={
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_audio,
}
return model_outputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_outputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_outputs['''logits'''][0]
if self.framework == "pt":
lowerCamelCase_ =logits.softmax(dim=0 )
lowerCamelCase_ =probs.tolist()
else:
raise ValueError('''`tf` framework not supported.''' )
lowerCamelCase_ =[
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(lowerCAmelCase, lowerCAmelCase ), key=lambda lowerCAmelCase : -x[0] )
]
return result
| 75 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
a_ : Any = re.compile(R"""\s+""")
def a_ ( __snake_case : Tuple ) -> List[str]:
"""simple docstring"""
return {"hash": hashlib.mda(re.sub(__snake_case , '''''' , example['''content'''] ).encode('''utf-8''' ) ).hexdigest()}
def a_ ( __snake_case : Any ) -> str:
"""simple docstring"""
lowerCamelCase_ =[len(__snake_case ) for line in example['''content'''].splitlines()]
return {"line_mean": np.mean(__snake_case ), "line_max": max(__snake_case )}
def a_ ( __snake_case : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =np.mean([c.isalnum() for c in example['''content''']] )
return {"alpha_frac": alpha_frac}
def a_ ( __snake_case : str , __snake_case : int ) -> Optional[Any]:
"""simple docstring"""
if example["hash"] in uniques:
uniques.remove(example['''hash'''] )
return True
else:
return False
def a_ ( __snake_case : Dict , __snake_case : List[Any]=5 ) -> int:
"""simple docstring"""
lowerCamelCase_ =['''auto-generated''', '''autogenerated''', '''automatically generated''']
lowerCamelCase_ =example['''content'''].splitlines()
for _, line in zip(range(__snake_case ) , __snake_case ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def a_ ( __snake_case : List[Any] , __snake_case : str=5 , __snake_case : List[Any]=0.0_5 ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =['''unit tests''', '''test file''', '''configuration file''']
lowerCamelCase_ =example['''content'''].splitlines()
lowerCamelCase_ =0
lowerCamelCase_ =0
# first test
for _, line in zip(range(__snake_case ) , __snake_case ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
lowerCamelCase_ =example['''content'''].count('''\n''' )
lowerCamelCase_ =int(coeff * nlines )
for line in lines:
count_config += line.lower().count('''config''' )
count_test += line.lower().count('''test''' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def a_ ( __snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =['''def ''', '''class ''', '''for ''', '''while ''']
lowerCamelCase_ =example['''content'''].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def a_ ( __snake_case : str , __snake_case : Optional[int]=4 ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =example['''content'''].splitlines()
lowerCamelCase_ =0
for line in lines:
counter += line.lower().count('''=''' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def a_ ( __snake_case : Dict ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =tokenizer(example['''content'''] , truncation=__snake_case )['''input_ids''']
lowerCamelCase_ =len(example['''content'''] ) / len(__snake_case )
return {"ratio": ratio}
def a_ ( __snake_case : Any ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ ={}
results.update(get_hash(__snake_case ) )
results.update(line_stats(__snake_case ) )
results.update(alpha_stats(__snake_case ) )
results.update(char_token_ratio(__snake_case ) )
results.update(is_autogenerated(__snake_case ) )
results.update(is_config_or_test(__snake_case ) )
results.update(has_no_keywords(__snake_case ) )
results.update(has_few_assignments(__snake_case ) )
return results
def a_ ( __snake_case : List[str] , __snake_case : int , __snake_case : List[str] ) -> Dict:
"""simple docstring"""
if not check_uniques(__snake_case , __snake_case ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def a_ ( __snake_case : List[str] ) -> List[str]:
"""simple docstring"""
with open(__snake_case , '''rb''' ) as f_in:
with gzip.open(str(__snake_case ) + '''.gz''' , '''wb''' , compresslevel=6 ) as f_out:
shutil.copyfileobj(__snake_case , __snake_case )
os.unlink(__snake_case )
# Settings
a_ : List[Any] = HfArgumentParser(PreprocessingArguments)
a_ : int = parser.parse_args()
if args.num_workers is None:
a_ : List[str] = multiprocessing.cpu_count()
a_ : Optional[Any] = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
a_ : Any = time.time()
a_ : str = load_dataset(args.dataset_name, split="""train""")
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
a_ : Tuple = time.time()
a_ : Any = ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
a_ : List[Any] = set(ds.unique("""hash"""))
a_ : str = len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
a_ : Tuple = time.time()
a_ : int = ds.filter(filter, fn_kwargs={"""uniques""": uniques, """args""": args})
print(F"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(F"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
a_ : Union[str, Any] = time.time()
a_ , a_ : List[str] = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(F"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
a_ : str = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / """duplicate_clusters.json""", """w""") as f:
json.dump(duplicate_clusters, f)
a_ : Optional[Any] = output_dir / """data"""
data_dir.mkdir(exist_ok=True)
a_ : Any = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
a_ : int = str(data_dir / F"""file-{file_number+1:012}.json""")
a_ : Optional[Any] = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"""Time to save dataset: {time.time()-t_start:.2f}""")
| 75 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
a_ : Optional[Any] = 5
a_ : str = 10
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : int =SpeechaTextTokenizer
lowercase : int =False
lowercase : List[str] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =sp.SentencePieceProcessor()
spm_model.Load(lowerCAmelCase )
lowerCamelCase_ =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowerCAmelCase ) )]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =Path(self.tmpdirname )
save_json(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''<pad>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<s>''' )
self.assertEqual(vocab_keys[1], '''<pad>''' )
self.assertEqual(vocab_keys[-1], '''j''' )
self.assertEqual(len(lowerCAmelCase ), 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [289, 50, 14, 174, 386], )
lowerCamelCase_ =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.'''], )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ =tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.'''], )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase, model_name='''facebook/s2t-small-mustc-en-de-st''', revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''', )
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Tuple ='valhalla/s2t_mustc_multilinguial_medium'
lowercase : Dict ='C\'est trop cool'
lowercase : str ='Esto es genial'
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''], 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''], 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''], 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''], 11 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 10_000 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertIn(lowerCAmelCase, self.tokenizer.all_special_ids )
lowerCamelCase_ =[ES_CODE, 4, 1_601, 47, 7_647, 2]
lowerCamelCase_ =self.tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
lowerCamelCase_ =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0], lowerCAmelCase )
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE] )
lowerCamelCase_ ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE] )
| 75 | 1 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[str] ='efficientformer'
def __init__( self, lowerCAmelCase = [3, 2, 6, 4], lowerCAmelCase = [48, 96, 224, 448], lowerCAmelCase = [True, True, True, True], lowerCAmelCase = 448, lowerCAmelCase = 32, lowerCAmelCase = 4, lowerCAmelCase = 7, lowerCAmelCase = 5, lowerCAmelCase = 8, lowerCAmelCase = 4, lowerCAmelCase = 0.0, lowerCAmelCase = 16, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 2, lowerCAmelCase = 1, lowerCAmelCase = 0.0, lowerCAmelCase = 1, lowerCAmelCase = True, lowerCAmelCase = True, lowerCAmelCase = 1e-5, lowerCAmelCase = "gelu", lowerCAmelCase = 0.0_2, lowerCAmelCase = 1e-12, lowerCAmelCase = 224, lowerCAmelCase = 1e-05, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =hidden_sizes
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =initializer_range
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =depths
lowerCamelCase_ =mlp_expansion_ratio
lowerCamelCase_ =downsamples
lowerCamelCase_ =dim
lowerCamelCase_ =key_dim
lowerCamelCase_ =attention_ratio
lowerCamelCase_ =resolution
lowerCamelCase_ =pool_size
lowerCamelCase_ =downsample_patch_size
lowerCamelCase_ =downsample_stride
lowerCamelCase_ =downsample_pad
lowerCamelCase_ =drop_path_rate
lowerCamelCase_ =num_metaad_blocks
lowerCamelCase_ =distillation
lowerCamelCase_ =use_layer_scale
lowerCamelCase_ =layer_scale_init_value
lowerCamelCase_ =image_size
lowerCamelCase_ =batch_norm_eps
| 75 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def a_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase_ ='''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
lowerCamelCase_ =Image.open(requests.get(__snake_case , stream=__snake_case ).raw ).convert('''RGB''' )
return image
def a_ ( __snake_case : Tuple ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''') )
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''') )
# fmt: on
return rename_keys
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =dct.pop(__snake_case )
lowerCamelCase_ =val
def a_ ( __snake_case : str , __snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''' )
lowerCamelCase_ =state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
lowerCamelCase_ =torch.cat((q_bias, torch.zeros_like(__snake_case , requires_grad=__snake_case ), v_bias) )
lowerCamelCase_ =qkv_bias
def a_ ( __snake_case : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ =364 if '''coco''' in model_name else 224
lowerCamelCase_ =InstructBlipVisionConfig(image_size=__snake_case ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
lowerCamelCase_ =TaConfig.from_pretrained('''google/flan-t5-xxl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf''' , vocab_size=3_2001 ).to_dict()
elif "vicuna-13b" in model_name:
lowerCamelCase_ =LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf''' , vocab_size=3_2001 ).to_dict()
else:
raise ValueError('''Model name not supported''' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
lowerCamelCase_ =InstructBlipQFormerConfig(vocab_size=3_0523 ).to_dict()
lowerCamelCase_ =InstructBlipConfig(vision_config=__snake_case , text_config=__snake_case , qformer_config=__snake_case )
return config, image_size
@torch.no_grad()
def a_ ( __snake_case : Optional[int] , __snake_case : str=None , __snake_case : List[Any]=False ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =AutoTokenizer.from_pretrained('''bert-base-uncased''' , truncation_side='''left''' )
qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''} )
if "t5" in model_name:
lowerCamelCase_ =TaTokenizerFast.from_pretrained('''google/flan-t5-xl''' , truncation_side='''left''' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
lowerCamelCase_ =LlamaTokenizerFast.from_pretrained(
'''huggyllama/llama-7b''' , truncation_side='''left''' , bos_token='''</s>''' , unk_token='''</s>''' )
tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''} )
lowerCamelCase_, lowerCamelCase_ =get_blipa_config(__snake_case )
lowerCamelCase_ =InstructBlipForConditionalGeneration(__snake_case ).eval()
lowerCamelCase_ ={
'''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
'''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
'''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
'''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
}
lowerCamelCase_, lowerCamelCase_ =model_name_to_original[model_name]
# load original model
print('''Loading original model...''' )
lowerCamelCase_ ='''cuda:1''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_ ='''cuda:2''' if torch.cuda.is_available() else '''cpu'''
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =load_model_and_preprocess(
name=__snake_case , model_type=__snake_case , is_eval=__snake_case , device=__snake_case )
original_model.eval()
print('''Done!''' )
# update state dict keys
lowerCamelCase_ =original_model.state_dict()
lowerCamelCase_ =create_rename_keys(__snake_case )
for src, dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
lowerCamelCase_ =state_dict.pop(__snake_case )
if key.startswith('''Qformer.bert''' ):
lowerCamelCase_ =key.replace('''Qformer.bert''' , '''qformer''' )
if "attention.self" in key:
lowerCamelCase_ =key.replace('''self''' , '''attention''' )
if "llm_proj" in key:
lowerCamelCase_ =key.replace('''llm_proj''' , '''language_projection''' )
if "t5_proj" in key:
lowerCamelCase_ =key.replace('''t5_proj''' , '''language_projection''' )
if key.startswith('''llm_model''' ):
lowerCamelCase_ =key.replace('''llm_model''' , '''language_model''' )
if key.startswith('''t5''' ):
lowerCamelCase_ =key.replace('''t5''' , '''language''' )
lowerCamelCase_ =val
# read in qv biases
read_in_q_v_bias(__snake_case , __snake_case )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(__snake_case , strict=__snake_case )
lowerCamelCase_ =load_demo_image()
lowerCamelCase_ ='''What is unusual about this image?'''
