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"""simple docstring"""
from numpy import exp, pi, sqrt
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase = 0.0 , _UpperCamelCase = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 86
|
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_A : int = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""
class a__ ( unittest.TestCase, a_ ):
def __magic_name__ ( self ):
lowercase : Tuple = load_tool("text-question-answering" )
self.tool.setup()
lowercase : Dict = load_tool("text-question-answering" , remote=_a )
def __magic_name__ ( self ):
lowercase : str = self.tool(_a , "What did Hugging Face do in April 2021?" )
self.assertEqual(_a , "launched the BigScience Research Workshop" )
def __magic_name__ ( self ):
lowercase : Union[str, Any] = self.remote_tool(_a , "What did Hugging Face do in April 2021?" )
self.assertEqual(_a , "launched the BigScience Research Workshop" )
def __magic_name__ ( self ):
lowercase : int = self.tool(text=_a , question="What did Hugging Face do in April 2021?" )
self.assertEqual(_a , "launched the BigScience Research Workshop" )
def __magic_name__ ( self ):
lowercase : Optional[Any] = self.remote_tool(text=_a , question="What did Hugging Face do in April 2021?" )
self.assertEqual(_a , "launched the BigScience Research Workshop" )
| 202
| 0
|
class UpperCamelCase__ :
def __init__(self : Optional[int] , snake_case_ : int ):
__a : List[Any] = n
__a : Tuple = [None] * self.n
__a : List[Any] = 0 # index of the first element
__a : List[str] = 0
__a : Tuple = 0
def __len__(self : List[Any] ):
return self.size
def lowerCAmelCase (self : Tuple ):
return self.size == 0
def lowerCAmelCase (self : Dict ):
return False if self.is_empty() else self.array[self.front]
def lowerCAmelCase (self : Optional[int] , snake_case_ : int ):
if self.size >= self.n:
raise Exception('''QUEUE IS FULL''' )
__a : Tuple = data
__a : str = (self.rear + 1) % self.n
self.size += 1
return self
def lowerCAmelCase (self : Optional[int] ):
if self.size == 0:
raise Exception('''UNDERFLOW''' )
__a : str = self.array[self.front]
__a : Union[str, Any] = None
__a : Tuple = (self.front + 1) % self.n
self.size -= 1
return temp
| 369
|
def __UpperCamelCase ( lowerCAmelCase__ : list[list[int | float]] ):
__a : int = len(lowerCAmelCase__ )
__a : Dict = len(matrix[0] )
__a : Union[str, Any] = min(lowerCAmelCase__ , lowerCAmelCase__ )
for row in range(lowerCAmelCase__ ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , lowerCAmelCase__ ):
__a : Dict = matrix[col][row] / matrix[row][row]
for i in range(lowerCAmelCase__ , lowerCAmelCase__ ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
__a : Optional[int] = True
for i in range(row + 1 , lowerCAmelCase__ ):
if matrix[i][row] != 0:
__a , __a : Any = matrix[i], matrix[row]
__a : Union[str, Any] = False
break
if reduce:
rank -= 1
for i in range(lowerCAmelCase__ ):
__a : Optional[Any] = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 90
| 0
|
'''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"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
_a : str= logging.get_logger(__name__)
_a : str= {"vocab_file": "spiece.model"}
_a : Tuple= {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
}
}
_a : int= {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
# Segments (not really needed)
_a : Optional[int]= 0
_a : str= 1
_a : Tuple= 2
_a : str= 3
_a : Optional[Any]= 4
class UpperCamelCase ( lowercase ):
UpperCAmelCase : Union[str, Any] = VOCAB_FILES_NAMES
UpperCAmelCase : int = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : str = """left"""
def __init__(self : List[Any] , _A : List[str] , _A : int=False , _A : Tuple=True , _A : Optional[Any]=False , _A : List[Any]="<s>" , _A : Dict="</s>" , _A : str="<unk>" , _A : Optional[Any]="<sep>" , _A : Optional[Any]="<pad>" , _A : Optional[Any]="<cls>" , _A : Dict="<mask>" , _A : List[Any]=["<eop>", "<eod>"] , _A : Optional[Dict[str, Any]] = None , **_A : List[str] , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
__snake_case : str = AddedToken(_A , lstrip=_A , rstrip=_A) if isinstance(_A , _A) else mask_token
__snake_case : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_A , remove_space=_A , keep_accents=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , pad_token=_A , cls_token=_A , mask_token=_A , additional_special_tokens=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
__snake_case : Tuple = 3
__snake_case : Optional[int] = do_lower_case
__snake_case : Union[str, Any] = remove_space
__snake_case : Dict = keep_accents
__snake_case : str = vocab_file
__snake_case : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(_A)
@property
def _lowercase (self : Dict) -> List[str]:
return len(self.sp_model)
def _lowercase (self : Dict) -> Union[str, Any]:
__snake_case : str = {self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self : Union[str, Any]) -> List[str]:
__snake_case : Optional[Any] = self.__dict__.copy()
__snake_case : Union[str, Any] = None
return state
def __setstate__(self : Union[str, Any] , _A : Optional[Any]) -> str:
__snake_case : Optional[int] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
__snake_case : List[Any] = {}
__snake_case : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _lowercase (self : Any , _A : Tuple) -> List[str]:
if self.remove_space:
__snake_case : List[Any] = ' '.join(inputs.strip().split())
else:
__snake_case : Tuple = inputs
__snake_case : int = outputs.replace('``' , '"').replace('\'\'' , '"')
if not self.keep_accents:
__snake_case : str = unicodedata.normalize('NFKD' , _A)
__snake_case : Tuple = ''.join([c for c in outputs if not unicodedata.combining(_A)])
if self.do_lower_case:
__snake_case : Union[str, Any] = outputs.lower()
return outputs
def _lowercase (self : List[Any] , _A : str) -> List[str]:
__snake_case : int = self.preprocess_text(_A)
__snake_case : Dict = self.sp_model.encode(_A , out_type=_A)
__snake_case : Union[str, Any] = []
for piece in pieces:
if len(_A) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
__snake_case : List[str] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_A , ''))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
__snake_case : List[str] = cur_pieces[1:]
else:
__snake_case : Union[str, Any] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(_A)
else:
new_pieces.append(_A)
return new_pieces
def _lowercase (self : Union[str, Any] , _A : Union[str, Any]) -> Any:
return self.sp_model.PieceToId(_A)
def _lowercase (self : Tuple , _A : str) -> Optional[int]:
return self.sp_model.IdToPiece(_A)
def _lowercase (self : List[str] , _A : Dict) -> List[Any]:
__snake_case : str = ''.join(_A).replace(_A , ' ').strip()
return out_string
def _lowercase (self : Dict , _A : List[int] , _A : bool = False , _A : bool = None , _A : bool = True , **_A : str , ) -> str:
__snake_case : Tuple = kwargs.pop('use_source_tokenizer' , _A)
__snake_case : Tuple = self.convert_ids_to_tokens(_A , skip_special_tokens=_A)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
__snake_case : List[str] = []
__snake_case : str = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_A))
__snake_case : List[Any] = []
sub_texts.append(_A)
else:
current_sub_text.append(_A)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_A))
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
__snake_case : Optional[int] = ''.join(_A)
__snake_case : str = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
__snake_case : str = self.clean_up_tokenization(_A)
return clean_text
else:
return text
def _lowercase (self : Dict , _A : List[int] , _A : Optional[List[int]] = None) -> List[int]:
__snake_case : int = [self.sep_token_id]
__snake_case : Any = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _lowercase (self : List[str] , _A : List[int] , _A : Optional[List[int]] = None , _A : bool = False) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A)
if token_ids_a is not None:
return ([0] * len(_A)) + [1] + ([0] * len(_A)) + [1, 1]
return ([0] * len(_A)) + [1, 1]
def _lowercase (self : Dict , _A : List[int] , _A : Optional[List[int]] = None) -> List[int]:
__snake_case : Tuple = [self.sep_token_id]
__snake_case : Optional[int] = [2]
if token_ids_a is None:
return len(token_ids_a + sep) * [0] + cls_segment_id
return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id
def _lowercase (self : Tuple , _A : str , _A : Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(_A):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
__snake_case : str = os.path.join(
_A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(_A) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , _A)
elif not os.path.isfile(self.vocab_file):
with open(_A , 'wb') as fi:
__snake_case : Tuple = self.sp_model.serialized_model_proto()
fi.write(_A)
return (out_vocab_file,)
| 172
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongT5EncoderModel''',
'''LongT5ForConditionalGeneration''',
'''LongT5Model''',
'''LongT5PreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'''FlaxLongT5ForConditionalGeneration''',
'''FlaxLongT5Model''',
'''FlaxLongT5PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 217
|
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _lowerCAmelCase ( lowerCamelCase_ : dict , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : List[str] ):
__lowercase = set()
# keep track of all the paths to be checked
__lowercase = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
__lowercase = queue.pop(0 )
# get the last node from the path
__lowercase = path[-1]
if node not in explored:
__lowercase = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
__lowercase = list(lowerCamelCase_ )
new_path.append(lowerCamelCase_ )
queue.append(lowerCamelCase_ )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(lowerCamelCase_ )
# in case there's no path between the 2 nodes
return []
def _lowerCAmelCase ( lowerCamelCase_ : dict , lowerCamelCase_ : str , lowerCamelCase_ : str ):
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
__lowercase = [start]
__lowercase = set(lowerCamelCase_ )
# Keep tab on distances from `start` node.
__lowercase = {start: 0, target: -1}
while queue:
__lowercase = queue.pop(0 )
if node == target:
__lowercase = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(lowerCamelCase_ )
queue.append(lowerCamelCase_ )
__lowercase = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 217
| 1
|
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("0.12.2"):
raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
raise Exception("requires fairseq < v2")
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Dict = "Hello, World!"
SCREAMING_SNAKE_CASE : Optional[Any] = "en_XX"
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
_lowercase : List[Any] = Path('data_bin' )
_lowercase : Optional[int] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(UpperCAmelCase_ ).parent ) , checkpoint_file=Path(UpperCAmelCase_ ).name , _name='xmod_base' , arch='xmod_base' , task='multilingual_masked_lm' , data_name_or_path=str(UpperCAmelCase_ ) , bpe='sentencepiece' , sentencepiece_model=str(Path(UpperCAmelCase_ ).parent / 'sentencepiece.bpe.model' ) , src_dict=str(data_dir / 'dict.txt' ) , )
xmod.eval() # disable dropout
print(UpperCAmelCase_ )
_lowercase : Optional[int] = xmod.model.encoder.sentence_encoder
_lowercase : Tuple = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , 'bottleneck' , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
_lowercase : List[str] = xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]
print('Our X-MOD config:' , UpperCAmelCase_ )
_lowercase : Any = XmodForSequenceClassification(UpperCAmelCase_ ) if classification_head else XmodForMaskedLM(UpperCAmelCase_ )
model.eval()
# Now let's copy all the weights.
# Embeddings
_lowercase : int = xmod_sent_encoder.embed_tokens.weight
_lowercase : str = xmod_sent_encoder.embed_positions.weight
_lowercase : List[Any] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
_lowercase : Optional[int] = xmod_sent_encoder.layernorm_embedding.weight
_lowercase : int = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
_lowercase : str = model.roberta.encoder.layer[i]
_lowercase : List[str] = xmod_sent_encoder.layers[i]
# self attention
_lowercase : List[Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError('Dimensions of self-attention weights do not match.' )
_lowercase : List[Any] = xmod_layer.self_attn.q_proj.weight
_lowercase : Dict = xmod_layer.self_attn.q_proj.bias
_lowercase : Dict = xmod_layer.self_attn.k_proj.weight
_lowercase : Tuple = xmod_layer.self_attn.k_proj.bias
_lowercase : List[Any] = xmod_layer.self_attn.v_proj.weight
_lowercase : Union[str, Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
_lowercase : List[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError('Dimensions of self-attention output weights do not match.' )
_lowercase : Tuple = xmod_layer.self_attn.out_proj.weight
_lowercase : Optional[Any] = xmod_layer.self_attn.out_proj.bias
_lowercase : Union[str, Any] = xmod_layer.self_attn_layer_norm.weight
_lowercase : List[Any] = xmod_layer.self_attn_layer_norm.bias
# intermediate
_lowercase : str = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('Dimensions of intermediate weights do not match.' )
_lowercase : List[str] = xmod_layer.fca.weight
_lowercase : List[Any] = xmod_layer.fca.bias
# output
_lowercase : Tuple = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('Dimensions of feed-forward weights do not match.' )
_lowercase : Optional[Any] = xmod_layer.fca.weight
_lowercase : List[str] = xmod_layer.fca.bias
_lowercase : Optional[int] = xmod_layer.final_layer_norm.weight
_lowercase : Dict = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
_lowercase : Any = xmod_layer.adapter_layer_norm.weight
_lowercase : Optional[Any] = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError('Lists of language adapters do not match.' )
for lang_code, adapter in xmod_layer.adapter_modules.items():
_lowercase : str = bert_output.adapter_modules[lang_code]
_lowercase : Dict = xmod_layer.adapter_modules[lang_code]
_lowercase : Any = from_adapter.fca.weight
_lowercase : int = from_adapter.fca.bias
_lowercase : Dict = from_adapter.fca.weight
_lowercase : Tuple = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
_lowercase : Any = xmod_sent_encoder.layer_norm.weight
_lowercase : List[str] = xmod_sent_encoder.layer_norm.bias
if classification_head:
_lowercase : Optional[Any] = xmod.model.classification_heads['mnli'].dense.weight
_lowercase : Optional[Any] = xmod.model.classification_heads['mnli'].dense.bias
_lowercase : List[Any] = xmod.model.classification_heads['mnli'].out_proj.weight
_lowercase : Optional[int] = xmod.model.classification_heads['mnli'].out_proj.bias
else:
# LM Head
_lowercase : Optional[Any] = xmod.model.encoder.lm_head.dense.weight
_lowercase : str = xmod.model.encoder.lm_head.dense.bias
_lowercase : List[Any] = xmod.model.encoder.lm_head.layer_norm.weight
_lowercase : List[str] = xmod.model.encoder.lm_head.layer_norm.bias
_lowercase : str = xmod.model.encoder.lm_head.weight
_lowercase : str = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
_lowercase : Optional[Any] = xmod.encode(UpperCAmelCase_ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(UpperCAmelCase_ )
_lowercase : Optional[Any] = model(UpperCAmelCase_ )[0]
if classification_head:
_lowercase : List[str] = xmod.model.classification_heads['mnli'](xmod.extract_features(UpperCAmelCase_ ) )
else:
_lowercase : str = xmod.model(UpperCAmelCase_ , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
_lowercase : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
_lowercase : Union[str, Any] = torch.allclose(UpperCAmelCase_ , UpperCAmelCase_ , atol=1e-3 )
print('Do both models output the same tensors?' , '🔥' if success else '💩' )
if not success:
raise Exception('Something went wRoNg' )
Path(UpperCAmelCase_ ).mkdir(parents=UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--xmod_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--classification_head", action="store_true", help="Whether to convert a final classification head."