# create processor
lowerCamelCase_ =BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size} , image_mean=__snake_case , image_std=__snake_case )
lowerCamelCase_ =InstructBlipProcessor(
image_processor=__snake_case , tokenizer=__snake_case , qformer_tokenizer=__snake_case , )
lowerCamelCase_ =processor(images=__snake_case , text=__snake_case , return_tensors='''pt''' ).to(__snake_case )
# make sure processor creates exact same pixel values
lowerCamelCase_ =vis_processors['''eval'''](__snake_case ).unsqueeze(0 ).to(__snake_case )
lowerCamelCase_ =inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , __snake_case )
original_model.to(__snake_case )
hf_model.to(__snake_case )
with torch.no_grad():
if "vicuna" in model_name:
lowerCamelCase_ =original_model({'''image''': original_pixel_values, '''text_input''': [prompt]} ).logits
lowerCamelCase_ =hf_model(**__snake_case ).logits
else:
lowerCamelCase_ =original_model(
{'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']} ).logits
lowerCamelCase_ =tokenizer('''\n''' , return_tensors='''pt''' ).input_ids.to(__snake_case )
lowerCamelCase_ =label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
lowerCamelCase_ =hf_model(**__snake_case , labels=__snake_case ).logits
print('''First values of original logits:''' , original_logits[0, :3, :3] )
print('''First values of HF logits:''' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
lowerCamelCase_ =1e-4 if '''vicuna''' in model_name else 1e-5
assert torch.allclose(original_logits.to(logits.device ) , __snake_case , atol=__snake_case )
print('''Looks ok!''' )
print('''Generating with original model...''' )
lowerCamelCase_ =original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('''Generating with HF model...''' )
lowerCamelCase_ =hf_model.generate(
**__snake_case , do_sample=__snake_case , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
lowerCamelCase_ =2
print('''Original generation:''' , __snake_case )
lowerCamelCase_ =processor.batch_decode(__snake_case , skip_special_tokens=__snake_case )
lowerCamelCase_ =[text.strip() for text in output_text]
print('''HF generation:''' , __snake_case )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__snake_case )
hf_model.save_pretrained(__snake_case )
if push_to_hub:
processor.push_to_hub(F'''Salesforce/{model_name}''' )
hf_model.push_to_hub(F'''Salesforce/{model_name}''' )
if __name__ == "__main__":
a_ : Any = argparse.ArgumentParser()
a_ : Any = [
"""instructblip-vicuna-7b""",
"""instructblip-vicuna-13b""",
"""instructblip-flan-t5-xl""",
"""instructblip-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""instructblip-flan-t5-xl""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
a_ : str = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 75 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : str =UnCLIPImageVariationPipeline
lowercase : Optional[Any] =IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
lowercase : List[str] =IMAGE_VARIATION_BATCH_PARAMS
lowercase : Optional[int] =[
'generator',
'return_dict',
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
lowercase : int =False
@property
def lowercase__ ( self ):
"""simple docstring"""
return 32
@property
def lowercase__ ( self ):
"""simple docstring"""
return 32
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.time_input_dim
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowercase__ ( self ):
"""simple docstring"""
return 100
@property
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=self.text_embedder_hidden_size, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, )
return CLIPTextModelWithProjection(lowerCAmelCase )
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size, projection_dim=self.text_embedder_hidden_size, num_hidden_layers=5, num_attention_heads=4, image_size=32, intermediate_size=37, patch_size=1, )
return CLIPVisionModelWithProjection(lowerCAmelCase )
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ ={
'''clip_embeddings_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''cross_attention_dim''': self.cross_attention_dim,
}
lowerCamelCase_ =UnCLIPTextProjModel(**lowerCAmelCase )
return model
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ ={
'''sample_size''': 32,
# RGB in channels
'''in_channels''': 3,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 6,
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': '''identity''',
}
lowerCamelCase_ =UNetaDConditionModel(**lowerCAmelCase )
return model
@property
def lowercase__ ( self ):
"""simple docstring"""
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(1 )
lowerCamelCase_ =UNetaDModel(**self.dummy_super_res_kwargs )
return model
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.dummy_decoder
lowerCamelCase_ =self.dummy_text_proj
lowerCamelCase_ =self.dummy_text_encoder
lowerCamelCase_ =self.dummy_tokenizer
lowerCamelCase_ =self.dummy_super_res_first
lowerCamelCase_ =self.dummy_super_res_last
lowerCamelCase_ =UnCLIPScheduler(
variance_type='''learned_range''', prediction_type='''epsilon''', num_train_timesteps=1_000, )
lowerCamelCase_ =UnCLIPScheduler(
variance_type='''fixed_small_log''', prediction_type='''epsilon''', num_train_timesteps=1_000, )
lowerCamelCase_ =CLIPImageProcessor(crop_size=32, size=32 )
lowerCamelCase_ =self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0, lowerCAmelCase=True ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
if pil_image:
lowerCamelCase_ =input_image * 0.5 + 0.5
lowerCamelCase_ =input_image.clamp(0, 1 )
lowerCamelCase_ =input_image.cpu().permute(0, 2, 3, 1 ).float().numpy()
lowerCamelCase_ =DiffusionPipeline.numpy_to_pil(lowerCAmelCase )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase, pil_image=lowerCAmelCase )
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =output.images
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase, pil_image=lowerCAmelCase )
lowerCamelCase_ =pipe(
**lowerCAmelCase, return_dict=lowerCAmelCase, )[0]
lowerCamelCase_ =image[0, -3:, -3:, -1]
lowerCamelCase_ =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase_ =np.array(
[
0.9_9_9_7,
0.0_0_0_2,
0.9_9_9_7,
0.9_9_9_7,
0.9_9_6_9,
0.0_0_2_3,
0.9_9_9_7,
0.9_9_6_9,
0.9_9_7_0,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase, pil_image=lowerCAmelCase )
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =output.images
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase, pil_image=lowerCAmelCase )
lowerCamelCase_ =pipe(
**lowerCAmelCase, return_dict=lowerCAmelCase, )[0]
lowerCamelCase_ =image[0, -3:, -3:, -1]
lowerCamelCase_ =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase_ =np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase, pil_image=lowerCAmelCase )
lowerCamelCase_ =[
pipeline_inputs['''image'''],
pipeline_inputs['''image'''],
]
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =output.images
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase, pil_image=lowerCAmelCase )
lowerCamelCase_ =[
tuple_pipeline_inputs['''image'''],
tuple_pipeline_inputs['''image'''],
]
lowerCamelCase_ =pipe(
**lowerCAmelCase, return_dict=lowerCAmelCase, )[0]
lowerCamelCase_ =image[0, -3:, -3:, -1]
lowerCamelCase_ =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
lowerCamelCase_ =np.array(
[
0.9_9_9_7,
0.9_9_8_9,
0.0_0_0_8,
0.0_0_2_1,
0.9_9_6_0,
0.0_0_1_8,
0.0_0_1_4,
0.0_0_0_2,
0.9_9_3_3,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =torch.device('''cpu''' )
class __UpperCamelCase :
lowercase : Union[str, Any] =1
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(0 )
lowerCamelCase_ =pipe.decoder.dtype
lowerCamelCase_ =1
lowerCamelCase_ =(
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
lowerCamelCase_ =pipe.prepare_latents(
lowerCAmelCase, dtype=lowerCAmelCase, device=lowerCAmelCase, generator=lowerCAmelCase, latents=lowerCAmelCase, scheduler=DummyScheduler() )
lowerCamelCase_ =(
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
lowerCamelCase_ =pipe.prepare_latents(
lowerCAmelCase, dtype=lowerCAmelCase, device=lowerCAmelCase, generator=lowerCAmelCase, latents=lowerCAmelCase, scheduler=DummyScheduler() )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase, pil_image=lowerCAmelCase )
lowerCamelCase_ =pipe(
**lowerCAmelCase, decoder_latents=lowerCAmelCase, super_res_latents=lowerCAmelCase ).images
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase, pil_image=lowerCAmelCase )
# Don't pass image, instead pass embedding
lowerCamelCase_ =pipeline_inputs.pop('''image''' )
lowerCamelCase_ =pipe.image_encoder(lowerCAmelCase ).image_embeds
lowerCamelCase_ =pipe(
**lowerCAmelCase, decoder_latents=lowerCAmelCase, super_res_latents=lowerCAmelCase, image_embeddings=lowerCAmelCase, ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1e-4
@skip_mps
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =torch_device == '''cpu'''
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
lowerCamelCase_ =1e-2
self._test_attention_slicing_forward_pass(
test_max_difference=lowerCAmelCase, expected_max_diff=lowerCAmelCase )
@skip_mps
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =torch_device == '''cpu'''
lowerCamelCase_ =True
lowerCamelCase_ =[
'''decoder_num_inference_steps''',
'''super_res_num_inference_steps''',
]
self._test_inference_batch_single_identical(
test_max_difference=lowerCAmelCase, relax_max_difference=lowerCAmelCase, additional_params_copy_to_batched_inputs=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =[
'''decoder_num_inference_steps''',
'''super_res_num_inference_steps''',
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
lowerCamelCase_ =[2, 3]
self._test_inference_batch_consistent(
batch_sizes=lowerCAmelCase, additional_params_copy_to_batched_inputs=lowerCAmelCase, )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=lowerCAmelCase )
@skip_mps
def lowercase__ ( self ):
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowercase__ ( self ):
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def lowercase__ ( self ):
"""simple docstring"""
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png''' )
lowerCamelCase_ =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/unclip/karlo_v1_alpha_cat_variation_fp16.npy''' )
lowerCamelCase_ =UnCLIPImageVariationPipeline.from_pretrained(
'''kakaobrain/karlo-v1-alpha-image-variations''', torch_dtype=torch.floataa )
lowerCamelCase_ =pipeline.to(lowerCAmelCase )
pipeline.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =torch.Generator(device='''cpu''' ).manual_seed(0 )
lowerCamelCase_ =pipeline(
lowerCAmelCase, generator=lowerCAmelCase, output_type='''np''', )
lowerCamelCase_ =output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(lowerCAmelCase, lowerCAmelCase, 15 )
| 75 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return 0.0
def a_ ( __snake_case : np.ndarray , __snake_case : int ) -> tuple[int | float, int | float]:
"""simple docstring"""
lowerCamelCase_ =min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
lowerCamelCase_ =max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.abs(np.fft.fft(__snake_case ) )
lowerCamelCase_ =20 * np.logaa(__snake_case )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
lowerCamelCase_ =get_bounds(__snake_case , __snake_case )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(__snake_case )
plt.show()
def a_ ( __snake_case : FilterType , __snake_case : int ) -> None:
"""simple docstring"""
lowerCamelCase_ =512
lowerCamelCase_ =[1] + [0] * (size - 1)
lowerCamelCase_ =[filter_type.process(__snake_case ) for item in inputs]
lowerCamelCase_ =[0] * (samplerate - size) # zero-padding
outputs += filler
lowerCamelCase_ =np.angle(np.fft.fft(__snake_case ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(__snake_case , -2 * pi ) )
plt.show()
| 75 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
a_ : str = """2020.9.26"""
a_ : Tuple = """xcodz-dot, cclaus, dhruvmanila"""
def a_ ( __snake_case : float , __snake_case : float , __snake_case : float , __snake_case : float , __snake_case : float ) -> tuple[float, float]:
"""simple docstring"""
if not all(isinstance(__snake_case , (float, int) ) for val in locals().values() ):
lowerCamelCase_ =F'''Input values must either be float or int: {list(locals().values() )}'''
raise TypeError(__snake_case )
lowerCamelCase_ =((x * distance) / (z + distance)) * scale
lowerCamelCase_ =((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def a_ ( __snake_case : float , __snake_case : float , __snake_case : float , __snake_case : str , __snake_case : float ) -> tuple[float, float, float]:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
raise TypeError('''Axis must be a str''' )
lowerCamelCase_ =locals()
del input_variables["axis"]
if not all(isinstance(__snake_case , (float, int) ) for val in input_variables.values() ):
lowerCamelCase_ =(
'''Input values except axis must either be float or int: '''
F'''{list(input_variables.values() )}'''
)
raise TypeError(__snake_case )
lowerCamelCase_ =(angle % 360) / 450 * 180 / math.pi
if axis == "z":
lowerCamelCase_ =x * math.cos(__snake_case ) - y * math.sin(__snake_case )
lowerCamelCase_ =y * math.cos(__snake_case ) + x * math.sin(__snake_case )
lowerCamelCase_ =z
elif axis == "x":
lowerCamelCase_ =y * math.cos(__snake_case ) - z * math.sin(__snake_case )
lowerCamelCase_ =z * math.cos(__snake_case ) + y * math.sin(__snake_case )
lowerCamelCase_ =x
elif axis == "y":
lowerCamelCase_ =x * math.cos(__snake_case ) - z * math.sin(__snake_case )
lowerCamelCase_ =z * math.cos(__snake_case ) + x * math.sin(__snake_case )
lowerCamelCase_ =y
else:
raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")
| 75 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =FunnelTokenizer
lowercase : List[str] =FunnelTokenizerFast
lowercase : Union[str, Any] =True
lowercase : int =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =[
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return FunnelTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ='''UNwant\u00E9d,running'''
lowerCamelCase_ ='''unwanted, running'''
return input_text, output_text
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.tokenizer_class(self.vocab_file )
lowerCamelCase_ =tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(lowerCAmelCase, ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [7, 4, 5, 10, 8, 9] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
lowerCamelCase_ =tokenizer('''UNwant\u00E9d,running''' )
lowerCamelCase_ =len(inputs['''input_ids'''] ) - 1
self.assertListEqual(inputs['''token_type_ids'''], [2] + [0] * sentence_len )
lowerCamelCase_ =tokenizer('''UNwant\u00E9d,running''', '''UNwant\u00E9d,running''' )
self.assertListEqual(inputs['''token_type_ids'''], [2] + [0] * sentence_len + [1] * sentence_len )
| 75 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ : List[str] = get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Dict =PegasusTokenizer
lowercase : List[str] =PegasusTokenizerFast
lowercase : Any =True
lowercase : Tuple =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ =PegasusTokenizer(lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return ("This is a test", "This is a test")
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''</s>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<pad>''' )
self.assertEqual(vocab_keys[1], '''</s>''' )
self.assertEqual(vocab_keys[-1], '''v''' )
self.assertEqual(len(lowerCAmelCase ), 1_103 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_103 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCamelCase_ =self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCamelCase_ =(
'''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
''' </s> <pad> <pad> <pad>'''
)
lowerCamelCase_ =rust_tokenizer([raw_input_str], return_tensors=lowerCAmelCase, add_special_tokens=lowerCAmelCase ).input_ids[0]
lowerCamelCase_ =py_tokenizer([raw_input_str], return_tensors=lowerCAmelCase, add_special_tokens=lowerCAmelCase ).input_ids[0]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowerCamelCase_ ='''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
lowerCamelCase_ =[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
lowerCamelCase_ =tokenizer([raw_input_str], return_tensors=lowerCAmelCase ).input_ids[0]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96_103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_024
lowerCamelCase_ ='''To ensure a smooth flow of bank resolutions.'''
lowerCamelCase_ =[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
lowerCamelCase_ =tokenizer([raw_input_str], return_tensors=lowerCAmelCase ).input_ids[0]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =['''This is going to be way too long.''' * 150, '''short example''']
lowerCamelCase_ =['''not super long but more than 5 tokens''', '''tiny''']
lowerCamelCase_ =self._large_tokenizer(lowerCAmelCase, padding=lowerCAmelCase, truncation=lowerCAmelCase, return_tensors='''pt''' )
lowerCamelCase_ =self._large_tokenizer(
text_target=lowerCAmelCase, max_length=5, padding=lowerCAmelCase, truncation=lowerCAmelCase, return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 1_024)
assert batch.attention_mask.shape == (2, 1_024)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCAmelCase ) == 2 # input_ids, attention_mask.
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase, model_name='''google/bigbird-pegasus-large-arxiv''', revision='''ba85d0851d708441f91440d509690f1ab6353415''', )
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =PegasusTokenizer
lowercase : List[str] =PegasusTokenizerFast
lowercase : List[Any] =True
lowercase : Union[str, Any] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ =PegasusTokenizer(lowerCAmelCase, offset=0, mask_token_sent=lowerCAmelCase, mask_token='''[MASK]''' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return ("This is a test", "This is a test")
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCamelCase_ =self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCamelCase_ =(
'''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
''' <pad> <pad> <pad>'''
)
lowerCamelCase_ =rust_tokenizer([raw_input_str], return_tensors=lowerCAmelCase, add_special_tokens=lowerCAmelCase ).input_ids[0]
lowerCamelCase_ =py_tokenizer([raw_input_str], return_tensors=lowerCAmelCase, add_special_tokens=lowerCAmelCase ).input_ids[0]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =['''This is going to be way too long.''' * 1_000, '''short example''']
lowerCamelCase_ =['''not super long but more than 5 tokens''', '''tiny''']
lowerCamelCase_ =self._large_tokenizer(lowerCAmelCase, padding=lowerCAmelCase, truncation=lowerCAmelCase, return_tensors='''pt''' )
lowerCamelCase_ =self._large_tokenizer(
text_target=lowerCAmelCase, max_length=5, padding=lowerCAmelCase, truncation=lowerCAmelCase, return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 4_096)
assert batch.attention_mask.shape == (2, 4_096)
assert targets["input_ids"].shape == (2, 5)
assert len(lowerCAmelCase ) == 2 # input_ids, attention_mask.
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =(
'''This is an example string that is used to test the original TF implementation against the HF'''
''' implementation'''
)
lowerCamelCase_ =self._large_tokenizer(lowerCAmelCase ).input_ids
self.assertListEqual(
lowerCAmelCase, [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1], )
| 75 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def a_ ( __snake_case : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ =checkpoints.load_tax_checkpoint(__snake_case )
lowerCamelCase_ =flatten_dict(__snake_case )
return flax_params
def a_ ( __snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ ={}
lowerCamelCase_ ={
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCamelCase_ ={
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCamelCase_ ='''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =flax_dict[key]
lowerCamelCase_ ={}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCamelCase_ =torch.from_numpy(converted_dict[key].T )
else:
lowerCamelCase_ =torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Any=False , __snake_case : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =get_flax_param(__snake_case )
if not use_large:
lowerCamelCase_ =PixaStructVisionConfig()
lowerCamelCase_ =PixaStructTextConfig()
else:
lowerCamelCase_ =PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowerCamelCase_ =PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowerCamelCase_ =PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__snake_case )
lowerCamelCase_ =PixaStructForConditionalGeneration(__snake_case )
lowerCamelCase_ =rename_and_convert_flax_params(__snake_case )
model.load_state_dict(__snake_case )
lowerCamelCase_ =AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCamelCase_ =PixaStructImageProcessor()
lowerCamelCase_ =PixaStructProcessor(image_processor=__snake_case , tokenizer=__snake_case )
if use_large:
lowerCamelCase_ =4096
lowerCamelCase_ =True
# mkdir if needed
os.makedirs(__snake_case , exist_ok=__snake_case )
model.save_pretrained(__snake_case )
processor.save_pretrained(__snake_case )
print('''Model saved in {}'''.format(__snake_case ) )
if __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
a_ : Tuple = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 75 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
super().__init__()
self.register_modules(unet=lowerCAmelCase, scheduler=lowerCAmelCase )
def __call__( self ):
"""simple docstring"""
lowerCamelCase_ =torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), )
lowerCamelCase_ =1
lowerCamelCase_ =self.unet(lowerCAmelCase, lowerCAmelCase ).sample
lowerCamelCase_ =self.scheduler.step(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ).prev_sample
lowerCamelCase_ =scheduler_output - scheduler_output + torch.ones_like(lowerCAmelCase )
return result
| 75 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a_ : Union[str, Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Tuple =['pixel_values']
def __init__( self, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = True, lowerCAmelCase = 1 / 255, lowerCAmelCase = True, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = True, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =size if size is not None else {'''shortest_edge''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
lowerCamelCase_ =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase, param_name='''crop_size''' )
lowerCamelCase_ =do_resize
lowerCamelCase_ =size
lowerCamelCase_ =resample
lowerCamelCase_ =do_center_crop
lowerCamelCase_ =crop_size
lowerCamelCase_ =do_rescale
lowerCamelCase_ =rescale_factor
lowerCamelCase_ =do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCamelCase_ =image_std if image_std is not None else OPENAI_CLIP_STD
lowerCamelCase_ =do_convert_rgb
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = PILImageResampling.BICUBIC, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase, default_to_square=lowerCAmelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowerCamelCase_ =get_resize_output_image_size(lowerCAmelCase, size=size['''shortest_edge'''], default_to_square=lowerCAmelCase )
return resize(lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =get_size_dict(lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowerCAmelCase, size=(size['''height'''], size['''width''']), data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return rescale(lowerCAmelCase, scale=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
return normalize(lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase, data_format=lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = None, lowerCAmelCase = ChannelDimension.FIRST, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ =size if size is not None else self.size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =resample if resample is not None else self.resample
lowerCamelCase_ =do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase_ =crop_size if crop_size is not None else self.crop_size
lowerCamelCase_ =get_size_dict(lowerCAmelCase, param_name='''crop_size''', default_to_square=lowerCAmelCase )
lowerCamelCase_ =do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ =rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ =do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ =image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ =image_std if image_std is not None else self.image_std
lowerCamelCase_ =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase_ =make_list_of_images(lowerCAmelCase )
if not valid_images(lowerCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase_ =[convert_to_rgb(lowerCAmelCase ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase_ =[to_numpy_array(lowerCAmelCase ) for image in images]
if do_resize:
lowerCamelCase_ =[self.resize(image=lowerCAmelCase, size=lowerCAmelCase, resample=lowerCAmelCase ) for image in images]
if do_center_crop:
lowerCamelCase_ =[self.center_crop(image=lowerCAmelCase, size=lowerCAmelCase ) for image in images]
if do_rescale:
lowerCamelCase_ =[self.rescale(image=lowerCAmelCase, scale=lowerCAmelCase ) for image in images]
if do_normalize:
lowerCamelCase_ =[self.normalize(image=lowerCAmelCase, mean=lowerCAmelCase, std=lowerCAmelCase ) for image in images]
lowerCamelCase_ =[to_channel_dimension_format(lowerCAmelCase, lowerCAmelCase ) for image in images]
lowerCamelCase_ ={'''pixel_values''': images}
return BatchFeature(data=lowerCAmelCase, tensor_type=lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
def a_ ( __snake_case : int ) -> int:
"""simple docstring"""
lowerCamelCase_ =[[0 for _ in range(__snake_case )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCamelCase_ =1
for n in range(m + 1 ):
for k in range(1 , __snake_case ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
a_ : str = int(input("""Enter a number: """).strip())
print(partition(n))
except ValueError:
print("""Please enter a number.""")
else:
try:
a_ : Any = int(sys.argv[1])
print(partition(n))
except ValueError:
print("""Please pass a number.""")
| 75 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : str , __snake_case : list[str] | None = None , __snake_case : dict[str, float] | None = None , __snake_case : bool = False , ) -> tuple[int, float, str]:
"""simple docstring"""
lowerCamelCase_ =cipher_alphabet or [chr(__snake_case ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
lowerCamelCase_ ={
'''a''': 0.0_8_4_9_7,
'''b''': 0.0_1_4_9_2,
'''c''': 0.0_2_2_0_2,
'''d''': 0.0_4_2_5_3,
'''e''': 0.1_1_1_6_2,
'''f''': 0.0_2_2_2_8,
'''g''': 0.0_2_0_1_5,
'''h''': 0.0_6_0_9_4,
'''i''': 0.0_7_5_4_6,
'''j''': 0.0_0_1_5_3,
'''k''': 0.0_1_2_9_2,
'''l''': 0.0_4_0_2_5,
'''m''': 0.0_2_4_0_6,
'''n''': 0.0_6_7_4_9,
'''o''': 0.0_7_5_0_7,
'''p''': 0.0_1_9_2_9,
'''q''': 0.0_0_0_9_5,
'''r''': 0.0_7_5_8_7,
'''s''': 0.0_6_3_2_7,
'''t''': 0.0_9_3_5_6,
'''u''': 0.0_2_7_5_8,
'''v''': 0.0_0_9_7_8,
'''w''': 0.0_2_5_6_0,
'''x''': 0.0_0_1_5_0,
'''y''': 0.0_1_9_9_4,
'''z''': 0.0_0_0_7_7,
}
else:
# Custom frequencies dictionary
lowerCamelCase_ =frequencies_dict
if not case_sensitive:
lowerCamelCase_ =ciphertext.lower()
# Chi squared statistic values
lowerCamelCase_ ={}
# cycle through all of the shifts
for shift in range(len(__snake_case ) ):
lowerCamelCase_ =''''''
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
lowerCamelCase_ =(alphabet_letters.index(letter.lower() ) - shift) % len(
__snake_case )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
lowerCamelCase_ =0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
lowerCamelCase_ =letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.lower().count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
lowerCamelCase_ =decrypted_with_shift.count(__snake_case )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
lowerCamelCase_ =frequencies[letter] * occurrences
# Complete the chi squared statistic formula
lowerCamelCase_ =((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
lowerCamelCase_ =(
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(__snake_case : int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
lowerCamelCase_ =min(
__snake_case , key=__snake_case , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
lowerCamelCase_
), (
lowerCamelCase_
),
) =chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 75 | 1 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def a_ ( ) -> Union[str, Any]:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
lowerCamelCase_ ='''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching , '''os.path.join''' , __snake_case ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def a_ ( ) -> int:
"""simple docstring"""
assert _test_patching.open is open
lowerCamelCase_ ='''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , '''open''' , __snake_case ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def a_ ( ) -> Optional[int]:
"""simple docstring"""
# pandas.read_csv is not present in _test_patching
lowerCamelCase_ ='''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching , '''pandas.read_csv''' , __snake_case ):
pass
def a_ ( ) -> List[str]:
"""simple docstring"""
# builtin should always be mocked even if they're not in the globals
# in case they're loaded at one point
lowerCamelCase_ ='''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , '''len''' , __snake_case ) is None
with patch_submodule(_test_patching , '''len''' , __snake_case ):
assert _test_patching.len is mock
assert _test_patching.len is len
def a_ ( ) -> int:
"""simple docstring"""
lowerCamelCase_ ='''__test_patch_submodule_start_and_stop_mock__'''
lowerCamelCase_ =patch_submodule(_test_patching , '''open''' , __snake_case )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def a_ ( ) -> List[Any]:
"""simple docstring"""
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
lowerCamelCase_ ='''__test_patch_submodule_successive_join__'''
lowerCamelCase_ ='''__test_patch_submodule_successive_dirname__'''
lowerCamelCase_ ='''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , '''os.path.join''' , __snake_case ):
with patch_submodule(_test_patching , '''os.rename''' , __snake_case ):
with patch_submodule(_test_patching , '''os.path.dirname''' , __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , '''os.rename''' , __snake_case ):
with patch_submodule(_test_patching , '''os.path.join''' , __snake_case ):
with patch_submodule(_test_patching , '''os.path.dirname''' , __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def a_ ( ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ ='''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching , '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''' , __snake_case ):
pass
with patch_submodule(_test_patching , '''os.__attribute_that_doesn_exist__''' , __snake_case ):
pass
| 75 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
a_ : List[Any] = (
"""https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)
a_ : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def a_ ( ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ ='''https://pypi.org/pypi/diffusers/json'''
lowerCamelCase_ =json.loads(request.urlopen(__snake_case ).read() )['''releases'''].keys()
return sorted(__snake_case , key=lambda __snake_case : version.Version(__snake_case ) )
def a_ ( ) -> str:
"""simple docstring"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__snake_case )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase_ =Path(__snake_case ) / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def a_ ( __snake_case : Union[str, os.PathLike] ) -> List[str]:
"""simple docstring"""
init_hf_modules()
lowerCamelCase_ =Path(__snake_case ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase_ =dynamic_module_path / '''__init__.py'''
if not init_path.exists():
init_path.touch()
def a_ ( __snake_case : Tuple ) -> List[str]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowerCamelCase_ =f.read()
# Imports of the form `import .xxx`
lowerCamelCase_ =re.findall('''^\s*import\s+\.(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Unique-ify
return list(set(__snake_case ) )
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
lowerCamelCase_ =False
lowerCamelCase_ =[module_file]
lowerCamelCase_ =[]
# Let's recurse through all relative imports
while not no_change:
lowerCamelCase_ =[]
for f in files_to_check:
new_imports.extend(get_relative_imports(__snake_case ) )
lowerCamelCase_ =Path(__snake_case ).parent
lowerCamelCase_ =[str(module_path / m ) for m in new_imports]
lowerCamelCase_ =[f for f in new_import_files if f not in all_relative_imports]
lowerCamelCase_ =[F'''{f}.py''' for f in new_import_files]
lowerCamelCase_ =len(__snake_case ) == 0
all_relative_imports.extend(__snake_case )
return all_relative_imports
def a_ ( __snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' ) as f:
lowerCamelCase_ =f.read()
# Imports of the form `import xxx`
lowerCamelCase_ =re.findall('''^\s*import\s+(\S+)\s*$''' , __snake_case , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('''^\s*from\s+(\S+)\s+import''' , __snake_case , flags=re.MULTILINE )
# Only keep the top-level module
lowerCamelCase_ =[imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]
# Unique-ify and test we got them all
lowerCamelCase_ =list(set(__snake_case ) )
lowerCamelCase_ =[]
for imp in imports:
try:
importlib.import_module(__snake_case )
except ImportError:
missing_packages.append(__snake_case )
if len(__snake_case ) > 0:
raise ImportError(
'''This modeling file requires the following packages that were not found in your environment: '''
F'''{', '.join(__snake_case )}. Run `pip install {' '.join(__snake_case )}`''' )
return get_relative_imports(__snake_case )
def a_ ( __snake_case : Tuple , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =module_path.replace(os.path.sep , '''.''' )
lowerCamelCase_ =importlib.import_module(__snake_case )
if class_name is None:
return find_pipeline_class(__snake_case )
return getattr(__snake_case , __snake_case )
def a_ ( __snake_case : Dict ) -> Any:
"""simple docstring"""
from ..pipelines import DiffusionPipeline
lowerCamelCase_ =dict(inspect.getmembers(__snake_case , inspect.isclass ) )
lowerCamelCase_ =None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __snake_case )
and cls.__module__.split('''.''' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''' )
lowerCamelCase_ =cls
return pipeline_class
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =str(__snake_case )
lowerCamelCase_ =os.path.join(__snake_case , __snake_case )
if os.path.isfile(__snake_case ):
lowerCamelCase_ =module_file_or_url
lowerCamelCase_ ='''local'''
elif pretrained_model_name_or_path.count('''/''' ) == 0:
lowerCamelCase_ =get_diffusers_versions()
# cut ".dev0"
lowerCamelCase_ ='''v''' + '''.'''.join(__version__.split('''.''' )[:3] )
# retrieve github version that matches
if revision is None:
lowerCamelCase_ =latest_version if latest_version[1:] in available_versions else '''main'''
logger.info(F'''Defaulting to latest_version: {revision}.''' )
elif revision in available_versions:
lowerCamelCase_ =F'''v{revision}'''
elif revision == "main":
lowerCamelCase_ =revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {', '.join(available_versions + ['main'] )}.''' )
# community pipeline on GitHub
lowerCamelCase_ =COMMUNITY_PIPELINES_URL.format(revision=__snake_case , pipeline=__snake_case )
try:
lowerCamelCase_ =cached_download(
__snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowerCamelCase_ ='''git'''
lowerCamelCase_ =pretrained_model_name_or_path + '''.py'''
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
else:
try:
# Load from URL or cache if already cached
lowerCamelCase_ =hf_hub_download(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , proxies=__snake_case , resume_download=__snake_case , local_files_only=__snake_case , use_auth_token=__snake_case , )
lowerCamelCase_ =os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
# Check we have all the requirements in our environment
lowerCamelCase_ =check_imports(__snake_case )