)
SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 21
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A_ :
'''simple docstring'''
def __init__( self : Any , lowercase_ : List[Any] , lowercase_ : int=3 , lowercase_ : Dict=32 , lowercase_ : Optional[Any]=3 , lowercase_ : Tuple=10 , lowercase_ : Optional[Any]=[10, 20, 30, 40] , lowercase_ : List[str]=[1, 1, 2, 1] , lowercase_ : Optional[int]=True , lowercase_ : str=True , lowercase_ : Dict="relu" , lowercase_ : Optional[Any]=3 , lowercase_ : List[str]=None , ) -> int:
UpperCAmelCase : Dict = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Any = image_size
UpperCAmelCase : Any = num_channels
UpperCAmelCase : List[str] = embeddings_size
UpperCAmelCase : str = hidden_sizes
UpperCAmelCase : str = depths
UpperCAmelCase : Optional[int] = is_training
UpperCAmelCase : int = use_labels
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : List[Any] = num_labels
UpperCAmelCase : Union[str, Any] = scope
UpperCAmelCase : Any = len(lowercase_ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
return ResNetConfig(
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 UpperCAmelCase_ ( self : int , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = TFResNetModel(config=lowercase_ )
UpperCAmelCase : int = model(lowercase_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def UpperCAmelCase_ ( self : Any , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : Optional[int] ) -> List[Any]:
UpperCAmelCase : List[Any] = self.num_labels
UpperCAmelCase : Union[str, Any] = TFResNetForImageClassification(lowercase_ )
UpperCAmelCase : Any = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : str = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class A_ ( _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
UpperCAmelCase_ : Dict = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
UpperCAmelCase_ : Tuple = False
UpperCAmelCase_ : Tuple = False
UpperCAmelCase_ : List[Any] = False
UpperCAmelCase_ : str = False
UpperCAmelCase_ : Optional[int] = False
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
UpperCAmelCase : Optional[int] = TFResNetModelTester(self )
UpperCAmelCase : Dict = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ )
def UpperCAmelCase_ ( self : str ) -> Any:
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 UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
return
@unittest.skip(reason='ResNet does not use inputs_embeds' )
def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]:
pass
@unittest.skip(reason='ResNet does not support input and output embeddings' )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
pass
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(lowercase_ )
UpperCAmelCase : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Dict = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase_ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
def check_hidden_states_output(lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
UpperCAmelCase : Union[str, Any] = model_class(lowercase_ )
UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
UpperCAmelCase : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase : int = self.model_tester.num_stages
self.assertEqual(len(lowercase_ ) , expected_num_stages + 1 )
# ResNet'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] , )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
UpperCAmelCase : List[Any] = layer_type
UpperCAmelCase : int = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : List[Any] = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : Dict ) -> Tuple:
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : str = TFResNetModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def UpperCamelCase( ):
UpperCAmelCase : Optional[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class A_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Any:
UpperCAmelCase : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCAmelCase : Any = self.default_image_processor
UpperCAmelCase : List[Any] = prepare_img()
UpperCAmelCase : Dict = image_processor(images=lowercase_ , return_tensors='tf' )
# forward pass
UpperCAmelCase : List[Any] = model(**lowercase_ )
# verify the logits
UpperCAmelCase : Optional[Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowercase_ )
UpperCAmelCase : int = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowercase_ , atol=1E-4 ) )
| 151
| 0
|
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE : Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
@dataclass
class UpperCamelCase :
'''simple docstring'''
lowercase : Optional[str] =field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase : Optional[str] =field(
default=lowercase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase : Optional[str] =field(
default=lowercase__ , metadata={"""help""": """The column name of the images in the files."""} )
lowercase : Optional[str] =field(default=lowercase__ , metadata={"""help""": """A folder containing the training data."""} )
lowercase : Optional[str] =field(default=lowercase__ , metadata={"""help""": """A folder containing the validation data."""} )
lowercase : Optional[float] =field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase : Optional[int] =field(
default=lowercase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase : Optional[int] =field(
default=lowercase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def UpperCamelCase ( self ):
lowercase_ :int = {}
if self.train_dir is not None:
lowercase_ :Union[str, Any] = self.train_dir
if self.validation_dir is not None:
lowercase_ :int = self.validation_dir
lowercase_ :str = data_files if data_files else None
@dataclass
class UpperCamelCase :
'''simple docstring'''
lowercase : str =field(
default=lowercase__ , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
lowercase : Optional[str] =field(
default=lowercase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
lowercase : Optional[str] =field(
default=lowercase__ , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase : Optional[str] =field(
default=lowercase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowercase : str =field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase : str =field(default=lowercase__ , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase : bool =field(
default=lowercase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase : float =field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
lowercase : bool =field(
default=lowercase__ , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
lowercase : float =field(
default=1E-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def UpperCamelCase ( _a ) -> int:
'''simple docstring'''
lowercase_ :Tuple = torch.stack([example['''pixel_values'''] for example in examples] )
return {"pixel_values": pixel_values}
def UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
lowercase_ :str = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase_ , lowercase_ , lowercase_ :int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase_ , lowercase_ , lowercase_ :Dict = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_mae''' , _a , _a )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase_ :Dict = training_args.get_process_log_level()
logger.setLevel(_a )
transformers.utils.logging.set_verbosity(_a )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(f"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
lowercase_ :Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase_ :List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset.
lowercase_ :Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowercase_ :Dict = None if '''validation''' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _a ) and data_args.train_val_split > 0.0:
lowercase_ :int = ds['''train'''].train_test_split(data_args.train_val_split )
lowercase_ :Tuple = split['''train''']
lowercase_ :Optional[int] = split['''test''']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase_ :int = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name:
lowercase_ :Union[str, Any] = ViTMAEConfig.from_pretrained(model_args.config_name , **_a )
elif model_args.model_name_or_path:
lowercase_ :Union[str, Any] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **_a )
else:
lowercase_ :str = ViTMAEConfig()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(f"New config: {config}" )
# adapt config
config.update(
{
'''mask_ratio''': model_args.mask_ratio,
'''norm_pix_loss''': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
lowercase_ :int = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **_a )
elif model_args.model_name_or_path:
lowercase_ :Dict = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **_a )
else:
lowercase_ :Optional[Any] = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
lowercase_ :Dict = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_a , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
lowercase_ :str = ViTMAEForPreTraining(_a )
if training_args.do_train:
lowercase_ :str = ds['''train'''].column_names
else:
lowercase_ :Tuple = ds['''validation'''].column_names
if data_args.image_column_name is not None:
lowercase_ :Optional[Any] = data_args.image_column_name
elif "image" in column_names:
lowercase_ :str = '''image'''
elif "img" in column_names:
lowercase_ :Any = '''img'''
else:
lowercase_ :Optional[Any] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
lowercase_ :int = image_processor.size['''shortest_edge''']
else:
lowercase_ :Union[str, Any] = (image_processor.size['''height'''], image_processor.size['''width'''])
lowercase_ :List[str] = Compose(
[
Lambda(lambda _a : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(_a , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(_a ):
lowercase_ :List[Any] = [transforms(_a ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
lowercase_ :Tuple = ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_a )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
lowercase_ :str = (
ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_a )
# Compute absolute learning rate
lowercase_ :Any = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
lowercase_ :str = training_args.base_learning_rate * total_train_batch_size / 2_5_6
# Initialize our trainer
lowercase_ :Any = Trainer(
model=_a , args=_a , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=_a , data_collator=_a , )
# Training
if training_args.do_train:
lowercase_ :Any = None
if training_args.resume_from_checkpoint is not None:
lowercase_ :Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase_ :Tuple = last_checkpoint
lowercase_ :List[Any] = trainer.train(resume_from_checkpoint=_a )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase_ :str = trainer.evaluate()
trainer.log_metrics('''eval''' , _a )
trainer.save_metrics('''eval''' , _a )
# Write model card and (optionally) push to hub
lowercase_ :List[Any] = {
'''tasks''': '''masked-auto-encoding''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''masked-auto-encoding'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_a )
else:
trainer.create_model_card(**_a )
def UpperCamelCase ( _a ) -> str:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 252
|
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase ( self ):
lowercase_ :int = logging.get_logger()
# the current default level is logging.WARNING
lowercase_ :List[str] = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(UpperCamelCase_ )
def UpperCamelCase ( self ):
lowercase_ :Tuple = logging.get_verbosity()
lowercase_ :str = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
lowercase_ :Tuple = '''Testing 1, 2, 3'''
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(UpperCamelCase_ ) as cl:
logger.warning(UpperCamelCase_ )
self.assertEqual(cl.out , msg + '''\n''' )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(UpperCamelCase_ ) as cl:
logger.warning(UpperCamelCase_ )
self.assertEqual(cl.out , '''''' )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(UpperCamelCase_ ) as cl:
logger.warning(UpperCamelCase_ )
self.assertEqual(cl.out , msg + '''\n''' )
# restore to the original level
logging.set_verbosity(UpperCamelCase_ )
@mockenv(TRANSFORMERS_VERBOSITY='''error''' )
def UpperCamelCase ( self ):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
lowercase_ :Any = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
lowercase_ :Optional[Any] = os.getenv('''TRANSFORMERS_VERBOSITY''' , UpperCamelCase_ )
lowercase_ :Any = logging.log_levels[env_level_str]
lowercase_ :Optional[int] = logging.get_verbosity()
self.assertEqual(
UpperCamelCase_ , UpperCamelCase_ , f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}" , )
# restore to the original level
lowercase_ :str = ''''''
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY='''super-error''' )
def UpperCamelCase ( self ):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
lowercase_ :Any = logging.logging.getLogger()
with CaptureLogger(UpperCamelCase_ ) as cl:
# this action activates the env var
logging.get_logger('''transformers.models.bart.tokenization_bart''' )
self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error''' , cl.out )
# no need to restore as nothing was changed
def UpperCamelCase ( self ):
# testing `logger.warning_advice()`
transformers.utils.logging._reset_library_root_logger()
lowercase_ :Optional[int] = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
lowercase_ :Any = '''Testing 1, 2, 3'''
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1''' ):
# nothing should be logged as env var disables this method
with CaptureLogger(UpperCamelCase_ ) as cl:
logger.warning_advice(UpperCamelCase_ )
self.assertEqual(cl.out , '''''' )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''''' ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(UpperCamelCase_ ) as cl:
logger.warning_advice(UpperCamelCase_ )
self.assertEqual(cl.out , msg + '''\n''' )
def UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 252
| 1
|
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=_lowerCamelCase ):
lowerCAmelCase__ = ["""speech"""]
def __init__(self :int , *_UpperCamelCase :Dict , **_UpperCamelCase :Optional[int] )-> Union[str, Any]:
requires_backends(self , ['''speech'''] )
class A_ ( metaclass=_lowerCamelCase ):
lowerCAmelCase__ = ["""speech"""]
def __init__(self :Union[str, Any] , *_UpperCamelCase :Optional[int] , **_UpperCamelCase :int )-> Dict:
requires_backends(self , ['''speech'''] )
| 117
|
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
snake_case__ : Union[str, Any] = 500000
snake_case__ , snake_case__ : Optional[Any] = os.path.split(__file__)
snake_case__ : List[Any] = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def _a ( lowerCamelCase: datasets.Dataset , **lowerCamelCase: Optional[int] ) -> str:
'''simple docstring'''
__A = dataset.map(**lowerCamelCase )
@get_duration
def _a ( lowerCamelCase: datasets.Dataset , **lowerCamelCase: Optional[int] ) -> str:
'''simple docstring'''
__A = dataset.filter(**lowerCamelCase )
def _a ( ) -> List[Any]:
'''simple docstring'''