# Now we move the module inside our cached dynamic modules.
lowerCamelCase_ =DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__snake_case )
lowerCamelCase_ =Path(__snake_case ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__snake_case , submodule_path / module_file )
for module_needed in modules_needed:
lowerCamelCase_ =F'''{module_needed}.py'''
shutil.copy(os.path.join(__snake_case , __snake_case ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__snake_case , __snake_case ):
lowerCamelCase_ =use_auth_token
elif use_auth_token is True:
lowerCamelCase_ =HfFolder.get_token()
else:
lowerCamelCase_ =None
lowerCamelCase_ =model_info(__snake_case , revision=__snake_case , token=__snake_case ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
lowerCamelCase_ =submodule_path / commit_hash
lowerCamelCase_ =full_submodule + os.path.sep + commit_hash
create_dynamic_module(__snake_case )
if not (submodule_path / module_file).exists():
shutil.copy(__snake_case , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__snake_case , F'''{module_needed}.py''' , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return os.path.join(__snake_case , __snake_case )
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : str , __snake_case : Optional[str] = None , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =get_cached_module_file(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
return get_class_in_module(__snake_case , final_module.replace('''.py''' , '''''' ) )
| 75 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a_ ( __snake_case : Callable , __snake_case : float , __snake_case : float , __snake_case : float , __snake_case : float ) -> np.ndarray:
"""simple docstring"""
lowerCamelCase_ =int(np.ceil((x_end - xa) / step_size ) )
lowerCamelCase_ =np.zeros((n + 1,) )
lowerCamelCase_ =ya
lowerCamelCase_ =xa
for k in range(__snake_case ):
lowerCamelCase_ =y[k] + step_size * ode_func(__snake_case , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
a_ : Any = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
a_ : Any = [
9_99,
9_76,
9_52,
9_28,
9_05,
8_82,
8_58,
8_57,
8_10,
7_62,
7_15,
7_14,
5_72,
4_29,
4_28,
2_86,
2_85,
2_38,
1_90,
1_43,
1_42,
1_18,
95,
71,
47,
24,
0,
]
a_ : Optional[Any] = [
9_99,
9_88,
9_77,
9_66,
9_55,
9_44,
9_33,
9_22,
9_11,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_50,
3_00,
2_99,
2_66,
2_33,
2_00,
1_99,
1_79,
1_59,
1_40,
1_20,
1_00,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
a_ : str = [
9_99,
9_95,
9_92,
9_89,
9_85,
9_81,
9_78,
9_75,
9_71,
9_67,
9_64,
9_61,
9_57,
9_56,
9_51,
9_47,
9_42,
9_37,
9_33,
9_28,
9_23,
9_19,
9_14,
9_13,
9_08,
9_03,
8_97,
8_92,
8_87,
8_81,
8_76,
8_71,
8_70,
8_64,
8_58,
8_52,
8_46,
8_40,
8_34,
8_28,
8_27,
8_20,
8_13,
8_06,
7_99,
7_92,
7_85,
7_84,
7_77,
7_70,
7_63,
7_56,
7_49,
7_42,
7_41,
7_33,
7_24,
7_16,
7_07,
6_99,
6_98,
6_88,
6_77,
6_66,
6_56,
6_55,
6_45,
6_34,
6_23,
6_13,
6_12,
5_98,
5_84,
5_70,
5_69,
5_55,
5_41,
5_27,
5_26,
5_05,
4_84,
4_83,
4_62,
4_40,
4_39,
3_96,
3_95,
3_52,
3_51,
3_08,
3_07,
2_64,
2_63,
2_20,
2_19,
1_76,
1_32,
88,
44,
0,
]
a_ : Optional[int] = [
9_99,
9_97,
9_95,
9_92,
9_90,
9_88,
9_86,
9_84,
9_81,
9_79,
9_77,
9_75,
9_72,
9_70,
9_68,
9_66,
9_64,
9_61,
9_59,
9_57,
9_56,
9_54,
9_51,
9_49,
9_46,
9_44,
9_41,
9_39,
9_36,
9_34,
9_31,
9_29,
9_26,
9_24,
9_21,
9_19,
9_16,
9_14,
9_13,
9_10,
9_07,
9_05,
9_02,
8_99,
8_96,
8_93,
8_91,
8_88,
8_85,
8_82,
8_79,
8_77,
8_74,
8_71,
8_70,
8_67,
8_64,
8_61,
8_58,
8_55,
8_52,
8_49,
8_46,
8_43,
8_40,
8_37,
8_34,
8_31,
8_28,
8_27,
8_24,
8_21,
8_17,
8_14,
8_11,
8_08,
8_04,
8_01,
7_98,
7_95,
7_91,
7_88,
7_85,
7_84,
7_80,
7_77,
7_74,
7_70,
7_66,
7_63,
7_60,
7_56,
7_52,
7_49,
7_46,
7_42,
7_41,
7_37,
7_33,
7_30,
7_26,
7_22,
7_18,
7_14,
7_10,
7_07,
7_03,
6_99,
6_98,
6_94,
6_90,
6_85,
6_81,
6_77,
6_73,
6_69,
6_64,
6_60,
6_56,
6_55,
6_50,
6_46,
6_41,
6_36,
6_32,
6_27,
6_22,
6_18,
6_13,
6_12,
6_07,
6_02,
5_96,
5_91,
5_86,
5_80,
5_75,
5_70,
5_69,
5_63,
5_57,
5_51,
5_45,
5_39,
5_33,
5_27,
5_26,
5_19,
5_12,
5_05,
4_98,
4_91,
4_84,
4_83,
4_74,
4_66,
4_57,
4_49,
4_40,
4_39,
4_28,
4_18,
4_07,
3_96,
3_95,
3_81,
3_66,
3_52,
3_51,
3_30,
3_08,
3_07,
2_86,
2_64,
2_63,
2_42,
2_20,
2_19,
1_76,
1_75,
1_32,
1_31,
88,
44,
0,
]
a_ : Dict = [
9_99,
9_91,
9_82,
9_74,
9_66,
9_58,
9_50,
9_41,
9_33,
9_25,
9_16,
9_08,
9_00,
8_99,
8_74,
8_50,
8_25,
8_00,
7_99,
7_00,
6_00,
5_00,
4_00,
3_00,
2_00,
1_00,
0,
]
a_ : Tuple = [
9_99,
9_92,
9_85,
9_78,
9_71,
9_64,
9_57,
9_49,
9_42,
9_35,
9_28,
9_21,
9_14,
9_07,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_00,
2_99,
2_00,
1_99,
1_00,
99,
0,
]
a_ : Any = [
9_99,
9_96,
9_92,
9_89,
9_85,
9_82,
9_79,
9_75,
9_72,
9_68,
9_65,
9_61,
9_58,
9_55,
9_51,
9_48,
9_44,
9_41,
9_38,
9_34,
9_31,
9_27,
9_24,
9_20,
9_17,
9_14,
9_10,
9_07,
9_03,
9_00,
8_99,
8_91,
8_84,
8_76,
8_69,
8_61,
8_53,
8_46,
8_38,
8_30,
8_23,
8_15,
8_08,
8_00,
7_99,
7_88,
7_77,
7_66,
7_55,
7_44,
7_33,
7_22,
7_11,
7_00,
6_99,
6_88,
6_77,
6_66,
6_55,
6_44,
6_33,
6_22,
6_11,
6_00,
5_99,
5_85,
5_71,
5_57,
5_42,
5_28,
5_14,
5_00,
4_99,
4_85,
4_71,
4_57,
4_42,
4_28,
4_14,
4_00,
3_99,
3_79,
3_59,
3_40,
3_20,
3_00,
2_99,
2_79,
2_59,
2_40,
2_20,
2_00,
1_99,
1_66,
1_33,
1_00,
99,
66,
33,
0,
]
| 75 | 1 |
'''simple docstring'''
from torch import nn
class __UpperCamelCase ( nn.Module ):
def __init__( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
super().__init__()
lowerCamelCase_ =class_size
lowerCamelCase_ =embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
lowerCamelCase_ =nn.Linear(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.mlp(lowerCAmelCase )
return logits
| 75 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""convert_funnel_original_tf_checkpoint_to_pytorch""": [],
"""tokenization_funnel""": ["""FunnelTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[str] = ["""FunnelTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[int] = [
"""FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FunnelBaseModel""",
"""FunnelForMaskedLM""",
"""FunnelForMultipleChoice""",
"""FunnelForPreTraining""",
"""FunnelForQuestionAnswering""",
"""FunnelForSequenceClassification""",
"""FunnelForTokenClassification""",
"""FunnelModel""",
"""FunnelPreTrainedModel""",
"""load_tf_weights_in_funnel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = [
"""TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFFunnelBaseModel""",
"""TFFunnelForMaskedLM""",
"""TFFunnelForMultipleChoice""",
"""TFFunnelForPreTraining""",
"""TFFunnelForQuestionAnswering""",
"""TFFunnelForSequenceClassification""",
"""TFFunnelForTokenClassification""",
"""TFFunnelModel""",
"""TFFunnelPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
a_ : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 75 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
a_ : List[str] = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def a_ ( __snake_case : str = "mumbai" ) -> Generator[tuple[str, str], None, None]:
"""simple docstring"""
lowerCamelCase_ =BeautifulSoup(requests.get(url + location ).content , '''html.parser''' )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all('''div''' , attrs={'''data-tn-component''': '''organicJob'''} ):
lowerCamelCase_ =job.find('''a''' , attrs={'''data-tn-element''': '''jobTitle'''} ).text.strip()
lowerCamelCase_ =job.find('''span''' , {'''class''': '''company'''} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
print(F"""Job {i:>2} is {job[0]} at {job[1]}""")
| 75 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
lowerCamelCase_ ={
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 16_000,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(self.tmpdirname, lowerCAmelCase )
with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
with open(self.feature_extraction_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )
# load decoder from hub
lowerCamelCase_ ='''hf-internal-testing/ngram-beam-search-decoder'''
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.add_kwargs_tokens_map.copy()
kwargs.update(lowerCAmelCase )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name, **lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer, lowerCAmelCase )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor, lowerCAmelCase )
# decoder
self.assertEqual(processor.decoder._alphabet.labels, decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set, decoder.model_container[decoder._model_key]._unigram_set, )
self.assertIsInstance(processor.decoder, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname, alpha=5.0, beta=3.0, score_boundary=-7.0, unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha, 5.0 )
self.assertEqual(processor.language_model.beta, 3.0 )
self.assertEqual(processor.language_model.score_boundary, -7.0 )
self.assertEqual(processor.language_model.unk_score_offset, 3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(lowerCAmelCase, '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=lowerCAmelCase, feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =floats_list((3, 1_000) )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =processor(lowerCAmelCase, return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ ='''This is a test string'''
lowerCamelCase_ =processor(text=lowerCAmelCase )
lowerCamelCase_ =tokenizer(lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def lowercase__ ( self, lowerCAmelCase=(2, 10, 16), lowerCAmelCase=77 ):
"""simple docstring"""
np.random.seed(lowerCAmelCase )
return np.random.rand(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits(shape=(10, 16), seed=13 )
lowerCamelCase_ =processor.decode(lowerCAmelCase )
lowerCamelCase_ =decoder.decode_beams(lowerCAmelCase )[0]
self.assertEqual(decoded_decoder[0], decoded_processor.text )
self.assertEqual('''</s> <s> </s>''', decoded_processor.text )
self.assertEqual(decoded_decoder[-2], decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1], decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase )
else:
with get_context(lowerCAmelCase ).Pool() as pool:
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =list(lowerCAmelCase )
with get_context('''fork''' ).Pool() as p:
lowerCamelCase_ =decoder.decode_beams_batch(lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =[], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowerCAmelCase, decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''], decoded_processor.text )
self.assertListEqual(lowerCAmelCase, decoded_processor.logit_score )
self.assertListEqual(lowerCAmelCase, decoded_processor.lm_score )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =15
lowerCamelCase_ =-2_0.0
lowerCamelCase_ =-4.0
lowerCamelCase_ =processor.batch_decode(
lowerCAmelCase, beam_width=lowerCAmelCase, beam_prune_logp=lowerCAmelCase, token_min_logp=lowerCAmelCase, )
lowerCamelCase_ =decoded_processor_out.text
lowerCamelCase_ =list(lowerCAmelCase )
with get_context('''fork''' ).Pool() as pool:
lowerCamelCase_ =decoder.decode_beams_batch(
lowerCAmelCase, lowerCAmelCase, beam_width=lowerCAmelCase, beam_prune_logp=lowerCAmelCase, token_min_logp=lowerCAmelCase, )
lowerCamelCase_ =[d[0][0] for d in decoded_decoder_out]
lowerCamelCase_ =[d[0][2] for d in decoded_decoder_out]
lowerCamelCase_ =[d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''], lowerCAmelCase )
self.assertTrue(np.array_equal(lowerCAmelCase, decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7], lowerCAmelCase, atol=1e-3 ) )
self.assertTrue(np.array_equal(lowerCAmelCase, decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4], lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =2.0
lowerCamelCase_ =5.0
lowerCamelCase_ =-2_0.0
lowerCamelCase_ =True
lowerCamelCase_ =processor.batch_decode(
lowerCAmelCase, alpha=lowerCAmelCase, beta=lowerCAmelCase, unk_score_offset=lowerCAmelCase, lm_score_boundary=lowerCAmelCase, )
lowerCamelCase_ =decoded_processor_out.text
lowerCamelCase_ =list(lowerCAmelCase )
decoder.reset_params(
alpha=lowerCAmelCase, beta=lowerCAmelCase, unk_score_offset=lowerCAmelCase, lm_score_boundary=lowerCAmelCase, )
with get_context('''fork''' ).Pool() as pool:
lowerCamelCase_ =decoder.decode_beams_batch(
lowerCAmelCase, lowerCAmelCase, )
lowerCamelCase_ =[d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''], lowerCAmelCase )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha, 2.0 )
self.assertEqual(lm_model.beta, 5.0 )
self.assertEqual(lm_model.unk_score_offset, -2_0.0 )
self.assertEqual(lm_model.score_boundary, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
lowerCamelCase_ =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
lowerCamelCase_ =os.listdir(lowerCAmelCase )
lowerCamelCase_ =['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =snapshot_download('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase )
lowerCamelCase_ =processor.decoder.model_container[processor.decoder._model_key]
lowerCamelCase_ =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
lowerCamelCase_ =os.listdir(lowerCAmelCase )
lowerCamelCase_ =os.listdir(lowerCAmelCase )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =floats_list((3, 1_000) )
lowerCamelCase_ =processor_wavaveca(lowerCAmelCase, return_tensors='''np''' )
lowerCamelCase_ =processor_auto(lowerCAmelCase, return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum(), input_auto[key].sum(), delta=1e-2 )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =processor_wavaveca.batch_decode(lowerCAmelCase )
lowerCamelCase_ =processor_auto.batch_decode(lowerCAmelCase )
self.assertListEqual(decoded_wavaveca.text, decoded_auto.text )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_feature_extractor()
lowerCamelCase_ =self.get_tokenizer()
lowerCamelCase_ =self.get_decoder()
lowerCamelCase_ =WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase, feature_extractor=lowerCAmelCase, decoder=lowerCAmelCase )
self.assertListEqual(
processor.model_input_names, feature_extractor.model_input_names, msg='''`processor` and `feature_extractor` model input names do not match''', )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[d[key] for d in offsets]
return retrieved_list
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =self._get_dummy_logits()[0]
lowerCamelCase_ =processor.decode(lowerCAmelCase, output_word_offsets=lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ), 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(lowerCAmelCase, lowerCAmelCase ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''], '''word''' ) ), outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''word''' ), ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''start_offset''' ), [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''], '''end_offset''' ), [1, 3, 5] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
lowerCamelCase_ =self._get_dummy_logits()
lowerCamelCase_ =processor.batch_decode(lowerCAmelCase, output_word_offsets=lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ), 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(lowerCAmelCase, lowerCAmelCase ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ) for o in outputs['''word_offsets''']], outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''word''' ), ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''start_offset''' ), [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0], '''end_offset''' ), [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =load_dataset('''common_voice''', '''en''', split='''train''', streaming=lowerCAmelCase )
lowerCamelCase_ =ds.cast_column('''audio''', datasets.Audio(sampling_rate=16_000 ) )
lowerCamelCase_ =iter(lowerCAmelCase )
lowerCamelCase_ =next(lowerCAmelCase )
lowerCamelCase_ =AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
lowerCamelCase_ =WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
lowerCamelCase_ =processor(sample['''audio''']['''array'''], return_tensors='''pt''' ).input_values
with torch.no_grad():
lowerCamelCase_ =model(lowerCAmelCase ).logits.cpu().numpy()
lowerCamelCase_ =processor.decode(logits[0], output_word_offsets=lowerCAmelCase )
lowerCamelCase_ =model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
lowerCamelCase_ =[
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
lowerCamelCase_ ='''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ), lowerCAmelCase )
self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase, '''word''' ) ), output.text )
# output times
lowerCamelCase_ =torch.tensor(self.get_from_offsets(lowerCAmelCase, '''start_time''' ) )
lowerCamelCase_ =torch.tensor(self.get_from_offsets(lowerCAmelCase, '''end_time''' ) )
# fmt: off
lowerCamelCase_ =torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
lowerCamelCase_ =torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=0.0_1 ) )
self.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=0.0_1 ) )
| 75 | 1 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_ : Optional[int] = """src/diffusers"""
a_ : str = """."""