__A = {'''num examples''': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
__A = datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
__A = generate_example_dataset(
os.path.join(lowerCamelCase , '''dataset.arrow''' ) , lowerCamelCase , num_examples=lowerCamelCase )
__A = transformers.AutoTokenizer.from_pretrained('''bert-base-cased''' , use_fast=lowerCamelCase )
def tokenize(lowerCamelCase: List[str] ):
return tokenizer(examples['''text'''] )
__A = map(lowerCamelCase )
__A = map(lowerCamelCase , batched=lowerCamelCase )
__A = map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
with dataset.formatted_as(type='''numpy''' ):
__A = map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
with dataset.formatted_as(type='''pandas''' ):
__A = map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
with dataset.formatted_as(type='''torch''' , columns='''numbers''' ):
__A = map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
with dataset.formatted_as(type='''tensorflow''' , columns='''numbers''' ):
__A = map(lowerCamelCase , function=lambda lowerCamelCase : None , batched=lowerCamelCase )
__A = map(lowerCamelCase , function=lowerCamelCase , batched=lowerCamelCase )
__A = filter(lowerCamelCase )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(lowerCamelCase , '''wb''' ) as f:
f.write(json.dumps(lowerCamelCase ).encode('''utf-8''' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 117
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"configuration_timm_backbone": ["TimmBackboneConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["TimmBackbone"]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
a_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 352
|
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> float:
_validate_point(__lowercase )
_validate_point(__lowercase )
if len(__lowercase ) != len(__lowercase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(__lowercase , __lowercase ) ) )
def a__ ( __lowercase ) -> None:
if point:
if isinstance(__lowercase , __lowercase ):
for item in point:
if not isinstance(__lowercase , (int, float) ):
_A = (
"Expected a list of numbers as input, found "
f"""{type(__lowercase ).__name__}"""
)
raise TypeError(__lowercase )
else:
_A = f"""Expected a list of numbers as input, found {type(__lowercase ).__name__}"""
raise TypeError(__lowercase )
else:
raise ValueError("Missing an input" )
def a__ ( __lowercase , __lowercase ) -> float:
_validate_point(__lowercase )
_validate_point(__lowercase )
if len(__lowercase ) != len(__lowercase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(__lowercase , __lowercase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 163
| 0
|
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
A_ : Union[str, Any] = None
A_ : str = logging.get_logger(__name__)
A_ : Dict = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
A_ : Optional[int] = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
},
"tokenizer_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
},
}
# TODO(PVP) - this should be removed in Transformers v5
A_ : Optional[Any] = {
"t5-small": 512,
"t5-base": 512,
"t5-large": 512,
"t5-3b": 512,
"t5-11b": 512,
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : Tuple = VOCAB_FILES_NAMES
lowerCamelCase__ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : str = ["input_ids", "attention_mask"]
lowerCamelCase__ : Optional[int] = TaTokenizer
lowerCamelCase__ : List[int] = []
def __init__( self : Optional[int] , __UpperCAmelCase : str=None , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : Optional[int]="</s>" , __UpperCAmelCase : Tuple="<unk>" , __UpperCAmelCase : Optional[int]="<pad>" , __UpperCAmelCase : List[str]=1_0_0 , __UpperCAmelCase : Dict=None , **__UpperCAmelCase : Union[str, Any] , ) -> str:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
SCREAMING_SNAKE_CASE__ = [F"""<extra_id_{i}>""" for i in range(__UpperCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
SCREAMING_SNAKE_CASE__ = len(set(filter(lambda __UpperCAmelCase : bool("""extra_id_""" in str(__UpperCAmelCase ) ) , __UpperCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
""" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
""" tokens""" )
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , extra_ids=__UpperCAmelCase , additional_special_tokens=__UpperCAmelCase , **__UpperCAmelCase , )
SCREAMING_SNAKE_CASE__ = vocab_file
SCREAMING_SNAKE_CASE__ = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ = extra_ids
@staticmethod
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : str ) -> Any:
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
SCREAMING_SNAKE_CASE__ = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"""This tokenizer was incorrectly instantiated with a model max length of"""
F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
""" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
""" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
F""" {pretrained_model_name_or_path} automatically truncating your input to"""
F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
""" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
""" instantiate this tokenizer with `model_max_length` set to your preferred value.""" , __UpperCAmelCase , )
return max_model_length
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE__ = os.path.join(
__UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ):
copyfile(self.vocab_file , __UpperCAmelCase )
logger.info(F"""Copy vocab file to {out_vocab_file}""" )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE ( self : str , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
SCREAMING_SNAKE_CASE__ = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def SCREAMING_SNAKE_CASE ( self : Optional[int] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Dict:
return list(
set(filter(lambda __UpperCAmelCase : bool(re.search(r"""<extra_id_\d+>""" , __UpperCAmelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Union[str, Any]:
return [self.convert_tokens_to_ids(__UpperCAmelCase ) for token in self.get_sentinel_tokens()]
| 165
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
snake_case_ : List[str] = logging.get_logger(__name__)
snake_case_ : Union[str, Any] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
snake_case_ : Optional[int] = {
"vocab_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
),
},
}
snake_case_ : Optional[Any] = {
"yjernite/retribert-base-uncased": 5_12,
}
snake_case_ : Union[str, Any] = {
"yjernite/retribert-base-uncased": {"do_lower_case": True},
}
class __a (lowerCamelCase ):
__a : Optional[Any] = VOCAB_FILES_NAMES
__a : Dict = PRETRAINED_VOCAB_FILES_MAP
__a : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a : int = PRETRAINED_INIT_CONFIGURATION
__a : Union[str, Any] = RetriBertTokenizer
__a : Optional[int] = ["input_ids", "attention_mask"]
def __init__( self : str , __magic_name__ : List[Any]=None , __magic_name__ : Optional[int]=None , __magic_name__ : Any=True , __magic_name__ : int="[UNK]" , __magic_name__ : List[Any]="[SEP]" , __magic_name__ : List[Any]="[PAD]" , __magic_name__ : Optional[int]="[CLS]" , __magic_name__ : Union[str, Any]="[MASK]" , __magic_name__ : int=True , __magic_name__ : Optional[Any]=None , **__magic_name__ : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(
__magic_name__ , tokenizer_file=__magic_name__ , do_lower_case=__magic_name__ , unk_token=__magic_name__ , sep_token=__magic_name__ , pad_token=__magic_name__ , cls_token=__magic_name__ , mask_token=__magic_name__ , tokenize_chinese_chars=__magic_name__ , strip_accents=__magic_name__ , **__magic_name__ , )
UpperCAmelCase_ : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __magic_name__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __magic_name__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __magic_name__ ) != tokenize_chinese_chars
):
UpperCAmelCase_ : Dict = getattr(__magic_name__ , normalizer_state.pop('''type''' ) )
UpperCAmelCase_ : Optional[int] = do_lower_case
UpperCAmelCase_ : Optional[int] = strip_accents
UpperCAmelCase_ : Tuple = tokenize_chinese_chars
UpperCAmelCase_ : Optional[int] = normalizer_class(**__magic_name__ )
UpperCAmelCase_ : List[str] = do_lower_case
def UpperCAmelCase__ ( self : int , __magic_name__ : List[str] , __magic_name__ : Optional[Any]=None ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : str = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase__ ( self : Optional[Any] , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ : Dict = [self.sep_token_id]
UpperCAmelCase_ : Optional[int] = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
UpperCAmelCase_ : int = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
| 125
| 0
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
lowerCAmelCase_ = None
class _lowerCAmelCase ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = 2
@register_to_config
def __init__(self , UpperCAmelCase = 0.02 , UpperCAmelCase = 100 , UpperCAmelCase = 1.007 , UpperCAmelCase = 80 , UpperCAmelCase = 0.05 , UpperCAmelCase = 50 , ) -> Any:
# standard deviation of the initial noise distribution
_snake_case = sigma_max
# setable values
_snake_case = None
_snake_case = None
_snake_case = None # sigma(t_i)
def lowercase (self , UpperCAmelCase , UpperCAmelCase = None ) -> torch.FloatTensor:
return sample
def lowercase (self , UpperCAmelCase , UpperCAmelCase = None ) -> Tuple:
_snake_case = num_inference_steps
_snake_case = np.arange(0 , self.num_inference_steps )[::-1].copy()
_snake_case = torch.from_numpy(UpperCAmelCase ).to(UpperCAmelCase )
_snake_case = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
_snake_case = torch.tensor(UpperCAmelCase , dtype=torch.floataa , device=UpperCAmelCase )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = None ) -> Tuple[torch.FloatTensor, float]:
if self.config.s_min <= sigma <= self.config.s_max:
_snake_case = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
_snake_case = 0
# sample eps ~ N(0, S_noise^2 * I)
_snake_case = self.config.s_noise * randn_tensor(sample.shape , generator=UpperCAmelCase ).to(sample.device )
_snake_case = sigma + gamma * sigma
_snake_case = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = True , ) -> Union[KarrasVeOutput, Tuple]:
_snake_case = sample_hat + sigma_hat * model_output
_snake_case = (sample_hat - pred_original_sample) / sigma_hat
_snake_case = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=UpperCAmelCase , derivative=UpperCAmelCase , pred_original_sample=UpperCAmelCase )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = True , ) -> Union[KarrasVeOutput, Tuple]:
_snake_case = sample_prev + sigma_prev * model_output
_snake_case = (sample_prev - pred_original_sample) / sigma_prev
_snake_case = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=UpperCAmelCase , derivative=UpperCAmelCase , pred_original_sample=UpperCAmelCase )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> str:
raise NotImplementedError()
| 270
|
'''simple docstring'''
from random import randint, random
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = 5 , ):
_snake_case = [[-1] * number_of_cells] # Create a highway without any car
_snake_case = 0
_snake_case = max(_SCREAMING_SNAKE_CASE , 0 )
while i < number_of_cells:
_snake_case = (
randint(0 , _SCREAMING_SNAKE_CASE ) if random_speed else initial_speed
) # Place the cars
i += (
randint(1 , max_speed * 2 ) if random_frequency else frequency
) # Arbitrary number, may need tuning
return highway
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = 0
_snake_case = highway_now[car_index + 1 :]
for cell in range(len(_SCREAMING_SNAKE_CASE ) ): # May need a better name for this
if cells[cell] != -1: # If the cell is not empty then
return distance # we have the distance we wanted
distance += 1
# Here if the car is near the end of the highway
return distance + get_distance(_SCREAMING_SNAKE_CASE , -1 )
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = len(_SCREAMING_SNAKE_CASE )
# Beforce calculations, the highway is empty
_snake_case = [-1] * number_of_cells
for car_index in range(_SCREAMING_SNAKE_CASE ):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
_snake_case = min(highway_now[car_index] + 1 , _SCREAMING_SNAKE_CASE )
# Number of empty cell before the next car
_snake_case = get_distance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) - 1
# We can't have the car causing an accident
_snake_case = min(next_highway[car_index] , _SCREAMING_SNAKE_CASE )
if random() < probability:
# Randomly, a driver will slow down
_snake_case = max(next_highway[car_index] - 1 , 0 )
return next_highway
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = len(highway[0] )
for i in range(_SCREAMING_SNAKE_CASE ):
_snake_case = update(highway[i] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_snake_case = [-1] * number_of_cells
for car_index in range(_SCREAMING_SNAKE_CASE ):
_snake_case = next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
_snake_case = (car_index + speed) % number_of_cells
# Commit the change of position
_snake_case = speed
highway.append(_SCREAMING_SNAKE_CASE )
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| 270
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__snake_case : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[str] = ['MLukeTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
__snake_case : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 269
|
"""simple docstring"""
def _lowercase ( ) -> int:
return 1
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(__snake_case )
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else ten_pence(x - 10 ) + five_pence(__snake_case )
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else twenty_pence(x - 20 ) + ten_pence(__snake_case )
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else fifty_pence(x - 50 ) + twenty_pence(__snake_case )
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else one_pound(x - 100 ) + fifty_pence(__snake_case )
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else two_pound(x - 200 ) + one_pound(__snake_case )
def _lowercase ( __snake_case = 200 ) -> int:
return two_pound(__snake_case )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 269
| 1
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( _lowerCAmelCase ):
@staticmethod
@abstractmethod
def _UpperCAmelCase ( A_ ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def _UpperCAmelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
| 370
|
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: float | Decimal , lowerCAmelCase: float = 10**-10 ) -> float:
_UpperCAmelCase : Optional[int] = a
while True:
_UpperCAmelCase : Tuple = Decimal(lowerCAmelCase ) - (
Decimal(eval(lowerCAmelCase ) ) / Decimal(eval(str(diff(lowerCAmelCase ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(lowerCAmelCase ) ) < precision: # noqa: S307
return float(lowerCAmelCase )
# 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
print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
# Find Square Root of 5
print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
# Exponential Roots
print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')