# This is to make sure the diffusers module imported is the one in the repo.
a_ : List[str] = importlib.util.spec_from_file_location(
"""diffusers""",
os.path.join(DIFFUSERS_PATH, """__init__.py"""),
submodule_search_locations=[DIFFUSERS_PATH],
)
a_ : List[Any] = spec.loader.load_module()
def a_ ( __snake_case : Any , __snake_case : Any ) -> int:
"""simple docstring"""
return line.startswith(__snake_case ) or len(__snake_case ) <= 1 or re.search(r'''^\s*\)(\s*->.*:|:)\s*$''' , __snake_case ) is not None
def a_ ( __snake_case : Any ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =object_name.split('''.''' )
lowerCamelCase_ =0
# First let's find the module where our object lives.
lowerCamelCase_ =parts[i]
while i < len(__snake_case ) and not os.path.isfile(os.path.join(__snake_case , F'''{module}.py''' ) ):
i += 1
if i < len(__snake_case ):
lowerCamelCase_ =os.path.join(__snake_case , parts[i] )
if i >= len(__snake_case ):
raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''' )
with open(os.path.join(__snake_case , F'''{module}.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCamelCase_ =f.readlines()
# Now let's find the class / func in the code!
lowerCamelCase_ =''''''
lowerCamelCase_ =0
for name in parts[i + 1 :]:
while (
line_index < len(__snake_case ) and re.search(rF'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(__snake_case ):
raise ValueError(F''' {object_name} does not match any function or class in {module}.''' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
lowerCamelCase_ =line_index
while line_index < len(__snake_case ) and _should_continue(lines[line_index] , __snake_case ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
lowerCamelCase_ =lines[start_index:line_index]
return "".join(__snake_case )
a_ : Tuple = re.compile(R"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
a_ : Optional[int] = re.compile(R"""^\s*(\S+)->(\S+)(\s+.*|$)""")
a_ : Union[str, Any] = re.compile(R"""<FILL\s+[^>]*>""")
def a_ ( __snake_case : Dict ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =code.split('''\n''' )
lowerCamelCase_ =0
while idx < len(__snake_case ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(__snake_case ):
return re.search(r'''^(\s*)\S''' , lines[idx] ).groups()[0]
return ""
def a_ ( __snake_case : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ =len(get_indent(__snake_case ) ) > 0
if has_indent:
lowerCamelCase_ =F'''class Bla:\n{code}'''
lowerCamelCase_ =black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=__snake_case )
lowerCamelCase_ =black.format_str(__snake_case , mode=__snake_case )
lowerCamelCase_, lowerCamelCase_ =style_docstrings_in_code(__snake_case )
return result[len('''class Bla:\n''' ) :] if has_indent else result
def a_ ( __snake_case : str , __snake_case : Union[str, Any]=False ) -> Optional[int]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCamelCase_ =f.readlines()
lowerCamelCase_ =[]
lowerCamelCase_ =0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(__snake_case ):
lowerCamelCase_ =_re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =search.groups()
lowerCamelCase_ =find_code_in_diffusers(__snake_case )
lowerCamelCase_ =get_indent(__snake_case )
lowerCamelCase_ =line_index + 1 if indent == theoretical_indent else line_index + 2
lowerCamelCase_ =theoretical_indent
lowerCamelCase_ =start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
lowerCamelCase_ =True
while line_index < len(__snake_case ) and should_continue:
line_index += 1
if line_index >= len(__snake_case ):
break
lowerCamelCase_ =lines[line_index]
lowerCamelCase_ =_should_continue(__snake_case , __snake_case ) and re.search(F'''^{indent}# End copy''' , __snake_case ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
lowerCamelCase_ =lines[start_index:line_index]
lowerCamelCase_ =''''''.join(__snake_case )
# Remove any nested `Copied from` comments to avoid circular copies
lowerCamelCase_ =[line for line in theoretical_code.split('''\n''' ) if _re_copy_warning.search(__snake_case ) is None]
lowerCamelCase_ ='''\n'''.join(__snake_case )
# Before comparing, use the `replace_pattern` on the original code.
if len(__snake_case ) > 0:
lowerCamelCase_ =replace_pattern.replace('''with''' , '''''' ).split(''',''' )
lowerCamelCase_ =[_re_replace_pattern.search(__snake_case ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =pattern.groups()
lowerCamelCase_ =re.sub(__snake_case , __snake_case , __snake_case )
if option.strip() == "all-casing":
lowerCamelCase_ =re.sub(obja.lower() , obja.lower() , __snake_case )
lowerCamelCase_ =re.sub(obja.upper() , obja.upper() , __snake_case )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
lowerCamelCase_ =blackify(lines[start_index - 1] + theoretical_code )
lowerCamelCase_ =theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
lowerCamelCase_ =lines[:start_index] + [theoretical_code] + lines[line_index:]
lowerCamelCase_ =start_index + 1
if overwrite and len(__snake_case ) > 0:
# Warn the user a file has been modified.
print(F'''Detected changes, rewriting {filename}.''' )
with open(__snake_case , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(__snake_case )
return diffs
def a_ ( __snake_case : bool = False ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =glob.glob(os.path.join(__snake_case , '''**/*.py''' ) , recursive=__snake_case )
lowerCamelCase_ =[]
for filename in all_files:
lowerCamelCase_ =is_copy_consistent(__snake_case , __snake_case )
diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(__snake_case ) > 0:
lowerCamelCase_ ='''\n'''.join(__snake_case )
raise Exception(
'''Found the following copy inconsistencies:\n'''
+ diff
+ '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.''' )
if __name__ == "__main__":
a_ : str = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
a_ : Union[str, Any] = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 75 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =StableDiffusionInstructPixaPixPipeline
lowercase : List[Any] =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
lowercase : Optional[Any] =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase : Union[str, Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase : List[Any] =IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=8, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=32, )
lowerCamelCase_ =PNDMScheduler(skip_prk_steps=lowerCAmelCase )
torch.manual_seed(0 )
lowerCamelCase_ =AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
torch.manual_seed(0 )
lowerCamelCase_ =CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, )
lowerCamelCase_ =CLIPTextModel(lowerCAmelCase )
lowerCamelCase_ =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCamelCase_ ={
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowerCamelCase_ =image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase_ =Image.fromarray(np.uinta(lowerCAmelCase ) ).convert('''RGB''' )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ ='''french fries'''
lowerCamelCase_ =sd_pipe(**lowerCAmelCase, negative_prompt=lowerCAmelCase )
lowerCamelCase_ =output.images
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =[inputs['''prompt''']] * 2
lowerCamelCase_ =np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
lowerCamelCase_ =torch.from_numpy(lowerCAmelCase ).unsqueeze(0 ).to(lowerCAmelCase )
lowerCamelCase_ =image / 2 + 0.5
lowerCamelCase_ =image.permute(0, 3, 1, 2 )
lowerCamelCase_ =image.repeat(2, 1, 1, 1 )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
lowerCamelCase_ =np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='''scaled_linear''' )
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
lowerCamelCase_ =sd_pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1]
lowerCamelCase_ =[round(lowerCAmelCase, 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(lowerCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
lowerCamelCase_ =np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowerCamelCase_ =VaeImageProcessor(do_resize=lowerCAmelCase, do_normalize=lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' ) )[0]
lowerCamelCase_ =components['''vae''']
lowerCamelCase_ =self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
lowerCamelCase_ =vae.encode(inputs[image_param] ).latent_dist.mode()
lowerCamelCase_ =pipe(**lowerCAmelCase )[0]
lowerCamelCase_ =np.abs(out - out_latents_inputs ).max()
self.assertLess(lowerCAmelCase, 1e-4, '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
lowerCamelCase_ ={
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase )
lowerCamelCase_ =DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase ).images
lowerCamelCase_ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ =np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0
def callback_fn(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ) -> None:
lowerCamelCase_ =True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowerCamelCase_ =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase_ =latents[0, -3:, -3:, -1]
lowerCamelCase_ =np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowerCamelCase_ =False
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =self.get_inputs()
pipe(**lowerCAmelCase, callback=lowerCAmelCase, callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowercase__ ( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCamelCase_ =self.get_inputs()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCamelCase_ =inputs['''image'''].resize((504, 504) )
lowerCamelCase_ ='''timbrooks/instruct-pix2pix'''
lowerCamelCase_ =StableDiffusionInstructPixaPixPipeline.from_pretrained(
lowerCAmelCase, safety_checker=lowerCAmelCase, )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowerCamelCase_ =pipe(**lowerCAmelCase )
lowerCamelCase_ =output.images[0]
lowerCamelCase_ =image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
lowerCamelCase_ =np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 75 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a_ : Dict = logging.get_logger(__name__)
a_ : str = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Tuple ='dpt'
def __init__( self, lowerCAmelCase=768, lowerCAmelCase=12, lowerCAmelCase=12, lowerCAmelCase=3_072, lowerCAmelCase="gelu", lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=0.0_2, lowerCAmelCase=1e-12, lowerCAmelCase=384, lowerCAmelCase=16, lowerCAmelCase=3, lowerCAmelCase=False, lowerCAmelCase=True, lowerCAmelCase=[2, 5, 8, 11], lowerCAmelCase="project", lowerCAmelCase=[4, 2, 1, 0.5], lowerCAmelCase=[96, 192, 384, 768], lowerCAmelCase=256, lowerCAmelCase=-1, lowerCAmelCase=False, lowerCAmelCase=True, lowerCAmelCase=0.4, lowerCAmelCase=255, lowerCAmelCase=0.1, lowerCAmelCase=[1, 1_024, 24, 24], lowerCAmelCase=[0, 1], lowerCAmelCase=None, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =hidden_size
lowerCamelCase_ =is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('''Initializing the config with a `BiT` backbone.''' )
lowerCamelCase_ ={
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
}
lowerCamelCase_ =BitConfig(**lowerCAmelCase )
elif isinstance(lowerCAmelCase, lowerCAmelCase ):
logger.info('''Initializing the config with a `BiT` backbone.''' )
lowerCamelCase_ =BitConfig(**lowerCAmelCase )
elif isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =backbone_config
else:
raise ValueError(
f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )
lowerCamelCase_ =backbone_featmap_shape
lowerCamelCase_ =neck_ignore_stages
if readout_type != "project":
raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.''' )
else:
lowerCamelCase_ =None
lowerCamelCase_ =None
lowerCamelCase_ =[]
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =intermediate_size
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =attention_probs_dropout_prob
lowerCamelCase_ =initializer_range
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =image_size
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =qkv_bias
lowerCamelCase_ =backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']''' )
lowerCamelCase_ =readout_type
lowerCamelCase_ =reassemble_factors
lowerCamelCase_ =neck_hidden_sizes
lowerCamelCase_ =fusion_hidden_size
lowerCamelCase_ =head_in_index
lowerCamelCase_ =use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
lowerCamelCase_ =use_auxiliary_head
lowerCamelCase_ =auxiliary_loss_weight
lowerCamelCase_ =semantic_loss_ignore_index
lowerCamelCase_ =semantic_classifier_dropout
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
lowerCamelCase_ =self.backbone_config.to_dict()
lowerCamelCase_ =self.__class__.model_type
return output
| 75 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __UpperCamelCase :
lowercase : Union[str, Any] =XGLMConfig
lowercase : Optional[Any] ={}
lowercase : Optional[int] ='gelu'
def __init__( self, lowerCAmelCase, lowerCAmelCase=14, lowerCAmelCase=7, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=True, lowerCAmelCase=99, lowerCAmelCase=32, lowerCAmelCase=2, lowerCAmelCase=4, lowerCAmelCase=37, lowerCAmelCase="gelu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=512, lowerCAmelCase=0.0_2, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =is_training
lowerCamelCase_ =use_input_mask
lowerCamelCase_ =use_labels
lowerCamelCase_ =vocab_size
lowerCamelCase_ =d_model
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =ffn_dim
lowerCamelCase_ =activation_function
lowerCamelCase_ =activation_dropout
lowerCamelCase_ =attention_dropout
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =initializer_range
lowerCamelCase_ =None
lowerCamelCase_ =0
lowerCamelCase_ =2
lowerCamelCase_ =1
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length], self.vocab_size ), clip_value_min=0, clip_value_max=3 )
lowerCamelCase_ =None
if self.use_input_mask:
lowerCamelCase_ =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ =self.get_config()
lowerCamelCase_ =floats_tensor([self.num_hidden_layers, self.num_attention_heads], 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def lowercase__ ( self ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size, d_model=self.hidden_size, num_layers=self.num_hidden_layers, attention_heads=self.num_attention_heads, ffn_dim=self.ffn_dim, activation_function=self.activation_function, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=lowerCAmelCase, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.prepare_config_and_inputs()
(
(
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
), (
lowerCamelCase_
),
) =config_and_inputs
lowerCamelCase_ ={
'''input_ids''': input_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_tf
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : int =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowercase : Optional[Any] =(TFXGLMForCausalLM,) if is_tf_available() else ()
lowercase : Tuple =(
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowercase : Optional[Any] =False
lowercase : Optional[Any] =False
lowercase : Optional[int] =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase, n_embd=37 )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def lowercase__ ( self ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ =TFXGLMModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
def lowercase__ ( self ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
@slow
def lowercase__ ( self, lowerCAmelCase=True ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =tf.convert_to_tensor([[2, 268, 9_865]], dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
lowerCamelCase_ =[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
# fmt: on
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist(), lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
tf.random.set_seed(0 )
lowerCamelCase_ =tokenizer('''Today is a nice day and''', return_tensors='''tf''' )
lowerCamelCase_ =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(''':/CPU:0''' ):
lowerCamelCase_ =model.generate(lowerCAmelCase, do_sample=lowerCAmelCase, seed=[7, 0] )
lowerCamelCase_ =tokenizer.decode(output_ids[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =(
'''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
)
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ =XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
lowerCamelCase_ ='''left'''
# use different length sentences to test batching
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When''',
'''Hello, my dog is a little''',
]
lowerCamelCase_ =tokenizer(lowerCAmelCase, return_tensors='''tf''', padding=lowerCAmelCase )
lowerCamelCase_ =inputs['''input_ids''']
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, attention_mask=inputs['''attention_mask'''], max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[0], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer(sentences[1], return_tensors='''tf''' ).input_ids
lowerCamelCase_ =model.generate(input_ids=lowerCAmelCase, max_new_tokens=12 )
lowerCamelCase_ =tokenizer.batch_decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_non_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =tokenizer.decode(output_padded[0], skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =[
'''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
'''left-padding, such as in batched generation. The output for the sequence below should be the same '''
'''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
'''a single''',
'''Hello, my dog is a little bit of a shy one, but he is very friendly''',
]
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [non_padded_sentence, padded_sentence] )
| 75 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
a_ : int = logging.get_logger(__name__)
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
warnings.warn(
'''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use FlavaImageProcessor instead.''', lowerCAmelCase, )
super().__init__(*lowerCAmelCase, **lowerCAmelCase )
| 75 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __UpperCamelCase :
@staticmethod
def lowercase__ ( *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
lowercase : int =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
]
return object_detector, examples
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =object_detector(examples[0], threshold=0.0 )
lowerCamelCase_ =len(lowerCAmelCase )
self.assertGreater(lowerCAmelCase, 0 )
self.assertEqual(
lowerCAmelCase, [
{
'''score''': ANY(lowerCAmelCase ),
'''label''': ANY(lowerCAmelCase ),
'''box''': {'''xmin''': ANY(lowerCAmelCase ), '''ymin''': ANY(lowerCAmelCase ), '''xmax''': ANY(lowerCAmelCase ), '''ymax''': ANY(lowerCAmelCase )},
}
for i in range(lowerCAmelCase )
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline(
'''zero-shot-object-detection''', model='''hf-internal-testing/tiny-random-owlvit-object-detection''' )
lowerCamelCase_ =object_detector(
'''./tests/fixtures/tests_samples/COCO/000000039769.png''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
}
], threshold=0.6_4, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
{'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
{'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
{'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
{'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
]
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
], )
lowerCamelCase_ =object_detector(
[
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
{
'''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
},
], )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
[
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
{'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
{'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
],
], )
@require_tf
@unittest.skip('''Zero Shot Object Detection not implemented in TF''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =0.2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], threshold=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
{'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
], )
@require_torch
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =2
lowerCamelCase_ =pipeline('''zero-shot-object-detection''' )
lowerCamelCase_ =object_detector(
'''http://images.cocodataset.org/val2017/000000039769.jpg''', candidate_labels=['''cat''', '''remote''', '''couch'''], top_k=lowerCAmelCase, )
self.assertEqual(
nested_simplify(lowerCAmelCase, decimals=4 ), [
{'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
{'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
], )
| 75 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __UpperCamelCase :
def __init__( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =str(id_ )
lowerCamelCase_ =None
lowerCamelCase_ =None
lowerCamelCase_ =[]
lowerCamelCase_ ={} # {vertex:distance}
def __lt__( self, lowerCAmelCase ):
"""simple docstring"""
return self.key < other.key
def __repr__( self ):
"""simple docstring"""
return self.id
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
self.neighbors.append(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =weight
def a_ ( __snake_case : Optional[int] , __snake_case : Tuple , __snake_case : Union[str, Any] , __snake_case : Optional[Any] ) -> str:
"""simple docstring"""
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __snake_case )
graph[b - 1].add_edge(graph[a - 1] , __snake_case )
def a_ ( __snake_case : list , __snake_case : Vertex ) -> list:
"""simple docstring"""
lowerCamelCase_ =[]
for u in graph:
lowerCamelCase_ =math.inf
lowerCamelCase_ =None
lowerCamelCase_ =0
lowerCamelCase_ =graph[:]
while q:
lowerCamelCase_ =min(__snake_case )
q.remove(__snake_case )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