| 189
| 0
|
from __future__ import annotations
def lowerCAmelCase_ ( __A ) -> list[int]:
'''simple docstring'''
return [ord(__A ) - 96 for elem in plain]
def lowerCAmelCase_ ( __A ) -> str:
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
UpperCAmelCase__ = encode(input("-> " ).strip().lower() )
print("Encoded: ", __A )
print("Decoded:", decode(__A ) )
if __name__ == "__main__":
main()
| 65
|
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class A__ ( unittest.TestCase):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=30 , _SCREAMING_SNAKE_CASE=4_00 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=1 / 2_55 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__lowerCAmelCase : Any = size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
__lowerCAmelCase : Optional[int] = parent
__lowerCAmelCase : int = batch_size
__lowerCAmelCase : str = num_channels
__lowerCAmelCase : Optional[int] = min_resolution
__lowerCAmelCase : List[Any] = max_resolution
__lowerCAmelCase : Union[str, Any] = do_resize
__lowerCAmelCase : Optional[Any] = size
__lowerCAmelCase : Dict = do_rescale
__lowerCAmelCase : Optional[Any] = rescale_factor
__lowerCAmelCase : Any = do_normalize
__lowerCAmelCase : List[str] = image_mean
__lowerCAmelCase : Union[str, Any] = image_std
__lowerCAmelCase : Optional[int] = do_pad
def __lowerCamelCase ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
if not batched:
__lowerCAmelCase : str = image_inputs[0]
if isinstance(_SCREAMING_SNAKE_CASE , Image.Image ):
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = image.size
else:
__lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = image.shape[1], image.shape[2]
if w < h:
__lowerCAmelCase : str = int(self.size['shortest_edge'] * h / w )
__lowerCAmelCase : Optional[int] = self.size['shortest_edge']
elif w > h:
__lowerCAmelCase : str = self.size['shortest_edge']
__lowerCAmelCase : Union[str, Any] = int(self.size['shortest_edge'] * w / h )
else:
__lowerCAmelCase : str = self.size['shortest_edge']
__lowerCAmelCase : Optional[Any] = self.size['shortest_edge']
else:
__lowerCAmelCase : str = []
for image in image_inputs:
__lowerCAmelCase , __lowerCAmelCase : List[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__lowerCAmelCase : Any = max(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : item[0] )[0]
__lowerCAmelCase : Dict = max(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A__ ( _lowerCamelCase , unittest.TestCase):
A_ : List[str] = DetrImageProcessor if is_vision_available() else None
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[Any] = DetrImageProcessingTester(self )
@property
def __lowerCamelCase ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'image_mean' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'image_std' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_normalize' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_rescale' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'rescale_factor' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_resize' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'size' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_pad' ) )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 13_33} )
self.assertEqual(image_processor.do_pad , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
# Initialize image_processing
__lowerCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCAmelCase : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
__lowerCAmelCase : int = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase : List[Any] = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCAmelCase , __lowerCAmelCase : int = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self ):
# Initialize image_processing
__lowerCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCAmelCase : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , numpify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
__lowerCAmelCase : Tuple = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase : Tuple = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCAmelCase : Union[str, Any] = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase : List[Any] = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCamelCase ( self ):
# Initialize image_processing
__lowerCAmelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCAmelCase : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , torchify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
__lowerCAmelCase : Any = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase : Dict = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCAmelCase : Tuple = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase : Any = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __lowerCamelCase ( self ):
# prepare image and target
__lowerCAmelCase : Union[str, Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
__lowerCAmelCase : Any = json.loads(f.read() )
__lowerCAmelCase : Tuple = {'image_id': 3_97_69, 'annotations': target}
# encode them
__lowerCAmelCase : Dict = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
__lowerCAmelCase : int = image_processing(images=_SCREAMING_SNAKE_CASE , annotations=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
# verify pixel values
__lowerCAmelCase : str = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['pixel_values'].shape , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) )
# verify area
__lowerCAmelCase : List[str] = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _SCREAMING_SNAKE_CASE ) )
# verify boxes
__lowerCAmelCase : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _SCREAMING_SNAKE_CASE , atol=1E-3 ) )
# verify image_id
__lowerCAmelCase : Dict = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _SCREAMING_SNAKE_CASE ) )
# verify is_crowd
__lowerCAmelCase : Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _SCREAMING_SNAKE_CASE ) )
# verify class_labels
__lowerCAmelCase : Union[str, Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _SCREAMING_SNAKE_CASE ) )
# verify orig_size
__lowerCAmelCase : int = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _SCREAMING_SNAKE_CASE ) )
# verify size
__lowerCAmelCase : List[Any] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _SCREAMING_SNAKE_CASE ) )
@slow
def __lowerCamelCase ( self ):
# prepare image, target and masks_path
__lowerCAmelCase : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
__lowerCAmelCase : Optional[int] = json.loads(f.read() )
__lowerCAmelCase : Optional[int] = {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}
__lowerCAmelCase : Union[str, Any] = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
__lowerCAmelCase : Optional[int] = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
__lowerCAmelCase : Optional[Any] = image_processing(images=_SCREAMING_SNAKE_CASE , annotations=_SCREAMING_SNAKE_CASE , masks_path=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
# verify pixel values
__lowerCAmelCase : str = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['pixel_values'].shape , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) )
# verify area
__lowerCAmelCase : int = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _SCREAMING_SNAKE_CASE ) )
# verify boxes
__lowerCAmelCase : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _SCREAMING_SNAKE_CASE , atol=1E-3 ) )
# verify image_id
__lowerCAmelCase : str = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _SCREAMING_SNAKE_CASE ) )
# verify is_crowd
__lowerCAmelCase : Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _SCREAMING_SNAKE_CASE ) )
# verify class_labels
__lowerCAmelCase : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _SCREAMING_SNAKE_CASE ) )
# verify masks
__lowerCAmelCase : Dict = 82_28_73
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , _SCREAMING_SNAKE_CASE )
# verify orig_size
__lowerCAmelCase : str = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _SCREAMING_SNAKE_CASE ) )
# verify size
__lowerCAmelCase : List[Any] = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _SCREAMING_SNAKE_CASE ) )
| 86
| 0
|
def __lowercase ( a__ ) -> int:
if not head:
return True
# split the list to two parts
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = head.next, head
while fast and fast.next:
__SCREAMING_SNAKE_CASE = fast.next.next
__SCREAMING_SNAKE_CASE = slow.next
__SCREAMING_SNAKE_CASE = slow.next
__SCREAMING_SNAKE_CASE = None # Don't forget here! But forget still works!
# reverse the second part
__SCREAMING_SNAKE_CASE = None
while second:
__SCREAMING_SNAKE_CASE = second.next
__SCREAMING_SNAKE_CASE = node
__SCREAMING_SNAKE_CASE = second
__SCREAMING_SNAKE_CASE = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
__SCREAMING_SNAKE_CASE = node.next
__SCREAMING_SNAKE_CASE = head.next
return True
def __lowercase ( a__ ) -> List[str]:
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
__SCREAMING_SNAKE_CASE = __SCREAMING_SNAKE_CASE = __SCREAMING_SNAKE_CASE = head
while fast and fast.next:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = fast.next.next, slow.next
# 2. Push the second half into the stack
__SCREAMING_SNAKE_CASE = [slow.val]
while slow.next:
__SCREAMING_SNAKE_CASE = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
__SCREAMING_SNAKE_CASE = cur.next
return True
def __lowercase ( a__ ) -> List[str]:
if not head or not head.next:
return True
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = 0
while head:
if head.val in d:
d[head.val].append(a__ )
else:
__SCREAMING_SNAKE_CASE = [pos]
__SCREAMING_SNAKE_CASE = head.next
pos += 1
__SCREAMING_SNAKE_CASE = pos - 1
__SCREAMING_SNAKE_CASE = 0
for v in d.values():
if len(a__ ) % 2 != 0:
middle += 1
else:
__SCREAMING_SNAKE_CASE = 0
for i in range(0 , len(a__ ) ):
if v[i] + v[len(a__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 350
|
import os
def __lowercase ( a__ = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(a__ ) , a__ ) ) as input_file:
__SCREAMING_SNAKE_CASE = [
[int(a__ ) for element in line.split(',' )]
for line in input_file.readlines()
]
__SCREAMING_SNAKE_CASE = len(a__ )
__SCREAMING_SNAKE_CASE = len(matrix[0] )
__SCREAMING_SNAKE_CASE = [[-1 for _ in range(a__ )] for _ in range(a__ )]
for i in range(a__ ):
__SCREAMING_SNAKE_CASE = matrix[i][0]
for j in range(1 , a__ ):
for i in range(a__ ):
__SCREAMING_SNAKE_CASE = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , a__ ):
__SCREAMING_SNAKE_CASE = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__SCREAMING_SNAKE_CASE = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 118
| 0
|
import math
import unittest
def __magic_name__ ( A : int ):
'''simple docstring'''
assert isinstance(A, A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(A ) + 1 ), 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : List[Any] ) -> Optional[int]:
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def __UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
with self.assertRaises(__lowerCamelCase ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) , "Zero doesn't have any positive factors, primes must have exactly two." , )
self.assertFalse(
is_prime(1 ) , "One only has 1 positive factor, primes must have exactly two." , )
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 107
|
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
__lowerCAmelCase : List[Any] = '0.12' # assumed parallelism: 8
if is_torch_available():
import torch
def __magic_name__ ( A : Dict, A : Union[str, Any], A : Optional[int]=None ):
'''simple docstring'''
if rng is None:
a = random.Random()
a = 1
for dim in shape:
total_dims *= dim
a = []
for _ in range(A ):
values.append(rng.randint(0, vocab_size - 1 ) )
a = np.array(A, dtype=jnp.intaa ).reshape(A )
return output
def __magic_name__ ( A : Dict, A : Union[str, Any]=None ):
'''simple docstring'''
a = ids_tensor(A, vocab_size=2, rng=A )
# make sure that at least one token is attended to for each batch
a = 1
return attn_mask
@require_flax
class snake_case__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = None
SCREAMING_SNAKE_CASE_ : Any = ()
def __UpperCAmelCase ( self : int ) -> List[str]:
a , a = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
a = 2
a = inputs["input_ids"].shape[-1] // 2
a = inputs["input_ids"][:max_batch_size, :sequence_length]
a = jnp.ones_like(__lowerCamelCase )
a = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
a = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
a = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def __UpperCAmelCase ( self : Optional[Any] ) -> int:
a , a , a , a = self._get_input_ids_and_config()
a = False
a = max_length
a = 0
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model_class.__name__[4:] # Skip the "Flax" at the beginning
a = getattr(__lowerCamelCase , __lowerCamelCase )
a = pt_model_class(__lowerCamelCase ).eval()
a = load_flax_weights_in_pytorch_model(__lowerCamelCase , flax_model.params )
a = flax_model.generate(__lowerCamelCase ).sequences
a = pt_model.generate(torch.tensor(__lowerCamelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
a = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def __UpperCAmelCase ( self : List[str] ) -> Optional[int]:
a , a , a , a = self._get_input_ids_and_config()
a = False
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __UpperCAmelCase ( self : Optional[int] ) -> Any:
a , a , a , a = self._get_input_ids_and_config()
a = True
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __UpperCAmelCase ( self : int ) -> Dict:
a , a , a , a = self._get_input_ids_and_config()
a = False
a = max_length
a = 2
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __UpperCAmelCase ( self : Any ) -> Union[str, Any]:
a , a , a , a = self._get_input_ids_and_config()
a = False
a = max_length
a = 2
a = 2
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def __UpperCAmelCase ( self : Optional[Any] ) -> Dict:
a , a , a , a = self._get_input_ids_and_config()
a = True
a = max_length
a = 0.8
a = 10
a = 0.3
a = 1
a = 8
a = 9
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
a , a , a , a = self._get_input_ids_and_config()
a = max_length
a = 1
a = 8
a = 9
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
a , a , a , a = self._get_input_ids_and_config()
a = max_length
a = 2
a = 1
a = 8
a = 9
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
a , a , a , a = self._get_input_ids_and_config()
# pad attention mask on the left
a = attention_mask.at[(0, 0)].set(0 )
a = False
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase , attention_mask=__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase , attention_mask=__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __UpperCAmelCase ( self : Tuple ) -> Tuple:
a , a , a , a = self._get_input_ids_and_config()
# pad attention mask on the left
a = attention_mask.at[(0, 0)].set(0 )
a = True
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase , attention_mask=__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase , attention_mask=__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def __UpperCAmelCase ( self : Optional[int] ) -> List[Any]:
a , a , a , a = self._get_input_ids_and_config()
# pad attention mask on the left
a = attention_mask.at[(0, 0)].set(0 )
a = 2
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCamelCase )
a = model.generate(__lowerCamelCase , attention_mask=__lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCamelCase )
a = jit(model.generate )
a = jit_generate(__lowerCamelCase , attention_mask=__lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Dict ) -> Optional[Any]:
a = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
a = FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
a = "Hello world"
a = tokenizer(__lowerCamelCase , return_tensors="np" ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(__lowerCamelCase , "do_samples" ):
model.generate(__lowerCamelCase , do_samples=__lowerCamelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(__lowerCamelCase , "foo" ):
a = {"foo": "bar"}
model.generate(__lowerCamelCase , **__lowerCamelCase )
| 107
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase ( a__ : List[str] , a__ : Optional[int] , a__ : Tuple ) -> Tuple:
if openai_config_file == "":
_UpperCamelCase = OpenAIGPTConfig()
else:
_UpperCamelCase = OpenAIGPTConfig.from_json_file(a__ )
_UpperCamelCase = OpenAIGPTModel(a__ )
# Load weights from numpy
load_tf_weights_in_openai_gpt(a__ , a__ , a__ )
# Save pytorch-model
_UpperCamelCase = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
_UpperCamelCase = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(F'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(model.state_dict() , a__ )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(a__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--openai_checkpoint_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the TensorFlow checkpoint path.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--openai_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
UpperCAmelCase = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 354
|
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCAmelCase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
UpperCAmelCase = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def lowercase ( a__ : list[list[int]] ) -> list[list[int]]:
_UpperCamelCase = []
for i in range(len(a__ ) ):
_UpperCamelCase = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
_UpperCamelCase = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(a__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(a__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(a__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
_UpperCamelCase = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(a__ )
return next_generation
def lowercase ( a__ : list[list[int]] , a__ : int ) -> list[Image.Image]:
_UpperCamelCase = []
for _ in range(a__ ):
# Create output image
_UpperCamelCase = Image.new('''RGB''' , (len(cells[0] ), len(a__ )) )
_UpperCamelCase = img.load()
# Save cells to image
for x in range(len(a__ ) ):
for y in range(len(cells[0] ) ):
_UpperCamelCase = 255 - cells[y][x] * 255
_UpperCamelCase = (colour, colour, colour)
# Save image
images.append(a__ )
_UpperCamelCase = new_generation(a__ )
return images
if __name__ == "__main__":
UpperCAmelCase = generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 54
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class __A ( UpperCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase : List[str] = """bert-generation"""
def __init__( self : Any ,_snake_case : Tuple=50_358 ,_snake_case : Union[str, Any]=1_024 ,_snake_case : Any=24 ,_snake_case : Optional[Any]=16 ,_snake_case : Tuple=4_096 ,_snake_case : Optional[Any]="gelu" ,_snake_case : Tuple=0.1 ,_snake_case : Optional[int]=0.1 ,_snake_case : Dict=512 ,_snake_case : Union[str, Any]=0.02 ,_snake_case : Optional[Any]=1e-12 ,_snake_case : Optional[Any]=0 ,_snake_case : int=2 ,_snake_case : Tuple=1 ,_snake_case : int="absolute" ,_snake_case : Tuple=True ,**_snake_case : Optional[Any] ,) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=__lowercase ,bos_token_id=__lowercase ,eos_token_id=__lowercase ,**__lowercase )
lowercase__ : List[str] = vocab_size
lowercase__ : Any = hidden_size
lowercase__ : List[Any] = num_hidden_layers
lowercase__ : str = num_attention_heads
lowercase__ : Optional[Any] = hidden_act
lowercase__ : Tuple = intermediate_size
lowercase__ : int = hidden_dropout_prob
lowercase__ : Optional[int] = attention_probs_dropout_prob
lowercase__ : Any = max_position_embeddings
lowercase__ : List[Any] = initializer_range
lowercase__ : Dict = layer_norm_eps
lowercase__ : Optional[Any] = position_embedding_type
lowercase__ : Dict = use_cache
| 16
|
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case__( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = JukeboxTokenizer
SCREAMING_SNAKE_CASE__ : int = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def lowercase_ ( self ) -> Union[str, Any]:
import torch
lowerCAmelCase_ : Union[str, Any] = JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
lowerCAmelCase_ : Any = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : List[str] = [
torch.tensor([[
0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
7_6, 7_6]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def lowercase_ ( self ) -> List[Any]:
import torch
lowerCAmelCase_ : Any = JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
lowerCAmelCase_ : str = tokenizer(**self.metas )['''input_ids''']
# fmt: off
lowerCAmelCase_ : Tuple = [
torch.tensor([[
0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 7_7, 7_7]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 262
| 0
|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class A ( unittest.TestCase , A_ ):
def _A (self ):
__lowercase= load_tool('text-classification' )
self.tool.setup()
__lowercase= load_tool('text-classification' , remote=lowerCAmelCase )
def _A (self ):
__lowercase= self.tool('That\'s quite cool' , ['positive', 'negative'] )
self.assertEqual(lowerCAmelCase , 'positive' )
def _A (self ):
__lowercase= self.remote_tool('That\'s quite cool' , ['positive', 'negative'] )
self.assertEqual(lowerCAmelCase , 'positive' )
def _A (self ):
__lowercase= self.tool(text='That\'s quite cool' , labels=['positive', 'negative'] )
self.assertEqual(lowerCAmelCase , 'positive' )
def _A (self ):
__lowercase= self.remote_tool(text='That\'s quite cool' , labels=['positive', 'negative'] )
self.assertEqual(lowerCAmelCase , 'positive' )
| 353
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowerCAmelCase = (3, 9, -1_1, 0, 7, 5, 1, -1)
lowerCAmelCase = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class A :
UpperCamelCase_ : int
UpperCamelCase_ : Node | None
class A :
def __init__(self , lowerCAmelCase ):
__lowercase= None
for i in sorted(lowerCAmelCase , reverse=lowerCAmelCase ):
__lowercase= Node(lowerCAmelCase , self.head )
def __iter__(self ):
__lowercase= self.head
while node:
yield node.data
__lowercase= node.next_node
def __len__(self ):
return sum(1 for _ in self )
def __str__(self ):
return " -> ".join([str(lowerCAmelCase ) for node in self] )
def _lowerCamelCase( lowercase__ , lowercase__ ) -> SortedLinkedList:
'''simple docstring'''
return SortedLinkedList(list(lowercase__ ) + list(lowercase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 304
| 0
|
'''simple docstring'''
def _lowerCamelCase ( lowercase : float , lowercase : int ) -> float:
if digit_amount > 0:
return round(number - int(lowercase ) , lowercase )
return number - int(lowercase )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 63
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
if dist[i][j] != float('''inf''' ):
print(int(dist[i][j] ) , end='''\t''' )
else:
print('''INF''' , end='''\t''' )
print()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : str = [[float('''inf''' ) for _ in range(__lowerCamelCase )] for _ in range(__lowerCamelCase )]
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
lowercase__ : List[str] = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(__lowerCamelCase ):
# looping through rows of graph array
for i in range(__lowerCamelCase ):
# looping through columns of graph array
for j in range(__lowerCamelCase ):
if (
dist[i][k] != float('''inf''' )
and dist[k][j] != float('''inf''' )
and dist[i][k] + dist[k][j] < dist[i][j]
):
lowercase__ : str = dist[i][k] + dist[k][j]
_print_dist(__lowerCamelCase , __lowerCamelCase )
return dist, v
if __name__ == "__main__":
lowerCAmelCase_ = int(input('Enter number of vertices: '))
lowerCAmelCase_ = int(input('Enter number of edges: '))
lowerCAmelCase_ = [[float('inf') for i in range(v)] for j in range(v)]
for i in range(v):
lowerCAmelCase_ = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print('\nEdge ', i + 1)
lowerCAmelCase_ = int(input('Enter source:'))
lowerCAmelCase_ = int(input('Enter destination:'))
lowerCAmelCase_ = float(input('Enter weight:'))
lowerCAmelCase_ = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 16
| 0
|
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) == 0 )
def __lowerCamelCase ( ):
"""simple docstring"""
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
if __name__ == "__main__":
test_and_gate()
print(and_gate(1, 0))
print(and_gate(0, 0))
print(and_gate(0, 1))
print(and_gate(1, 1))
| 121
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case__(unittest.TestCase ):
"""simple docstring"""
@property
def snake_case ( self : Any ):
torch.manual_seed(0 )
lowercase__ : Tuple = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
@property
def snake_case ( self : List[str] ):
torch.manual_seed(0 )
lowercase__ : Optional[int] = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , )
return model
@property
def snake_case ( self : Dict ):
torch.manual_seed(0 )
lowercase__ : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(SCREAMING_SNAKE_CASE )
def snake_case ( self : str ):
lowercase__ : Any = self.dummy_uncond_unet
lowercase__ : Dict = DDIMScheduler()
lowercase__ : Optional[Any] = self.dummy_vq_model
lowercase__ : Union[str, Any] = LDMPipeline(unet=SCREAMING_SNAKE_CASE , vqvae=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE )
ldm.to(SCREAMING_SNAKE_CASE )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
lowercase__ : int = torch.manual_seed(0 )
lowercase__ : Optional[int] = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" ).images
lowercase__ : str = torch.manual_seed(0 )
lowercase__ : List[Any] = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" , return_dict=SCREAMING_SNAKE_CASE )[0]
lowercase__ : Any = image[0, -3:, -3:, -1]
lowercase__ : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ : List[Any] = np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172] )
lowercase__ : Optional[Any] = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class snake_case__(unittest.TestCase ):
"""simple docstring"""
def snake_case ( self : Optional[Any] ):
lowercase__ : int = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256" )
ldm.to(SCREAMING_SNAKE_CASE )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
lowercase__ : Dict = torch.manual_seed(0 )
lowercase__ : Tuple = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=5 , output_type="numpy" ).images
lowercase__ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase__ : Optional[Any] = np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447] )
lowercase__ : int = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 121
| 1
|
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class A ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = tempfile.mkdtemp()
lowerCAmelCase_ = SamImageProcessor()
lowerCAmelCase_ = SamProcessor(UpperCamelCase__ )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname, **UpperCamelCase__ ).image_processor
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta )]
lowerCAmelCase_ = [Image.fromarray(np.moveaxis(UpperCamelCase__, 0, -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ = self.get_image_processor(do_normalize=UpperCamelCase__, padding_value=1.0 )
lowerCAmelCase_ = SamProcessor.from_pretrained(self.tmpdirname, do_normalize=UpperCamelCase__, padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = SamProcessor(image_processor=UpperCamelCase__ )
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = image_processor(UpperCamelCase__, return_tensors='''np''' )
lowerCAmelCase_ = processor(images=UpperCamelCase__, return_tensors='''np''' )
input_feat_extract.pop('''original_sizes''' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('''reshaped_input_sizes''' ) # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1E-2 )
@require_torch
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = SamProcessor(image_processor=UpperCamelCase__ )
lowerCAmelCase_ = [torch.ones((1, 3, 5, 5) )]
lowerCAmelCase_ = [[1764, 2646]]
lowerCAmelCase_ = [[683, 1024]]
lowerCAmelCase_ = processor.post_process_masks(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646) )
lowerCAmelCase_ = processor.post_process_masks(
UpperCamelCase__, torch.tensor(UpperCamelCase__ ), torch.tensor(UpperCamelCase__ ) )
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646) )
# should also work with np
lowerCAmelCase_ = [np.ones((1, 3, 5, 5) )]
lowerCAmelCase_ = processor.post_process_masks(UpperCamelCase__, np.array(UpperCamelCase__ ), np.array(UpperCamelCase__ ) )
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646) )
lowerCAmelCase_ = [[1, 0], [0, 1]]
with self.assertRaises(UpperCamelCase__ ):
lowerCAmelCase_ = processor.post_process_masks(UpperCamelCase__, np.array(UpperCamelCase__ ), np.array(UpperCamelCase__ ) )
@require_vision
@require_tf
class A ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = tempfile.mkdtemp()
lowerCAmelCase_ = SamImageProcessor()
lowerCAmelCase_ = SamProcessor(UpperCamelCase__ )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname, **UpperCamelCase__ ).image_processor
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta )]
lowerCAmelCase_ = [Image.fromarray(np.moveaxis(UpperCamelCase__, 0, -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ = self.get_image_processor(do_normalize=UpperCamelCase__, padding_value=1.0 )
lowerCAmelCase_ = SamProcessor.from_pretrained(self.tmpdirname, do_normalize=UpperCamelCase__, padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, UpperCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = SamProcessor(image_processor=UpperCamelCase__ )
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = image_processor(UpperCamelCase__, return_tensors='''np''' )
lowerCAmelCase_ = processor(images=UpperCamelCase__, return_tensors='''np''' )
input_feat_extract.pop('''original_sizes''' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('''reshaped_input_sizes''' ) # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1E-2 )
@require_tf
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = SamProcessor(image_processor=UpperCamelCase__ )
lowerCAmelCase_ = [tf.ones((1, 3, 5, 5) )]
lowerCAmelCase_ = [[1764, 2646]]
lowerCAmelCase_ = [[683, 1024]]
lowerCAmelCase_ = processor.post_process_masks(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, return_tensors='''tf''' )