lowerCamelCase_ =u
lowerCamelCase_ =u.edges[v.id]
for i in range(1 , len(__snake_case ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def a_ ( __snake_case : list , __snake_case : Vertex ) -> Iterator[tuple]:
"""simple docstring"""
for u in graph:
lowerCamelCase_ =math.inf
lowerCamelCase_ =None
lowerCamelCase_ =0
lowerCamelCase_ =list(__snake_case )
hq.heapify(__snake_case )
while h:
lowerCamelCase_ =hq.heappop(__snake_case )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
lowerCamelCase_ =u
lowerCamelCase_ =u.edges[v.id]
hq.heapify(__snake_case )
for i in range(1 , len(__snake_case ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def a_ ( ) -> None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Optional[int] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
a_ : List[Any] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
a_ : Optional[int] = {"""facebook/blenderbot_small-90M""": 5_12}
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ =set()
lowerCamelCase_ =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase_ =char
lowerCamelCase_ =set(__snake_case )
return pairs
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : Optional[int] =VOCAB_FILES_NAMES
lowercase : Tuple =PRETRAINED_VOCAB_FILES_MAP
lowercase : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Dict =['input_ids', 'attention_mask']
def __init__( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase="__start__", lowerCAmelCase="__end__", lowerCAmelCase="__unk__", lowerCAmelCase="__null__", **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(unk_token=lowerCAmelCase, bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, pad_token=lowerCAmelCase, **lowerCAmelCase )
with open(lowerCAmelCase, encoding='''utf-8''' ) as vocab_handle:
lowerCamelCase_ =json.load(lowerCAmelCase )
lowerCamelCase_ ={v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase, encoding='''utf-8''' ) as merges_handle:
lowerCamelCase_ =merges_handle.read().split('''\n''' )[1:-1]
lowerCamelCase_ =[tuple(merge.split() ) for merge in merges]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ ={}
@property
def lowercase__ ( self ):
"""simple docstring"""
return len(self.encoder )
def lowercase__ ( self ):
"""simple docstring"""
return dict(self.encoder, **self.added_tokens_encoder )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
lowerCamelCase_ =re.sub('''([.,!?()])''', R''' \1''', lowerCAmelCase )
lowerCamelCase_ =re.sub('''(\')''', R''' \1 ''', lowerCAmelCase )
lowerCamelCase_ =re.sub(R'''\s{2,}''', ''' ''', lowerCAmelCase )
if "\n" in token:
lowerCamelCase_ =token.replace('''\n''', ''' __newln__''' )
lowerCamelCase_ =token.split(''' ''' )
lowerCamelCase_ =[]
for token in tokens:
if not len(lowerCAmelCase ):
continue
lowerCamelCase_ =token.lower()
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
lowerCamelCase_ =get_pairs(lowerCAmelCase )
if not pairs:
words.append(lowerCAmelCase )
continue
while True:
lowerCamelCase_ =min(lowerCAmelCase, key=lambda lowerCAmelCase : self.bpe_ranks.get(lowerCAmelCase, float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase_, lowerCamelCase_ =bigram
lowerCamelCase_ =[]
lowerCamelCase_ =0
while i < len(lowerCAmelCase ):
try:
lowerCamelCase_ =word.index(lowerCAmelCase, lowerCAmelCase )
new_word.extend(word[i:j] )
lowerCamelCase_ =j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase_ =tuple(lowerCAmelCase )
lowerCamelCase_ =new_word
if len(lowerCAmelCase ) == 1:
break
else:
lowerCamelCase_ =get_pairs(lowerCAmelCase )
lowerCamelCase_ ='''@@ '''.join(lowerCAmelCase )
lowerCamelCase_ =word[:-4]
lowerCamelCase_ =word
words.append(lowerCAmelCase )
return " ".join(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_ =re.findall(R'''\S+\n?''', lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =token.lower()
return self.encoder.get(lowerCAmelCase, self.encoder.get(self.unk_token ) )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.decoder.get(lowerCAmelCase, self.unk_token )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''' '''.join(lowerCAmelCase ).replace('''@@ ''', '''''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=lowerCAmelCase, ensure_ascii=lowerCAmelCase ) + '''\n''' )
lowerCamelCase_ =0
with open(lowerCAmelCase, '''w''', encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
lowerCamelCase_ =token_index
writer.write(''' '''.join(lowerCAmelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 75 | 1 |
'''simple docstring'''
from __future__ import annotations
a_ : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a_ ( __snake_case : list[list[int]] , __snake_case : list[int] , __snake_case : list[int] , __snake_case : int , __snake_case : list[list[int]] , ) -> tuple[list[list[int]], list[list[int]]]:
"""simple docstring"""
lowerCamelCase_ =[
[0 for col in range(len(grid[0] ) )] for row in range(len(__snake_case ) )
] # the reference grid
lowerCamelCase_ =1
lowerCamelCase_ =[
[0 for col in range(len(grid[0] ) )] for row in range(len(__snake_case ) )
] # the action grid
lowerCamelCase_ =init[0]
lowerCamelCase_ =init[1]
lowerCamelCase_ =0
lowerCamelCase_ =g + heuristic[x][y] # cost from starting cell to destination cell
lowerCamelCase_ =[[f, g, x, y]]
lowerCamelCase_ =False # flag that is set when search is complete
lowerCamelCase_ =False # flag set if we can't find expand
while not found and not resign:
if len(__snake_case ) == 0:
raise ValueError('''Algorithm is unable to find solution''' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
lowerCamelCase_ =cell.pop()
lowerCamelCase_ =next_cell[2]
lowerCamelCase_ =next_cell[3]
lowerCamelCase_ =next_cell[1]
if x == goal[0] and y == goal[1]:
lowerCamelCase_ =True
else:
for i in range(len(__snake_case ) ): # to try out different valid actions
lowerCamelCase_ =x + DIRECTIONS[i][0]
lowerCamelCase_ =y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(__snake_case ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
lowerCamelCase_ =g + cost
lowerCamelCase_ =ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
lowerCamelCase_ =1
lowerCamelCase_ =i
lowerCamelCase_ =[]
lowerCamelCase_ =goal[0]
lowerCamelCase_ =goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
lowerCamelCase_ =x - DIRECTIONS[action[x][y]][0]
lowerCamelCase_ =y - DIRECTIONS[action[x][y]][1]
lowerCamelCase_ =xa
lowerCamelCase_ =ya
invpath.append([x, y] )
lowerCamelCase_ =[]
for i in range(len(__snake_case ) ):
path.append(invpath[len(__snake_case ) - 1 - i] )
return path, action
if __name__ == "__main__":
a_ : Dict = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
a_ : Tuple = [0, 0]
# all coordinates are given in format [y,x]
a_ : Optional[int] = [len(grid) - 1, len(grid[0]) - 1]
a_ : List[Any] = 1
# the cost map which pushes the path closer to the goal
a_ : List[Any] = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
a_ : int = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
a_ : int = 99
a_ , a_ : str = search(grid, init, goal, cost, heuristic)
print("""ACTION MAP""")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 75 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class __UpperCamelCase ( lowerCamelCase__ ):
lowercase : List[str] ='efficientformer'
def __init__( self, lowerCAmelCase = [3, 2, 6, 4], lowerCAmelCase = [48, 96, 224, 448], lowerCAmelCase = [True, True, True, True], lowerCAmelCase = 448, lowerCAmelCase = 32, lowerCAmelCase = 4, lowerCAmelCase = 7, lowerCAmelCase = 5, lowerCAmelCase = 8, lowerCAmelCase = 4, lowerCAmelCase = 0.0, lowerCAmelCase = 16, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 3, lowerCAmelCase = 2, lowerCAmelCase = 1, lowerCAmelCase = 0.0, lowerCAmelCase = 1, lowerCAmelCase = True, lowerCAmelCase = True, lowerCAmelCase = 1e-5, lowerCAmelCase = "gelu", lowerCAmelCase = 0.0_2, lowerCAmelCase = 1e-12, lowerCAmelCase = 224, lowerCAmelCase = 1e-05, **lowerCAmelCase, ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =hidden_sizes
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =initializer_range
lowerCamelCase_ =layer_norm_eps
lowerCamelCase_ =patch_size
lowerCamelCase_ =num_channels
lowerCamelCase_ =depths
lowerCamelCase_ =mlp_expansion_ratio
lowerCamelCase_ =downsamples
lowerCamelCase_ =dim
lowerCamelCase_ =key_dim
lowerCamelCase_ =attention_ratio
lowerCamelCase_ =resolution
lowerCamelCase_ =pool_size
lowerCamelCase_ =downsample_patch_size
lowerCamelCase_ =downsample_stride
lowerCamelCase_ =downsample_pad
lowerCamelCase_ =drop_path_rate
lowerCamelCase_ =num_metaad_blocks
lowerCamelCase_ =distillation
lowerCamelCase_ =use_layer_scale
lowerCamelCase_ =layer_scale_init_value
lowerCamelCase_ =image_size
lowerCamelCase_ =batch_norm_eps
| 75 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
a_ : str = """pt"""
elif is_tf_available():
a_ : List[Any] = """tf"""
else:
a_ : int = """jax"""
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : List[Any] =PerceiverTokenizer
lowercase : List[str] =False
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
return self.tokenizer_class.from_pretrained(self.tmpdirname, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=False, lowerCAmelCase=20, lowerCAmelCase=5 ):
"""simple docstring"""
lowerCamelCase_ =[]
for i in range(len(lowerCAmelCase ) ):
try:
lowerCamelCase_ =tokenizer.decode([i], clean_up_tokenization_spaces=lowerCAmelCase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCamelCase_ =list(filter(lambda lowerCAmelCase : re.match(R'''^[ a-zA-Z]+$''', t[1] ), lowerCAmelCase ) )
lowerCamelCase_ =list(filter(lambda lowerCAmelCase : [t[0]] == tokenizer.encode(t[1], add_special_tokens=lowerCAmelCase ), lowerCAmelCase ) )
if max_length is not None and len(lowerCAmelCase ) > max_length:
lowerCamelCase_ =toks[:max_length]
if min_length is not None and len(lowerCAmelCase ) < min_length and len(lowerCAmelCase ) > 0:
while len(lowerCAmelCase ) < min_length:
lowerCamelCase_ =toks + toks
# toks_str = [t[1] for t in toks]
lowerCamelCase_ =[t[0] for t in toks]
# Ensure consistency
lowerCamelCase_ =tokenizer.decode(lowerCAmelCase, clean_up_tokenization_spaces=lowerCAmelCase )
if " " not in output_txt and len(lowerCAmelCase ) > 1:
lowerCamelCase_ =(
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=lowerCAmelCase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=lowerCAmelCase )
)
if with_prefix_space:
lowerCamelCase_ =''' ''' + output_txt
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
return output_txt, output_ids
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.perceiver_tokenizer
lowerCamelCase_ ='''Unicode €.'''
lowerCamelCase_ =tokenizer(lowerCAmelCase )
lowerCamelCase_ =[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded['''input_ids'''], lowerCAmelCase )
# decoding
lowerCamelCase_ =tokenizer.decode(lowerCAmelCase )
self.assertEqual(lowerCAmelCase, '''[CLS]Unicode €.[SEP]''' )
lowerCamelCase_ =tokenizer('''e è é ê ë''' )
lowerCamelCase_ =[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded['''input_ids'''], lowerCAmelCase )
# decoding
lowerCamelCase_ =tokenizer.decode(lowerCAmelCase )
self.assertEqual(lowerCAmelCase, '''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ), '''[CLS]e è é ê ë[SEP]''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.perceiver_tokenizer
lowerCamelCase_ =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
lowerCamelCase_ =[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
lowerCamelCase_ =tokenizer(lowerCAmelCase, padding=lowerCAmelCase, return_tensors=lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
if FRAMEWORK != "jax":
lowerCamelCase_ =list(batch.input_ids.numpy()[0] )
else:
lowerCamelCase_ =list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertEqual((2, 38), batch.input_ids.shape )
self.assertEqual((2, 38), batch.attention_mask.shape )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.perceiver_tokenizer
lowerCamelCase_ =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowerCamelCase_ =tokenizer(lowerCAmelCase, padding=lowerCAmelCase, return_tensors=lowerCAmelCase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''', lowerCAmelCase )
self.assertIn('''attention_mask''', lowerCAmelCase )
self.assertNotIn('''decoder_input_ids''', lowerCAmelCase )
self.assertNotIn('''decoder_attention_mask''', lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.perceiver_tokenizer
lowerCamelCase_ =[
'''Summary of the text.''',
'''Another summary.''',
]
lowerCamelCase_ =tokenizer(
text_target=lowerCAmelCase, max_length=32, padding='''max_length''', truncation=lowerCAmelCase, return_tensors=lowerCAmelCase )
self.assertEqual(32, targets['''input_ids'''].shape[1] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length, 42 )
# Now let's start the test
lowerCamelCase_ =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =''' He is very happy, UNwant\u00E9d,running'''
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
tokenizer.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =tokenizer.__class__.from_pretrained(lowerCAmelCase )
lowerCamelCase_ =after_tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
shutil.rmtree(lowerCAmelCase )
lowerCamelCase_ =self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase_ =tempfile.mkdtemp()
lowerCamelCase_ =''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
lowerCamelCase_ =tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
lowerCamelCase_ =tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
tokenizer.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =tokenizer.__class__.from_pretrained(lowerCAmelCase )
lowerCamelCase_ =after_tokenizer.encode(lowerCAmelCase, add_special_tokens=lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, lowerCAmelCase )
self.assertIn('''new_additional_special_token''', after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length, 42 )
lowerCamelCase_ =tokenizer.__class__.from_pretrained(lowerCAmelCase, model_max_length=43 )
self.assertEqual(tokenizer.model_max_length, 43 )
shutil.rmtree(lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =[]
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCAmelCase )
with open(os.path.join(lowerCAmelCase, '''special_tokens_map.json''' ), encoding='''utf-8''' ) as json_file:
lowerCamelCase_ =json.load(lowerCAmelCase )
with open(os.path.join(lowerCAmelCase, '''tokenizer_config.json''' ), encoding='''utf-8''' ) as json_file:
lowerCamelCase_ =json.load(lowerCAmelCase )
lowerCamelCase_ =[f'''<extra_id_{i}>''' for i in range(125 )]
lowerCamelCase_ =added_tokens_extra_ids + [
'''an_additional_special_token'''
]
lowerCamelCase_ =added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(lowerCAmelCase, '''special_tokens_map.json''' ), '''w''', encoding='''utf-8''' ) as outfile:
json.dump(lowerCAmelCase, lowerCAmelCase )
with open(os.path.join(lowerCAmelCase, '''tokenizer_config.json''' ), '''w''', encoding='''utf-8''' ) as outfile:
json.dump(lowerCAmelCase, lowerCAmelCase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCamelCase_ =tokenizer_class.from_pretrained(
lowerCAmelCase, )
self.assertIn(
'''an_additional_special_token''', tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['''an_additional_special_token'''], tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ), )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCamelCase_ =added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''', lstrip=lowerCAmelCase )]
lowerCamelCase_ =tokenizer_class.from_pretrained(
lowerCAmelCase, additional_special_tokens=lowerCAmelCase, )
self.assertIn('''a_new_additional_special_token''', tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''], tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ), )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ), '''�''' )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_tokenizers(fast=lowerCAmelCase, do_lower_case=lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase_ =['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
lowerCamelCase_ =tokenizer.convert_tokens_to_string(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase, lowerCAmelCase )
| 75 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
a_ : Union[str, Any] = random.Random()
def a_ ( __snake_case : int , __snake_case : int=1.0 , __snake_case : Tuple=None , __snake_case : Union[str, Any]=None ) -> str:
"""simple docstring"""
if rng is None:
lowerCamelCase_ =global_rng
lowerCamelCase_ =[]
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self, lowerCAmelCase, lowerCAmelCase=7, lowerCAmelCase=400, lowerCAmelCase=2_000, lowerCAmelCase=24, lowerCAmelCase=24, lowerCAmelCase=0.0, lowerCAmelCase=16_000, lowerCAmelCase=True, lowerCAmelCase=True, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =min_seq_length
lowerCamelCase_ =max_seq_length
lowerCamelCase_ =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase_ =feature_size
lowerCamelCase_ =num_mel_bins
lowerCamelCase_ =padding_value
lowerCamelCase_ =sampling_rate
lowerCamelCase_ =return_attention_mask
lowerCamelCase_ =do_normalize
def lowercase__ ( self ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowercase__ ( self, lowerCAmelCase=False, lowerCAmelCase=False ):
"""simple docstring"""
def _flatten(lowerCAmelCase ):
return list(itertools.chain(*lowerCAmelCase ) )
if equal_length:
lowerCamelCase_ =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCamelCase_ =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Any =SpeechaTextFeatureExtractor if is_speech_available() else None
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextFeatureExtractionTester(self )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
self.assertTrue(np.all(np.mean(lowerCAmelCase, axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase, axis=0 ) - 1 ) < 1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =[np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]
# Test feature size
lowerCamelCase_ =feature_extractor(lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
lowerCamelCase_ =feature_extractor(speech_inputs[0], return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(np_speech_inputs[0], return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test batched
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase_ =[floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCamelCase_ =np.asarray(lowerCAmelCase )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCAmelCase, lowerCAmelCase ):
self.assertTrue(np.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-3 ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding=lowerCAmelCase, max_length=lowerCAmelCase, return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =['''longest''', '''max_length''', '''do_not_pad''']
lowerCamelCase_ =[None, 16, None]
for max_length, padding in zip(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, max_length=lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =[np.sum(lowerCAmelCase ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''max_length''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=4, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24) )
lowerCamelCase_ =[floats_list((1, x) )[0] for x in range(800, 1_400, 200 )]
lowerCamelCase_ =feature_extractor(
lowerCAmelCase, padding='''longest''', max_length=16, truncation=lowerCAmelCase, return_tensors='''np''', return_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =inputs.input_features
lowerCamelCase_ =inputs.attention_mask
lowerCamelCase_ =np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24) )
def lowercase__ ( self ):
"""simple docstring"""
import torch
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =np.random.rand(100, 32 ).astype(np.floataa )
lowerCamelCase_ =np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCamelCase_ =feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
from datasets import load_dataset
lowerCamelCase_ =load_dataset('''hf-internal-testing/librispeech_asr_dummy''', '''clean''', split='''validation''' )
# automatic decoding with librispeech
lowerCamelCase_ =ds.sort('''id''' ).select(range(lowerCAmelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =np.array([
-1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
-1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
-1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
] )
# fmt: on
lowerCamelCase_ =self._load_datasamples(1 )
lowerCamelCase_ =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase_ =feature_extractor(lowerCAmelCase, return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape, (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30], lowerCAmelCase, atol=1e-4 ) )
| 75 | 1 |
'''simple docstring'''
def a_ ( __snake_case : list , __snake_case : list , __snake_case : int ) -> int:
"""simple docstring"""
if len(__snake_case ) != len(__snake_case ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
raise ValueError('''max_weight must greater than zero.''' )
if any(p < 0 for p in profit ):
raise ValueError('''Profit can not be negative.''' )
if any(w < 0 for w in weight ):
raise ValueError('''Weight can not be negative.''' )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
lowerCamelCase_ =[p / w for p, w in zip(__snake_case , __snake_case )]
# Creating a copy of the list and sorting profit/weight in ascending order
lowerCamelCase_ =sorted(__snake_case )
# declaring useful variables
lowerCamelCase_ =len(__snake_case )
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
lowerCamelCase_ =sorted_profit_by_weight[length - i - 1]
lowerCamelCase_ =profit_by_weight.index(__snake_case )
lowerCamelCase_ =-1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
"""Input profits, weights, and then max_weight (all positive ints) separated by """
"""spaces."""