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646) )
lowerCAmelCase_ = processor.post_process_masks(
UpperCamelCase__, tf.convert_to_tensor(UpperCamelCase__ ), tf.convert_to_tensor(UpperCamelCase__ ), return_tensors='''tf''', )
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646) )
# should also work with np
lowerCAmelCase_ = [np.ones((1, 3, 5, 5) )]
lowerCAmelCase_ = processor.post_process_masks(
UpperCamelCase__, np.array(UpperCamelCase__ ), np.array(UpperCamelCase__ ), return_tensors='''tf''' )
self.assertEqual(masks[0].shape, (1, 3, 1764, 2646) )
lowerCAmelCase_ = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
lowerCAmelCase_ = processor.post_process_masks(
UpperCamelCase__, np.array(UpperCamelCase__ ), np.array(UpperCamelCase__ ), return_tensors='''tf''' )
@require_vision
@require_torchvision
class A ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = tempfile.mkdtemp()
lowerCAmelCase_ = SamImageProcessor()
lowerCAmelCase_ = SamProcessor(UpperCamelCase__ )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname, **UpperCamelCase__ ).image_processor
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta )]
lowerCAmelCase_ = [Image.fromarray(np.moveaxis(UpperCamelCase__, 0, -1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = SamProcessor(image_processor=UpperCamelCase__ )
lowerCAmelCase_ = np.random.randint(0, 2, size=(1, 3, 5, 5) ).astype(np.floataa )
lowerCAmelCase_ = [tf.convert_to_tensor(UpperCamelCase__ )]
lowerCAmelCase_ = [torch.tensor(UpperCamelCase__ )]
lowerCAmelCase_ = [[1764, 2646]]
lowerCAmelCase_ = [[683, 1024]]
lowerCAmelCase_ = processor.post_process_masks(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, return_tensors='''tf''' )
lowerCAmelCase_ = processor.post_process_masks(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, return_tensors='''pt''' )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.get_image_processor()
lowerCAmelCase_ = SamProcessor(image_processor=UpperCamelCase__ )
lowerCAmelCase_ = self.prepare_image_inputs()
lowerCAmelCase_ = image_processor(UpperCamelCase__, return_tensors='''pt''' )['''pixel_values'''].numpy()
lowerCAmelCase_ = processor(images=UpperCamelCase__, return_tensors='''pt''' )['''pixel_values'''].numpy()
lowerCAmelCase_ = image_processor(UpperCamelCase__, return_tensors='''tf''' )['''pixel_values'''].numpy()
lowerCAmelCase_ = processor(images=UpperCamelCase__, return_tensors='''tf''' )['''pixel_values'''].numpy()
self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__ ) )
self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__ ) )
self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__ ) )
| 278
|
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A , _A ):
assert isinstance(_A , _A )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCAmelCase_ = JsonDatasetReader(_A , cache_dir=_A , keep_in_memory=_A ).read()
_check_json_dataset(_A , _A )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase_ = features.copy() if features else default_expected_features
lowerCAmelCase_ = (
Features({feature: Value(_A ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCAmelCase_ = JsonDatasetReader(_A , features=_A , cache_dir=_A ).read()
_check_json_dataset(_A , _A )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
lowerCAmelCase_ = features.copy() if features else default_expected_features
lowerCAmelCase_ = (
Features({feature: Value(_A ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCAmelCase_ = JsonDatasetReader(_A , features=_A , cache_dir=_A ).read()
assert isinstance(_A , _A )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def __UpperCamelCase ( _A , _A ):
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
lowerCAmelCase_ = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
lowerCAmelCase_ = features.copy()
lowerCAmelCase_ = (
Features({feature: Value(_A ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = JsonDatasetReader(_A , features=_A , cache_dir=_A ).read()
assert isinstance(_A , _A )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase_ = JsonDatasetReader(_A , cache_dir=_A , split=_A ).read()
_check_json_dataset(_A , _A )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def __UpperCamelCase ( _A , _A , _A ):
if issubclass(_A , _A ):
lowerCAmelCase_ = jsonl_path
elif issubclass(_A , _A ):
lowerCAmelCase_ = [jsonl_path]
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase_ = JsonDatasetReader(_A , cache_dir=_A ).read()
_check_json_dataset(_A , _A )
def __UpperCamelCase ( _A , _A , _A=("train",) ):
assert isinstance(_A , _A )
for split in splits:
lowerCAmelCase_ = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCAmelCase_ = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_A , keep_in_memory=_A ).read()
_check_json_datasetdict(_A , _A )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase_ = features.copy() if features else default_expected_features
lowerCAmelCase_ = (
Features({feature: Value(_A ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCAmelCase_ = JsonDatasetReader({'''train''': jsonl_path} , features=_A , cache_dir=_A ).read()
_check_json_datasetdict(_A , _A )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def __UpperCamelCase ( _A , _A , _A ):
if split:
lowerCAmelCase_ = {split: jsonl_path}
else:
lowerCAmelCase_ = '''train'''
lowerCAmelCase_ = {'''train''': jsonl_path, '''test''': jsonl_path}
lowerCAmelCase_ = tmp_path / '''cache'''
lowerCAmelCase_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase_ = JsonDatasetReader(_A , cache_dir=_A ).read()
_check_json_datasetdict(_A , _A , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def __UpperCamelCase ( _A ):
return json.load(_A )
def __UpperCamelCase ( _A ):
return [json.loads(_A ) for line in buffer]
class A :
@pytest.mark.parametrize('''lines, load_json_function''', [(True, load_json_lines), (False, load_json)] )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__, UpperCamelCase__, lines=UpperCamelCase__ ).write()
buffer.seek(0 )
lowerCAmelCase_ = load_json_function(UpperCamelCase__ )
assert isinstance(UpperCamelCase__, UpperCamelCase__ )
assert isinstance(exported_content[0], UpperCamelCase__ )
assert len(UpperCamelCase__ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''', [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
], )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__, UpperCamelCase__, lines=UpperCamelCase__, orient=UpperCamelCase__ ).write()
buffer.seek(0 )
lowerCAmelCase_ = load_json(UpperCamelCase__ )
assert isinstance(UpperCamelCase__, UpperCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase__, '''keys''' ) and not hasattr(exported_content[0], '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase__ ) == 10
@pytest.mark.parametrize('''lines, load_json_function''', [(True, load_json_lines), (False, load_json)] )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__, UpperCamelCase__, lines=UpperCamelCase__, num_proc=2 ).write()
buffer.seek(0 )
lowerCAmelCase_ = load_json_function(UpperCamelCase__ )
assert isinstance(UpperCamelCase__, UpperCamelCase__ )
assert isinstance(exported_content[0], UpperCamelCase__ )
assert len(UpperCamelCase__ ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''', [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
], )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__, UpperCamelCase__, lines=UpperCamelCase__, orient=UpperCamelCase__, num_proc=2 ).write()
buffer.seek(0 )
lowerCAmelCase_ = load_json(UpperCamelCase__ )
assert isinstance(UpperCamelCase__, UpperCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(UpperCamelCase__, '''keys''' ) and not hasattr(exported_content[0], '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(UpperCamelCase__ ) == 10
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
with pytest.raises(UpperCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(UpperCamelCase__, UpperCamelCase__, num_proc=0 )
@pytest.mark.parametrize('''compression, extension''', [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = tmp_path_factory.mktemp('''data''' ) / f"test.json.{extension}"
lowerCAmelCase_ = str(shared_datadir / f"test_file.json.{extension}" )
JsonDatasetWriter(UpperCamelCase__, UpperCamelCase__, compression=UpperCamelCase__ ).write()
with fsspec.open(UpperCamelCase__, '''rb''', compression='''infer''' ) as f:
lowerCAmelCase_ = f.read()
with fsspec.open(UpperCamelCase__, '''rb''', compression='''infer''' ) as f:
lowerCAmelCase_ = f.read()
assert exported_content == original_content
| 278
| 1
|
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase: Optional[Any] = logging.get_logger(__name__)
def a( A : Optional[Any] , A : List[str] , A : str , A : str ) -> Optional[Any]:
"""simple docstring"""
a = original_name.split("." )[0]
a = key.split("." )
a = int(key_list[key_list.index(A ) - 2] )
a = int(key_list[key_list.index(A ) - 1] )
a = orig_block_num - offset
a = key.replace(f'''{orig_block_num}.{layer_num}.{original_name}''' , f'''block.{new_block_num}.{layer_num}.{new_name}''' )
return key
def a( A : int ) -> Dict:
"""simple docstring"""
a = OrderedDict()
a , a = 0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
a = key.replace("network" , "poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
a = key[: key.find("proj" )]
a = key.replace(A , f'''patch_embeddings.{total_embed_found}.''' )
a = key.replace("proj" , "projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
a = "poolformer.encoder." + key
if "mlp.fc1" in key:
a = replace_key_with_offset(A , A , "mlp.fc1" , "output.conv1" )
if "mlp.fc2" in key:
a = replace_key_with_offset(A , A , "mlp.fc2" , "output.conv2" )
if "norm1" in key:
a = replace_key_with_offset(A , A , "norm1" , "before_norm" )
if "norm2" in key:
a = replace_key_with_offset(A , A , "norm2" , "after_norm" )
if "layer_scale_1" in key:
a = replace_key_with_offset(A , A , "layer_scale_1" , "layer_scale_1" )
if "layer_scale_2" in key:
a = replace_key_with_offset(A , A , "layer_scale_2" , "layer_scale_2" )
if "head" in key:
a = key.replace("head" , "classifier" )
a = value
return new_state_dict
def a( ) -> Dict:
"""simple docstring"""
a = "http://images.cocodataset.org/val2017/000000039769.jpg"
a = Image.open(requests.get(A , stream=A ).raw )
return image
@torch.no_grad()
def a( A : List[Any] , A : List[str] , A : str ) -> Optional[int]:
"""simple docstring"""
a = PoolFormerConfig()
# set attributes based on model_name
a = "huggingface/label-files"
a = model_name[-3:]
a = 1000
a = "imagenet-1k-id2label.json"
a = (1, 1000)
# set config attributes
a = json.load(open(hf_hub_download(A , A , repo_type="dataset" ) , "r" ) )
a = {int(A ): v for k, v in idalabel.items()}
a = idalabel
a = {v: k for k, v in idalabel.items()}
if size == "s12":
a = [2, 2, 6, 2]
a = [64, 128, 320, 512]
a = 4.0
a = 0.9
elif size == "s24":
a = [4, 4, 12, 4]
a = [64, 128, 320, 512]
a = 4.0
a = 0.9
elif size == "s36":
a = [6, 6, 18, 6]
a = [64, 128, 320, 512]
a = 4.0
a = 1e-6
a = 0.9
elif size == "m36":
a = [6, 6, 18, 6]
a = [96, 192, 384, 768]
a = 4.0
a = 1e-6
a = 0.95
elif size == "m48":
a = [8, 8, 24, 8]
a = [96, 192, 384, 768]
a = 4.0
a = 1e-6
a = 0.95
else:
raise ValueError(f'''Size {size} not supported''' )
# load image processor
a = PoolFormerImageProcessor(crop_pct=A )
# Prepare image
a = prepare_img()
a = image_processor(images=A , return_tensors="pt" ).pixel_values
logger.info(f'''Converting model {model_name}...''' )
# load original state dict
a = torch.load(A , map_location=torch.device("cpu" ) )
# rename keys
a = rename_keys(A )
# create HuggingFace model and load state dict
a = PoolFormerForImageClassification(A )
model.load_state_dict(A )
model.eval()
# Define image processor
a = PoolFormerImageProcessor(crop_pct=A )
a = image_processor(images=prepare_img() , return_tensors="pt" ).pixel_values
# forward pass
a = model(A )
a = outputs.logits
# define expected logit slices for different models
if size == "s12":
a = torch.tensor([-0.3_045, -0.6_758, -0.4_869] )
elif size == "s24":
a = torch.tensor([0.4_402, -0.1_374, -0.8_045] )
elif size == "s36":
a = torch.tensor([-0.6_080, -0.5_133, -0.5_898] )
elif size == "m36":
a = torch.tensor([0.3_952, 0.2_263, -1.2_668] )
elif size == "m48":
a = torch.tensor([0.1_167, -0.0_656, -0.3_423] )
else:
raise ValueError(f'''Size {size} not supported''' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , A , atol=1e-2 )
# finally, save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(A ).mkdir(exist_ok=A )
model.save_pretrained(A )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(A )
if __name__ == "__main__":
_lowercase: Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="poolformer_s12",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
_lowercase: Union[str, Any] = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 365
|
def a( A : int , A : float , A : float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def a( A : float , A : float , A : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_821 * temperature) / (volume) ) )
def a( A : float , A : float , A : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_821 * temperature) / (pressure) ) )
def a( A : float , A : float , A : float ) -> float:
"""simple docstring"""
return round(float((pressure * volume) / (0.0_821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 71
| 0
|
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class __lowerCAmelCase ( UpperCAmelCase__ ):
def UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCamelCase ( self : Dict ):
"""simple docstring"""
with self.assertRaises(snake_case__ ):
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def UpperCamelCase ( self : int ):
"""simple docstring"""
with self.assertRaises(snake_case__ ):
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] , try_type=Value("bool" ) , type=Value("int64" ) ) )
def UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] , type=Value("int32" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCamelCase ( self : Tuple ):
"""simple docstring"""
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
_UpperCAmelCase = pa.array(TypedSequence(["foo", "bar"] , type=Value("int64" ) ) )
def UpperCamelCase ( self : int ):
"""simple docstring"""
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] , try_type=Value("int32" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCamelCase ( self : Dict ):
"""simple docstring"""
_UpperCAmelCase = pa.array(TypedSequence(["foo", "bar"] , try_type=Value("int64" ) ) )
self.assertEqual(arr.type , pa.string() )
def UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
_UpperCAmelCase = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , "int64" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , "int64" ) )
def UpperCamelCase ( self : str ):
"""simple docstring"""
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
_UpperCAmelCase = pa.array(TypedSequence(["foo", "bar"] , type=ArrayaD((1, 3) , "int64" ) ) )
def UpperCamelCase ( self : str ):
"""simple docstring"""
_UpperCAmelCase = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , "int64" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , "int64" ) )
def UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
_UpperCAmelCase = pa.array(TypedSequence(["foo", "bar"] , try_type=ArrayaD((1, 3) , "int64" ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def UpperCamelCase ( self : Any ):
"""simple docstring"""
import PIL.Image
_UpperCAmelCase = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
"datasets.arrow_writer.cast_to_python_objects" , side_effect=snake_case__ ) as mock_cast_to_python_objects:
_UpperCAmelCase = pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image] , type=Image() ) )
_UpperCAmelCase , _UpperCAmelCase = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn("optimize_list_casting" , snake_case__ )
self.assertFalse(kwargs["optimize_list_casting"] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferReader(snake_case_ ) if isinstance(snake_case_ , pa.Buffer ) else pa.memory_map(snake_case_ )
_UpperCAmelCase = pa.ipc.open_stream(snake_case_ )
_UpperCAmelCase = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
@pytest.mark.parametrize(
"fields" , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ , schema=snake_case_ , writer_batch_size=snake_case_ ) as writer:
writer.write({"col_1": "foo", "col_2": 1} )
writer.write({"col_1": "bar", "col_2": 2} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def __SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = Features({"labels": ClassLabel(names=["neg", "pos"] )} )
with ArrowWriter(stream=snake_case_ , features=snake_case_ ) as writer:
writer.write({"labels": 0} )
writer.write({"labels": 1} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
_UpperCAmelCase = pa.BufferReader(output.getvalue() )
_UpperCAmelCase = pa.ipc.open_stream(snake_case_ )
_UpperCAmelCase = f.read_all()
_UpperCAmelCase = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(snake_case_ )
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ , writer_batch_size=snake_case_ , hash_salt="split_name" , check_duplicates=snake_case_ , ) as writer:
with pytest.raises(snake_case_ ):
writer.write({"col_1": "foo", "col_2": 1} , key=[1, 2] )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
@pytest.mark.parametrize("writer_batch_size" , [None, 2, 10] )
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ , writer_batch_size=snake_case_ , hash_salt="split_name" , check_duplicates=snake_case_ , ) as writer:
with pytest.raises(snake_case_ ):
writer.write({"col_1": "foo", "col_2": 1} , key=10 )
writer.write({"col_1": "bar", "col_2": 2} , key=10 )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
@pytest.mark.parametrize("writer_batch_size" , [None, 2, 10] )
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ , writer_batch_size=snake_case_ , hash_salt="split_name" , check_duplicates=snake_case_ , ) as writer:
writer.write({"col_1": "foo", "col_2": 1} , key=1 )
writer.write({"col_1": "bar", "col_2": 2} , key=2 )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
@pytest.mark.parametrize(
"fields" , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ , schema=snake_case_ , writer_batch_size=snake_case_ ) as writer:
writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]} )
writer.write_batch({"col_1": [], "col_2": []} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
@pytest.mark.parametrize(
"fields" , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ , schema=snake_case_ , writer_batch_size=snake_case_ ) as writer:
writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]} ) )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
@pytest.mark.parametrize(
"fields" , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ , schema=snake_case_ , writer_batch_size=snake_case_ ) as writer:
writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]} ) )
writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]} ) )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def __SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
_UpperCAmelCase = os.path.join(snake_case_ , "test.arrow" )
with ArrowWriter(path=snake_case_ , schema=pa.schema(snake_case_ ) ) as writer:
writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(snake_case_ , metadata=writer._schema.metadata )
_check_output(snake_case_ , 1 )
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
if pa.types.is_list(snake_case_ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
if isinstance(lst[0] , snake_case_ ):
change_first_primitive_element_in_list(lst[0] , snake_case_ )
else:
_UpperCAmelCase = value
@pytest.mark.parametrize("optimized_int_type, expected_dtype" , [(None, pa.intaa()), (Value("int32" ), pa.intaa())] )
@pytest.mark.parametrize("sequence" , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.array(TypedSequence(snake_case_ , optimized_int_type=snake_case_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
"col, expected_dtype" , [
("attention_mask", pa.inta()),
("special_tokens_mask", pa.inta()),
("token_type_ids", pa.inta()),
("input_ids", pa.intaa()),
("other", pa.intaa()),
] , )
@pytest.mark.parametrize("sequence" , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = pa.array(OptimizedTypedSequence(snake_case_ , col=snake_case_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
_UpperCAmelCase = copy.deepcopy(snake_case_ )
_UpperCAmelCase = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(snake_case_ , snake_case_ )
_UpperCAmelCase = pa.array(OptimizedTypedSequence(snake_case_ , col=snake_case_ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize("raise_exception" , [False, True] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = str(tmp_path / "dataset-train.arrow" )
try:
with ArrowWriter(path=snake_case_ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = "mock://dataset-train.arrow"
with ArrowWriter(path=snake_case_ , storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs , type(snake_case_ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({"col_1": "foo", "col_2": 1} )
writer.write({"col_1": "bar", "col_2": 2} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(snake_case_ )
def __SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
with ParquetWriter(stream=snake_case_ ) as writer:
writer.write({"col_1": "foo", "col_2": 1} )
writer.write({"col_1": "bar", "col_2": 2} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_UpperCAmelCase = pa.BufferReader(output.getvalue() )
_UpperCAmelCase = pq.read_table(snake_case_ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize("embed_local_files" , [False, True] )
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''simple docstring'''
import PIL.Image
_UpperCAmelCase = str(tmp_path / "test_image_rgb.jpg" )
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(snake_case_ , format="png" )
_UpperCAmelCase = pa.BufferOutputStream()
with ParquetWriter(
stream=snake_case_ , features=Features({"image": Image()} ) , embed_local_files=snake_case_ ) as writer:
writer.write({"image": image_path} )
writer.finalize()
_UpperCAmelCase = pa.BufferReader(output.getvalue() )
_UpperCAmelCase = pq.read_table(snake_case_ )
_UpperCAmelCase = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out["image"][0]["path"] , snake_case_ )
with open(snake_case_ , "rb" ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def __SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
_UpperCAmelCase = pa.schema([pa.field("col_1" , pa.string() , nullable=snake_case_ )] )
_UpperCAmelCase = pa.BufferOutputStream()
with ArrowWriter(stream=snake_case_ ) as writer:
writer._build_writer(inferred_schema=snake_case_ )
assert writer._schema == pa.schema([pa.field("col_1" , pa.string() )] )
| 133
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Dict = logging.get_logger(__name__)
lowercase_ : Union[str, Any] = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class __lowerCAmelCase ( UpperCAmelCase__ ):
snake_case_ : int = "ctrl"
snake_case_ : Optional[int] = ["past_key_values"]
snake_case_ : Tuple = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[str] , snake_case__ : List[str]=246_534 , snake_case__ : Optional[Any]=256 , snake_case__ : List[str]=1_280 , snake_case__ : Optional[int]=8_192 , snake_case__ : List[Any]=48 , snake_case__ : Dict=16 , snake_case__ : int=0.1 , snake_case__ : List[str]=0.1 , snake_case__ : Optional[int]=1e-6 , snake_case__ : Dict=0.02 , snake_case__ : List[Any]=True , **snake_case__ : List[str] , ):
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = n_positions
_UpperCAmelCase = n_embd
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = dff
_UpperCAmelCase = resid_pdrop
_UpperCAmelCase = embd_pdrop
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
super().__init__(**snake_case__ )
| 133
| 1
|
from math import factorial
snake_case__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def _a ( lowerCamelCase: int ) -> int:
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise TypeError('''Parameter number must be int''' )
if number < 0:
raise ValueError('''Parameter number must be greater than or equal to 0''' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(lowerCamelCase ) )
def _a ( lowerCamelCase: int = 60 , lowerCamelCase: int = 1_00_00_00 ) -> int:
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ) or not isinstance(lowerCamelCase , lowerCamelCase ):
raise TypeError('''Parameters chain_length and number_limit must be int''' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'''Parameters chain_length and number_limit must be greater than 0''' )
# the counter for the chains with the exact desired length
__A = 0
# the cached sizes of the previous chains
__A = {}
for start_chain_element in range(1 , lowerCamelCase ):
# The temporary set will contain the elements of the chain
__A = set()
__A = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__A = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(lowerCamelCase )
chain_set_length += 1
__A = digit_factorial_sum(lowerCamelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__A = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'{solution()}')
| 250
|
import sys
def _a ( lowerCamelCase: Tuple ) -> Tuple:
'''simple docstring'''
__A = len(lowerCamelCase )
__A = [[0 for x in range(lowerCamelCase )] for x in range(lowerCamelCase )]
__A = [[0 for x in range(lowerCamelCase )] for x in range(lowerCamelCase )]
for chain_length in range(2 , lowerCamelCase ):
for a in range(1 , n - chain_length + 1 ):
__A = a + chain_length - 1
__A = sys.maxsize
for c in range(lowerCamelCase , lowerCamelCase ):
__A = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
__A = cost
__A = c
return matrix, sol
def _a ( lowerCamelCase: Optional[int] , lowerCamelCase: Optional[Any] , lowerCamelCase: List[str] ) -> Tuple:
'''simple docstring'''
if i == j:
print('''A''' + str(lowerCamelCase ) , end=''' ''' )
else:
print('''(''' , end=''' ''' )
print_optiomal_solution(lowerCamelCase , lowerCamelCase , optimal_solution[i][j] )
print_optiomal_solution(lowerCamelCase , optimal_solution[i][j] + 1 , lowerCamelCase )
print(''')''' , end=''' ''' )
def _a ( ) -> List[str]:
'''simple docstring'''
__A = [30, 35, 15, 5, 10, 20, 25]
__A = len(lowerCamelCase )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
__A , __A = matrix_chain_order(lowerCamelCase )
print('''No. of Operation required: ''' + str(matrix[1][n - 1] ) )
print_optiomal_solution(lowerCamelCase , 1 , n - 1 )
if __name__ == "__main__":
main()
| 250
| 1
|
from ..utils import DummyObject, requires_backends
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Tuple = ['sentencepiece']
def __init__( self: int , *UpperCamelCase_: List[str] , **UpperCamelCase_: Optional[Any] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Optional[int] = ['sentencepiece']
def __init__( self: str , *UpperCamelCase_: Optional[Any] , **UpperCamelCase_: str ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Optional[int] = ['sentencepiece']
def __init__( self: str , *UpperCamelCase_: Optional[int] , **UpperCamelCase_: List[str] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Union[str, Any] = ['sentencepiece']
def __init__( self: List[str] , *UpperCamelCase_: Dict , **UpperCamelCase_: Optional[Any] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : List[Any] = ['sentencepiece']
def __init__( self: Optional[Any] , *UpperCamelCase_: Optional[Any] , **UpperCamelCase_: Optional[int] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Dict = ['sentencepiece']
def __init__( self: Any , *UpperCamelCase_: Union[str, Any] , **UpperCamelCase_: str ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Union[str, Any] = ['sentencepiece']
def __init__( self: Any , *UpperCamelCase_: int , **UpperCamelCase_: Optional[int] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Tuple = ['sentencepiece']
def __init__( self: Optional[int] , *UpperCamelCase_: Any , **UpperCamelCase_: str ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Optional[int] = ['sentencepiece']
def __init__( self: Dict , *UpperCamelCase_: Dict , **UpperCamelCase_: Optional[Any] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Optional[Any] = ['sentencepiece']
def __init__( self: Optional[Any] , *UpperCamelCase_: Tuple , **UpperCamelCase_: List[Any] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Dict = ['sentencepiece']
def __init__( self: List[str] , *UpperCamelCase_: int , **UpperCamelCase_: Dict ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Any = ['sentencepiece']
def __init__( self: Dict , *UpperCamelCase_: int , **UpperCamelCase_: Any ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Union[str, Any] = ['sentencepiece']
def __init__( self: int , *UpperCamelCase_: Any , **UpperCamelCase_: Optional[int] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Optional[int] = ['sentencepiece']
def __init__( self: int , *UpperCamelCase_: Optional[Any] , **UpperCamelCase_: Optional[int] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Optional[int] = ['sentencepiece']
def __init__( self: Optional[Any] , *UpperCamelCase_: Any , **UpperCamelCase_: Union[str, Any] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Any = ['sentencepiece']
def __init__( self: Optional[Any] , *UpperCamelCase_: Dict , **UpperCamelCase_: List[str] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : str = ['sentencepiece']
def __init__( self: Tuple , *UpperCamelCase_: Union[str, Any] , **UpperCamelCase_: List[str] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Optional[Any] = ['sentencepiece']
def __init__( self: List[str] , *UpperCamelCase_: Optional[Any] , **UpperCamelCase_: str ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Tuple = ['sentencepiece']
def __init__( self: str , *UpperCamelCase_: Any , **UpperCamelCase_: str ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Tuple = ['sentencepiece']
def __init__( self: List[str] , *UpperCamelCase_: int , **UpperCamelCase_: Dict ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Tuple = ['sentencepiece']
def __init__( self: List[Any] , *UpperCamelCase_: List[Any] , **UpperCamelCase_: Any ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Any = ['sentencepiece']
def __init__( self: Dict , *UpperCamelCase_: Optional[int] , **UpperCamelCase_: Union[str, Any] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Any = ['sentencepiece']