)
a_ : Optional[int] = [int(x) for x in input("""Input profits separated by spaces: """).split()]
a_ : List[str] = [int(x) for x in input("""Input weights separated by spaces: """).split()]
a_ : int = int(input("""Max weight allowed: """))
# Function Call
calc_profit(profit, weight, max_weight)
| 75 |
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[str] ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in table:
res += inp[i - 1]
return res
def a_ ( __snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
return data[1:] + data[0]
def a_ ( __snake_case : str , __snake_case : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase_ =''''''
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def a_ ( __snake_case : Optional[Any] , __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ =int('''0b''' + data[0] + data[-1] , 2 )
lowerCamelCase_ =int('''0b''' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def a_ ( __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : int , __snake_case : Tuple , __snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =message[:4]
lowerCamelCase_ =message[4:]
lowerCamelCase_ =apply_table(__snake_case , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
lowerCamelCase_ =apply_sbox(__snake_case , temp[:4] ) # noqa: E741
lowerCamelCase_ =apply_sbox(__snake_case , temp[4:] )
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + l # noqa: E741
lowerCamelCase_ ='''0''' * (2 - len(__snake_case )) + r
lowerCamelCase_ =apply_table(l + r , __snake_case )
lowerCamelCase_ =xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
a_ : Any = input("""Enter 10 bit key: """)
a_ : Any = input("""Enter 8 bit message: """)
a_ : str = [6, 3, 7, 4, 8, 5, 10, 9]
a_ : str = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
a_ : str = [2, 4, 3, 1]
a_ : Optional[int] = [2, 6, 3, 1, 4, 8, 5, 7]
a_ : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
a_ : Union[str, Any] = [4, 1, 2, 3, 2, 3, 4, 1]
a_ : int = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
a_ : Any = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
a_ : List[Any] = apply_table(key, paa_table)
a_ : str = temp[:5]
a_ : Optional[Any] = temp[5:]
a_ : Tuple = left_shift(left)
a_ : Optional[Any] = left_shift(right)
a_ : str = apply_table(left + right, pa_table)
a_ : Optional[Any] = left_shift(left)
a_ : Tuple = left_shift(right)
a_ : Union[str, Any] = left_shift(left)
a_ : List[str] = left_shift(right)
a_ : Optional[int] = apply_table(left + right, pa_table)
# encryption
a_ : Optional[int] = apply_table(message, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : str = temp[4:] + temp[:4]
a_ : List[str] = function(expansion, sa, sa, keya, temp)
a_ : Union[str, Any] = apply_table(temp, IP_inv)
print("""Cipher text is:""", CT)
# decryption
a_ : Optional[int] = apply_table(CT, IP)
a_ : List[Any] = function(expansion, sa, sa, keya, temp)
a_ : int = temp[4:] + temp[:4]
a_ : int = function(expansion, sa, sa, keya, temp)
a_ : Optional[int] = apply_table(temp, IP_inv)
print("""Plain text after decypting is:""", PT)
| 75 | 1 |
'''simple docstring'''
def a_ ( __snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ =len(__snake_case )
while cur > 1:
# Find the maximum number in arr
lowerCamelCase_ =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowerCamelCase_ =arr[mi::-1] + arr[mi + 1 : len(__snake_case )]
# Reverse whole list
lowerCamelCase_ =arr[cur - 1 :: -1] + arr[cur : len(__snake_case )]
cur -= 1
return arr
if __name__ == "__main__":
a_ : Union[str, Any] = input("""Enter numbers separated by a comma:\n""").strip()
a_ : Any = [int(item) for item in user_input.split(""",""")]
print(pancake_sort(unsorted))
| 75 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
a_ : List[Any] = logging.get_logger(__name__)
a_ : Tuple = OrderedDict(
[
("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
("""beit""", """BeitFeatureExtractor"""),
("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
("""clap""", """ClapFeatureExtractor"""),
("""clip""", """CLIPFeatureExtractor"""),
("""clipseg""", """ViTFeatureExtractor"""),
("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
("""convnext""", """ConvNextFeatureExtractor"""),
("""cvt""", """ConvNextFeatureExtractor"""),
("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
("""data2vec-vision""", """BeitFeatureExtractor"""),
("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
("""deit""", """DeiTFeatureExtractor"""),
("""detr""", """DetrFeatureExtractor"""),
("""dinat""", """ViTFeatureExtractor"""),
("""donut-swin""", """DonutFeatureExtractor"""),
("""dpt""", """DPTFeatureExtractor"""),
("""encodec""", """EncodecFeatureExtractor"""),
("""flava""", """FlavaFeatureExtractor"""),
("""glpn""", """GLPNFeatureExtractor"""),
("""groupvit""", """CLIPFeatureExtractor"""),
("""hubert""", """Wav2Vec2FeatureExtractor"""),
("""imagegpt""", """ImageGPTFeatureExtractor"""),
("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
("""levit""", """LevitFeatureExtractor"""),
("""maskformer""", """MaskFormerFeatureExtractor"""),
("""mctct""", """MCTCTFeatureExtractor"""),
("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
("""mobilevit""", """MobileViTFeatureExtractor"""),
("""nat""", """ViTFeatureExtractor"""),
("""owlvit""", """OwlViTFeatureExtractor"""),
("""perceiver""", """PerceiverFeatureExtractor"""),
("""poolformer""", """PoolFormerFeatureExtractor"""),
("""regnet""", """ConvNextFeatureExtractor"""),
("""resnet""", """ConvNextFeatureExtractor"""),
("""segformer""", """SegformerFeatureExtractor"""),
("""sew""", """Wav2Vec2FeatureExtractor"""),
("""sew-d""", """Wav2Vec2FeatureExtractor"""),
("""speech_to_text""", """Speech2TextFeatureExtractor"""),
("""speecht5""", """SpeechT5FeatureExtractor"""),
("""swiftformer""", """ViTFeatureExtractor"""),
("""swin""", """ViTFeatureExtractor"""),
("""swinv2""", """ViTFeatureExtractor"""),
("""table-transformer""", """DetrFeatureExtractor"""),
("""timesformer""", """VideoMAEFeatureExtractor"""),
("""tvlt""", """TvltFeatureExtractor"""),
("""unispeech""", """Wav2Vec2FeatureExtractor"""),
("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
("""van""", """ConvNextFeatureExtractor"""),
("""videomae""", """VideoMAEFeatureExtractor"""),
("""vilt""", """ViltFeatureExtractor"""),
("""vit""", """ViTFeatureExtractor"""),
("""vit_mae""", """ViTFeatureExtractor"""),
("""vit_msn""", """ViTFeatureExtractor"""),
("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
("""wavlm""", """Wav2Vec2FeatureExtractor"""),
("""whisper""", """WhisperFeatureExtractor"""),
("""xclip""", """CLIPFeatureExtractor"""),
("""yolos""", """YolosFeatureExtractor"""),
]
)
a_ : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def a_ ( __snake_case : str ) -> Any:
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCamelCase_ =model_type_to_module_name(__snake_case )
lowerCamelCase_ =importlib.import_module(F'''.{module_name}''' , '''transformers.models''' )
try:
return getattr(__snake_case , __snake_case )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(__snake_case , '''__name__''' , __snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCamelCase_ =importlib.import_module('''transformers''' )
if hasattr(__snake_case , __snake_case ):
return getattr(__snake_case , __snake_case )
return None
def a_ ( __snake_case : Union[str, os.PathLike] , __snake_case : Optional[Union[str, os.PathLike]] = None , __snake_case : bool = False , __snake_case : bool = False , __snake_case : Optional[Dict[str, str]] = None , __snake_case : Optional[Union[bool, str]] = None , __snake_case : Optional[str] = None , __snake_case : bool = False , **__snake_case : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ =get_file_from_repo(
__snake_case , __snake_case , cache_dir=__snake_case , force_download=__snake_case , resume_download=__snake_case , proxies=__snake_case , use_auth_token=__snake_case , revision=__snake_case , local_files_only=__snake_case , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(__snake_case , encoding='''utf-8''' ) as reader:
return json.load(__snake_case )
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(lowerCAmelCase )
def lowercase__ ( cls, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =kwargs.pop('''config''', lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''trust_remote_code''', lowerCAmelCase )
lowerCamelCase_ =True
lowerCamelCase_, lowerCamelCase_ =FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =config_dict.get('''feature_extractor_type''', lowerCAmelCase )
lowerCamelCase_ =None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''', {} ):
lowerCamelCase_ =config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =AutoConfig.from_pretrained(lowerCAmelCase, **lowerCAmelCase )
# It could be in `config.feature_extractor_type``
lowerCamelCase_ =getattr(lowerCAmelCase, '''feature_extractor_type''', lowerCAmelCase )
if hasattr(lowerCAmelCase, '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
lowerCamelCase_ =config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
lowerCamelCase_ =feature_extractor_class_from_name(lowerCAmelCase )
lowerCamelCase_ =feature_extractor_auto_map is not None
lowerCamelCase_ =feature_extractor_class is not None or type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
lowerCamelCase_ =resolve_trust_remote_code(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
if has_remote_code and trust_remote_code:
lowerCamelCase_ =get_class_from_dynamic_module(
lowerCAmelCase, lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =kwargs.pop('''code_revision''', lowerCAmelCase )
if os.path.isdir(lowerCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
lowerCamelCase_ =FEATURE_EXTRACTOR_MAPPING[type(lowerCAmelCase )]
return feature_extractor_class.from_dict(lowerCAmelCase, **lowerCAmelCase )
raise ValueError(
f'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
f'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def lowercase__ ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase, lowerCAmelCase )
| 75 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
a_ : Optional[Any] = 5
a_ : str = 10
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : int =SpeechaTextTokenizer
lowercase : int =False
lowercase : List[str] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =sp.SentencePieceProcessor()
spm_model.Load(lowerCAmelCase )
lowerCamelCase_ =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowerCAmelCase ) )]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =Path(self.tmpdirname )
save_json(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''<pad>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<s>''' )
self.assertEqual(vocab_keys[1], '''<pad>''' )
self.assertEqual(vocab_keys[-1], '''j''' )
self.assertEqual(len(lowerCAmelCase ), 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [289, 50, 14, 174, 386], )
lowerCamelCase_ =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.'''], )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ =tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.'''], )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase, model_name='''facebook/s2t-small-mustc-en-de-st''', revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''', )
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Tuple ='valhalla/s2t_mustc_multilinguial_medium'
lowercase : Dict ='C\'est trop cool'
lowercase : str ='Esto es genial'
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''], 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''], 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''], 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''], 11 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 10_000 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertIn(lowerCAmelCase, self.tokenizer.all_special_ids )
lowerCamelCase_ =[ES_CODE, 4, 1_601, 47, 7_647, 2]
lowerCamelCase_ =self.tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
lowerCamelCase_ =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0], lowerCAmelCase )
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE] )
lowerCamelCase_ ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE] )
| 75 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_ : Optional[int] = logging.getLogger(__name__)
def a_ ( __snake_case : Union[str, Any] , __snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ =np.argmax(__snake_case , axis=1 )
return np.sum(outputs == labels )
def a_ ( __snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
with open(__snake_case , encoding='''utf_8''' ) as f:
lowerCamelCase_ =csv.reader(__snake_case )
lowerCamelCase_ =[]
next(__snake_case ) # skip the first line
for line in tqdm(__snake_case ):
output.append((''' '''.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a_ ( __snake_case : str , __snake_case : Dict , __snake_case : List[str] , __snake_case : List[str] , __snake_case : List[Any] , __snake_case : Dict ) -> Dict:
"""simple docstring"""
lowerCamelCase_ =[]
for dataset in encoded_datasets:
lowerCamelCase_ =len(__snake_case )
lowerCamelCase_ =np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch, 2) , dtype=np.intaa )
lowerCamelCase_ =np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
lowerCamelCase_ =np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__snake_case ):
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =len(__snake_case ) - 1
lowerCamelCase_ =with_conta
lowerCamelCase_ =with_conta
lowerCamelCase_ =mc_label
lowerCamelCase_ =(input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__snake_case ) for t in all_inputs ) )
return tensor_datasets
def a_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=__snake_case , default='''openai-gpt''' , help='''pretrained model name''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_eval''' , action='''store_true''' , help='''Whether to run eval on the dev set.''' )
parser.add_argument(
'''--output_dir''' , default=__snake_case , type=__snake_case , required=__snake_case , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument('''--train_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--eval_dataset''' , type=__snake_case , default='''''' )
parser.add_argument('''--seed''' , type=__snake_case , default=42 )
parser.add_argument('''--num_train_epochs''' , type=__snake_case , default=3 )
parser.add_argument('''--train_batch_size''' , type=__snake_case , default=8 )
parser.add_argument('''--eval_batch_size''' , type=__snake_case , default=16 )
parser.add_argument('''--adam_epsilon''' , default=1e-8 , type=__snake_case , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--max_grad_norm''' , type=__snake_case , default=1 )
parser.add_argument(
'''--max_steps''' , default=-1 , type=__snake_case , help=(
'''If > 0: set total number of training steps to perform. Override num_train_epochs.'''
) , )
parser.add_argument(
'''--gradient_accumulation_steps''' , type=__snake_case , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--learning_rate''' , type=__snake_case , default=6.25e-5 )
parser.add_argument('''--warmup_steps''' , default=0 , type=__snake_case , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--lr_schedule''' , type=__snake_case , default='''warmup_linear''' )
parser.add_argument('''--weight_decay''' , type=__snake_case , default=0.0_1 )
parser.add_argument('''--lm_coef''' , type=__snake_case , default=0.9 )
parser.add_argument('''--n_valid''' , type=__snake_case , default=374 )
parser.add_argument('''--server_ip''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=__snake_case , default='''''' , help='''Can be used for distant debugging.''' )
lowerCamelCase_ =parser.parse_args()
print(__snake_case )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__snake_case )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowerCamelCase_ =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
lowerCamelCase_ =torch.cuda.device_count()
logger.info('''device: {}, n_gpu {}'''.format(__snake_case , __snake_case ) )
if not args.do_train and not args.do_eval:
raise ValueError('''At least one of `do_train` or `do_eval` must be True.''' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowerCamelCase_ =['''_start_''', '''_delimiter_''', '''_classify_''']
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(__snake_case )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(__snake_case )
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(__snake_case ) )
model.to(__snake_case )
# Load and encode the datasets
def tokenize_and_encode(__snake_case : Union[str, Any] ):
if isinstance(__snake_case , __snake_case ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__snake_case ) )
elif isinstance(__snake_case , __snake_case ):
return obj
return [tokenize_and_encode(__snake_case ) for o in obj]
logger.info('''Encoding dataset...''' )
lowerCamelCase_ =load_rocstories_dataset(args.train_dataset )
lowerCamelCase_ =load_rocstories_dataset(args.eval_dataset )
lowerCamelCase_ =(train_dataset, eval_dataset)
lowerCamelCase_ =tokenize_and_encode(__snake_case )
# Compute the max input length for the Transformer
lowerCamelCase_ =model.config.n_positions // 2 - 2
lowerCamelCase_ =max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowerCamelCase_ =min(__snake_case , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowerCamelCase_ =pre_process_datasets(__snake_case , __snake_case , __snake_case , *__snake_case )
lowerCamelCase_, lowerCamelCase_ =tensor_datasets[0], tensor_datasets[1]
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =RandomSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.train_batch_size )
lowerCamelCase_ =TensorDataset(*__snake_case )
lowerCamelCase_ =SequentialSampler(__snake_case )
lowerCamelCase_ =DataLoader(__snake_case , sampler=__snake_case , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowerCamelCase_ =args.max_steps
lowerCamelCase_ =args.max_steps // (len(__snake_case ) // args.gradient_accumulation_steps) + 1
else:
lowerCamelCase_ =len(__snake_case ) // args.gradient_accumulation_steps * args.num_train_epochs
lowerCamelCase_ =list(model.named_parameters() )
lowerCamelCase_ =['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
lowerCamelCase_ =[
{
'''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'''weight_decay''': args.weight_decay,
},
{'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
]
lowerCamelCase_ =AdamW(__snake_case , lr=args.learning_rate , eps=args.adam_epsilon )
lowerCamelCase_ =get_linear_schedule_with_warmup(
__snake_case , num_warmup_steps=args.warmup_steps , num_training_steps=__snake_case )
if args.do_train:
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='''Epoch''' ):
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =tqdm(__snake_case , desc='''Training''' )
for step, batch in enumerate(__snake_case ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
lowerCamelCase_ =model(__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowerCamelCase_ =(
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowerCamelCase_ ='''Training loss: {:.2e} lr: {:.2e}'''.format(__snake_case , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowerCamelCase_ =model.module if hasattr(__snake_case , '''module''' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
lowerCamelCase_ =os.path.join(args.output_dir , __snake_case )
torch.save(model_to_save.state_dict() , __snake_case )
model_to_save.config.to_json_file(__snake_case )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowerCamelCase_ =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowerCamelCase_ =OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(__snake_case )
if args.do_eval:
model.eval()
lowerCamelCase_, lowerCamelCase_ =0, 0
lowerCamelCase_, lowerCamelCase_ =0, 0
for batch in tqdm(__snake_case , desc='''Evaluating''' ):
lowerCamelCase_ =tuple(t.to(__snake_case ) for t in batch )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =batch
with torch.no_grad():
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =model(
__snake_case , mc_token_ids=__snake_case , lm_labels=__snake_case , mc_labels=__snake_case )
lowerCamelCase_ =mc_logits.detach().cpu().numpy()
lowerCamelCase_ =mc_labels.to('''cpu''' ).numpy()
lowerCamelCase_ =accuracy(__snake_case , __snake_case )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowerCamelCase_ =eval_loss / nb_eval_steps
lowerCamelCase_ =eval_accuracy / nb_eval_examples
lowerCamelCase_ =tr_loss / nb_tr_steps if args.do_train else None
lowerCamelCase_ ={'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}
lowerCamelCase_ =os.path.join(args.output_dir , '''eval_results.txt''' )
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' , __snake_case , str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 75 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
a_ : Optional[Any] = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, Maja\",
booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
month = sep,
year = \"2015\",
address = \"Lisbon, Portugal\",
publisher = \"Association for Computational Linguistics\",
url = \"https://aclanthology.org/W15-3049\",
doi = \"10.18653/v1/W15-3049\",
pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
title = \"chr{F}++: words helping character n-grams\",
author = \"Popovi{\'c}, Maja\",
booktitle = \"Proceedings of the Second Conference on Machine Translation\",
month = sep,
year = \"2017\",
address = \"Copenhagen, Denmark\",
publisher = \"Association for Computational Linguistics\",
url = \"https://aclanthology.org/W17-4770\",
doi = \"10.18653/v1/W17-4770\",
pages = \"612--618\",
}
@inproceedings{post-2018-call,
title = \"A Call for Clarity in Reporting {BLEU} Scores\",
author = \"Post, Matt\",
booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
month = oct,
year = \"2018\",
address = \"Belgium, Brussels\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/W18-6319\",
pages = \"186--191\",
}
"""
a_ : List[Any] = """\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.
The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""
a_ : Optional[Any] = """
Produces ChrF(++) scores for hypotheses given reference translations.
Args:
predictions (list of str): The predicted sentences.
references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
char_order (int): Character n-gram order. Defaults to `6`.
word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
eps_smoothing (bool): If `True`, applies epsilon smoothing similar
to reference chrF++.py, NLTK and Moses implementations. If `False`,
it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.
Returns:
'score' (float): The chrF (chrF++) score,
'char_order' (int): The character n-gram order,
'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
'beta' (int): Determine the importance of recall w.r.t precision
Examples:
Example 1--a simple example of calculating chrF:
>>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
>>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
>>> chrf = datasets.load_metric(\"chrf\")
>>> results = chrf.compute(predictions=prediction, references=reference)
>>> print(results)
{'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}
Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
>>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
>>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
>>> chrf = datasets.load_metric(\"chrf\")
>>> results = chrf.compute(predictions=prediction,
... references=reference,
... word_order=2)
>>> print(results)
{'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}
Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
>>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
>>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
>>> chrf = datasets.load_metric(\"chrf\")
>>> results = chrf.compute(predictions=prediction,
... references=reference,
... word_order=2,
... lowercase=True)
>>> print(results)
{'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def lowercase__ ( self ):
"""simple docstring"""
if version.parse(scb.__version__ ) < version.parse('''1.4.12''' ):
raise ImportWarning(
'''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
'''You can install it with `pip install "sacrebleu>=1.4.12"`.''' )
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf''', inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'''predictions''': datasets.Value('''string''', id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''', id='''sequence''' ), id='''references''' ),
} ), codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf'''], reference_urls=[
'''https://github.com/m-popovic/chrF''',
], )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase = CHRF.CHAR_ORDER, lowerCAmelCase = CHRF.WORD_ORDER, lowerCAmelCase = CHRF.BETA, lowerCAmelCase = False, lowerCAmelCase = False, lowerCAmelCase = False, ):
"""simple docstring"""
lowerCamelCase_ =len(references[0] )
if any(len(lowerCAmelCase ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
lowerCamelCase_ =[[refs[i] for refs in references] for i in range(lowerCAmelCase )]
lowerCamelCase_ =CHRF(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =sb_chrf.corpus_score(lowerCAmelCase, lowerCAmelCase )
return {
"score": output.score,
"char_order": output.char_order,
"word_order": output.word_order,
"beta": output.beta,
}
| 75 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ =''''''
lowerCamelCase_ =[]
lowerCamelCase_ =0
lowerCamelCase_ =256
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =cva.imread(lowerCAmelCase, 0 )
lowerCamelCase_ =copy.deepcopy(self.img )
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ =plt.hist(self.img.ravel(), 256, [0, 256], label='''x''' )
lowerCamelCase_ =np.sum(lowerCAmelCase )
for i in range(len(lowerCAmelCase ) ):
lowerCamelCase_ =x[i] / self.k
self.sk += prk
lowerCamelCase_ =(self.L - 1) * self.sk
if self.rem != 0:
lowerCamelCase_ =int(last % last )
lowerCamelCase_ =int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(lowerCAmelCase )
lowerCamelCase_ =int(np.ma.count(self.img ) / self.img[1].size )
lowerCamelCase_ =self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCamelCase_ =self.img[j][i]
if num != self.last_list[num]:
lowerCamelCase_ =self.last_list[num]
cva.imwrite('''output_data/output.jpg''', self.img )
def lowercase__ ( self ):
"""simple docstring"""
plt.hist(self.img.ravel(), 256, [0, 256] )
def lowercase__ ( self ):
"""simple docstring"""
cva.imshow('''Output-Image''', self.img )
cva.imshow('''Input-Image''', self.original_image )
cva.waitKey(5_000 )
cva.destroyAllWindows()
if __name__ == "__main__":
a_ : str = os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
a_ : Optional[Any] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 75 | 1 |
'''simple docstring'''
import re
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
if len(re.findall('''[ATCG]''' , __snake_case ) ) != len(__snake_case ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.maketrans('''ATCG''' , '''TAGC''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ : Any = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(**lowerCAmelCase )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
# No specific FOR_XXX available yet
def __call__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return super().__call__(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ ={}
if "candidate_labels" in kwargs:
lowerCamelCase_ =kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
lowerCamelCase_ =kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(lowerCAmelCase, lowerCAmelCase ):
if audio.startswith('''http://''' ) or audio.startswith('''https://''' ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
lowerCamelCase_ =requests.get(lowerCAmelCase ).content
else:
with open(lowerCAmelCase, '''rb''' ) as f:
lowerCamelCase_ =f.read()
if isinstance(lowerCAmelCase, lowerCAmelCase ):
lowerCamelCase_ =ffmpeg_read(lowerCAmelCase, self.feature_extractor.sampling_rate )
if not isinstance(lowerCAmelCase, np.ndarray ):
raise ValueError('''We expect a numpy ndarray as input''' )
if len(audio.shape ) != 1:
raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''' )
lowerCamelCase_ =self.feature_extractor(
[audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='''pt''' )
lowerCamelCase_ =candidate_labels
lowerCamelCase_ =[hypothesis_template.format(lowerCAmelCase ) for x in candidate_labels]
lowerCamelCase_ =self.tokenizer(lowerCAmelCase, return_tensors=self.framework, padding=lowerCAmelCase )
lowerCamelCase_ =[text_inputs]
return inputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_inputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0], lowerCAmelCase ):
lowerCamelCase_ =text_inputs[0]
else:
# Batching case.
lowerCamelCase_ =text_inputs[0][0]
lowerCamelCase_ =self.model(**lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ ={
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_audio,
}
return model_outputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =model_outputs.pop('''candidate_labels''' )
lowerCamelCase_ =model_outputs['''logits'''][0]
if self.framework == "pt":
lowerCamelCase_ =logits.softmax(dim=0 )
lowerCamelCase_ =probs.tolist()
else:
raise ValueError('''`tf` framework not supported.''' )
lowerCamelCase_ =[
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(lowerCAmelCase, lowerCAmelCase ), key=lambda lowerCAmelCase : -x[0] )
]
return result
| 75 | 1 |
'''simple docstring'''
from math import factorial
def a_ ( __snake_case : int , __snake_case : int ) -> int:
"""simple docstring"""
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(__snake_case ) // (factorial(__snake_case ) * factorial(n - k ))
if __name__ == "__main__":
print(
"""The number of five-card hands possible from a standard""",
F"""fifty-two card deck is: {combinations(52, 5)}\n""",
)
print(
"""If a class of 40 students must be arranged into groups of""",
F"""4 for group projects, there are {combinations(40, 4)} ways""",
"""to arrange them.\n""",
)
print(
"""If 10 teams are competing in a Formula One race, there""",
F"""are {combinations(10, 3)} ways that first, second and""",
"""third place can be awarded.""",
)
| 75 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
a_ : Optional[Any] = 5
a_ : str = 10
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : int =SpeechaTextTokenizer
lowercase : int =False
lowercase : List[str] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =sp.SentencePieceProcessor()
spm_model.Load(lowerCAmelCase )
lowerCamelCase_ =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowerCAmelCase ) )]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =Path(self.tmpdirname )
save_json(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''<pad>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<s>''' )
self.assertEqual(vocab_keys[1], '''<pad>''' )
self.assertEqual(vocab_keys[-1], '''j''' )
self.assertEqual(len(lowerCAmelCase ), 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [289, 50, 14, 174, 386], )
lowerCamelCase_ =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.'''], )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ =tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.'''], )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase, model_name='''facebook/s2t-small-mustc-en-de-st''', revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''', )
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Tuple ='valhalla/s2t_mustc_multilinguial_medium'
lowercase : Dict ='C\'est trop cool'
lowercase : str ='Esto es genial'
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''], 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''], 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''], 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''], 11 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 10_000 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertIn(lowerCAmelCase, self.tokenizer.all_special_ids )
lowerCamelCase_ =[ES_CODE, 4, 1_601, 47, 7_647, 2]
lowerCamelCase_ =self.tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
lowerCamelCase_ =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0], lowerCAmelCase )
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE] )
lowerCamelCase_ ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE] )
| 75 | 1 |
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