def __init__( self: Optional[int] , *UpperCamelCase_: List[Any] , **UpperCamelCase_: Tuple ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : List[Any] = ['sentencepiece']
def __init__( self: str , *UpperCamelCase_: Optional[Any] , **UpperCamelCase_: Optional[int] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : int = ['sentencepiece']
def __init__( self: Union[str, Any] , *UpperCamelCase_: Optional[Any] , **UpperCamelCase_: Any ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : str = ['sentencepiece']
def __init__( self: Union[str, Any] , *UpperCamelCase_: List[str] , **UpperCamelCase_: str ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : int = ['sentencepiece']
def __init__( self: Optional[int] , *UpperCamelCase_: Tuple , **UpperCamelCase_: str ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : int = ['sentencepiece']
def __init__( self: Dict , *UpperCamelCase_: Optional[int] , **UpperCamelCase_: List[Any] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Tuple = ['sentencepiece']
def __init__( self: Dict , *UpperCamelCase_: int , **UpperCamelCase_: int ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : Optional[int] = ['sentencepiece']
def __init__( self: List[str] , *UpperCamelCase_: Union[str, Any] , **UpperCamelCase_: Union[str, Any] ):
requires_backends(self , ["""sentencepiece"""] )
class lowerCamelCase__( metaclass=__lowerCamelCase):
UpperCAmelCase__ : int = ['sentencepiece']
def __init__( self: Optional[Any] , *UpperCamelCase_: List[str] , **UpperCamelCase_: int ):
requires_backends(self , ["""sentencepiece"""] )
| 12
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A_ :
'''simple docstring'''
def __init__( self : Any , lowercase_ : List[Any] , lowercase_ : int=3 , lowercase_ : Dict=32 , lowercase_ : Optional[Any]=3 , lowercase_ : Tuple=10 , lowercase_ : Optional[Any]=[10, 20, 30, 40] , lowercase_ : List[str]=[1, 1, 2, 1] , lowercase_ : Optional[int]=True , lowercase_ : str=True , lowercase_ : Dict="relu" , lowercase_ : Optional[Any]=3 , lowercase_ : List[str]=None , ) -> int:
UpperCAmelCase : Dict = parent
UpperCAmelCase : Dict = batch_size
UpperCAmelCase : Any = image_size
UpperCAmelCase : Any = num_channels
UpperCAmelCase : List[str] = embeddings_size
UpperCAmelCase : str = hidden_sizes
UpperCAmelCase : str = depths
UpperCAmelCase : Optional[int] = is_training
UpperCAmelCase : int = use_labels
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : List[Any] = num_labels
UpperCAmelCase : Union[str, Any] = scope
UpperCAmelCase : Any = len(lowercase_ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase : Any = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
return ResNetConfig(
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 UpperCAmelCase_ ( self : int , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = TFResNetModel(config=lowercase_ )
UpperCAmelCase : int = model(lowercase_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def UpperCAmelCase_ ( self : Any , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : Optional[int] ) -> List[Any]:
UpperCAmelCase : List[Any] = self.num_labels
UpperCAmelCase : Union[str, Any] = TFResNetForImageClassification(lowercase_ )
UpperCAmelCase : Any = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = config_and_inputs
UpperCAmelCase : str = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class A_ ( _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
UpperCAmelCase_ : Dict = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
UpperCAmelCase_ : Tuple = False
UpperCAmelCase_ : Tuple = False
UpperCAmelCase_ : List[Any] = False
UpperCAmelCase_ : str = False
UpperCAmelCase_ : Optional[int] = False
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
UpperCAmelCase : Optional[int] = TFResNetModelTester(self )
UpperCAmelCase : Dict = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ )
def UpperCAmelCase_ ( self : str ) -> Any:
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 UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
return
@unittest.skip(reason='ResNet does not use inputs_embeds' )
def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]:
pass
@unittest.skip(reason='ResNet does not support input and output embeddings' )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
pass
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
UpperCAmelCase , UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase : List[Any] = model_class(lowercase_ )
UpperCAmelCase : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase : str = [*signature.parameters.keys()]
UpperCAmelCase : Dict = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase_ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
def check_hidden_states_output(lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
UpperCAmelCase : Union[str, Any] = model_class(lowercase_ )
UpperCAmelCase : Optional[Any] = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
UpperCAmelCase : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase : int = self.model_tester.num_stages
self.assertEqual(len(lowercase_ ) , expected_num_stages + 1 )
# ResNet'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] , )
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Tuple = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
UpperCAmelCase : List[Any] = layer_type
UpperCAmelCase : int = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase : List[Any] = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : Dict ) -> Tuple:
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : str = TFResNetModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def UpperCamelCase( ):
UpperCAmelCase : Optional[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class A_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Any:
UpperCAmelCase : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCAmelCase : Any = self.default_image_processor
UpperCAmelCase : List[Any] = prepare_img()
UpperCAmelCase : Dict = image_processor(images=lowercase_ , return_tensors='tf' )
# forward pass
UpperCAmelCase : List[Any] = model(**lowercase_ )
# verify the logits
UpperCAmelCase : Optional[Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowercase_ )
UpperCAmelCase : int = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowercase_ , atol=1E-4 ) )
| 151
| 0
|
"""simple docstring"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : List[Any] = {"vocab_file": "spiece.model"}
lowerCAmelCase : List[str] = {
"vocab_file": {
"TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
}
}
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , _a , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , _a = None , **_a , ):
"""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__(
do_lower_case=lowerCamelCase_ , remove_space=lowerCamelCase_ , keep_accents=lowerCamelCase_ , bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , additional_special_tokens=lowerCamelCase_ , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase_ , )
lowerCamelCase = 3
lowerCamelCase = do_lower_case
lowerCamelCase = remove_space
lowerCamelCase = keep_accents
lowerCamelCase = vocab_file
lowerCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCamelCase_ )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
"""You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
"""See https://pypi.org/project/jieba/ for installation.""" )
lowerCamelCase = jieba
lowerCamelCase = str.maketrans(""" \n""" , """\u2582\u2583""" )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def _lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.sp_model )
def _lowerCAmelCase ( 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 __getstate__( self ):
"""simple docstring"""
lowerCamelCase = self.__dict__.copy()
lowerCamelCase = None
return state
def __setstate__( self , _a ):
"""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.vocab_file )
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if self.remove_space:
lowerCamelCase = """ """.join(inputs.strip().split() )
else:
lowerCamelCase = inputs
lowerCamelCase = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
lowerCamelCase = unicodedata.normalize("""NFKD""" , lowerCamelCase_ )
lowerCamelCase = """""".join([c for c in outputs if not unicodedata.combining(lowerCamelCase_ )] )
if self.do_lower_case:
lowerCamelCase = outputs.lower()
return outputs
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
lowerCamelCase = self.preprocess_text(lowerCamelCase_ )
lowerCamelCase = self.sp_model.encode(lowerCamelCase_ , out_type=lowerCamelCase_ )
lowerCamelCase = []
for piece in pieces:
if len(lowerCamelCase_ ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
lowerCamelCase = self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase_ , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
lowerCamelCase = cur_pieces[1:]
else:
lowerCamelCase = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(lowerCamelCase_ )
else:
new_pieces.append(lowerCamelCase_ )
return new_pieces
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
return self.sp_model.PieceToId(lowerCamelCase_ )
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
return self.sp_model.IdToPiece(lowerCamelCase_ )
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
lowerCamelCase = """""".join(lowerCamelCase_ ).replace(lowerCamelCase_ , """ """ ).strip()
return out_string
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
lowerCamelCase = [self.sep_token_id]
lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _lowerCAmelCase ( self , _a , _a = None , _a = 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 not None:
return ([0] * len(lowerCamelCase_ )) + [1] + ([0] * len(lowerCamelCase_ )) + [1, 1]
return ([0] * len(lowerCamelCase_ )) + [1, 1]
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
lowerCamelCase = [self.sep_token_id]
lowerCamelCase = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _lowerCAmelCase ( self , _a , _a = 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,)
def _lowerCAmelCase ( self , *_a , **_a ):
"""simple docstring"""
lowerCamelCase = super()._decode(*lowerCamelCase_ , **lowerCamelCase_ )
lowerCamelCase = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" )
return text
| 355
|
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class __magic_name__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = 0
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(_a , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase = Path(_a ) / """preprocessor_config.json"""
lowerCamelCase = Path(_a ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(_a , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(_a , """w""" ) )
lowerCamelCase = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase = Path(_a ) / """preprocessor_config.json"""
lowerCamelCase = Path(_a ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(_a , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(_a , """w""" ) )
lowerCamelCase = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase = CLIPConfig()
# Create a dummy config file with image_proceesor_type
lowerCamelCase = Path(_a ) / """preprocessor_config.json"""
lowerCamelCase = Path(_a ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(_a , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(_a , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
lowerCamelCase = AutoImageProcessor.from_pretrained(_a ).to_dict()
config_dict.pop("""image_processor_type""" )
lowerCamelCase = CLIPImageProcessor(**_a )
# save in new folder
model_config.save_pretrained(_a )
config.save_pretrained(_a )
lowerCamelCase = AutoImageProcessor.from_pretrained(_a )
# make sure private variable is not incorrectly saved
lowerCamelCase = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(_a , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase = Path(_a ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(_a , """w""" ) , )
lowerCamelCase = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
_a , """clip-base is not a local folder and is not a valid model identifier""" ):
lowerCamelCase = AutoImageProcessor.from_pretrained("""clip-base""" )
def _lowerCAmelCase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
_a , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCamelCase = AutoImageProcessor.from_pretrained(_a , revision="""aaaaaa""" )
def _lowerCAmelCase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
_a , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCamelCase = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _lowerCAmelCase ( self ):
"""simple docstring"""
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_a ):
lowerCamelCase = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_a ):
lowerCamelCase = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=_a )
lowerCamelCase = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_a )
lowerCamelCase = AutoImageProcessor.from_pretrained(_a , trust_remote_code=_a )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def _lowerCAmelCase ( self ):
"""simple docstring"""
try:
AutoConfig.register("""custom""" , _a )
AutoImageProcessor.register(_a , _a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_a ):
AutoImageProcessor.register(_a , _a )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase = Path(_a ) / """preprocessor_config.json"""
lowerCamelCase = Path(_a ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(_a , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(_a , """w""" ) )
lowerCamelCase = CustomImageProcessor.from_pretrained(_a )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_a )
lowerCamelCase = AutoImageProcessor.from_pretrained(_a )
self.assertIsInstance(_a , _a )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _lowerCAmelCase ( self ):
"""simple docstring"""
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = True
try:
AutoConfig.register("""custom""" , _a )
AutoImageProcessor.register(_a , _a )
# If remote code is not set, the default is to use local
lowerCamelCase = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
lowerCamelCase = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
lowerCamelCase = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=_a )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(_a , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 168
| 0
|
import inspect
import unittest
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __magic_name__ ( self : Optional[int] ):
"""simple docstring"""
try:
import diffusers # noqa: F401
except ImportError:
assert False
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
import diffusers
from diffusers.dependency_versions_table import deps
_A: Optional[Any] = inspect.getmembers(lowerCAmelCase_ , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
_A: Any = '''k-diffusion'''
elif backend == "invisible_watermark":
_A: Any = '''invisible-watermark'''
assert backend in deps, F"""{backend} is not in the deps table!"""
| 121
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ : List[str] = logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : int = '''xlm'''
__UpperCamelCase : Optional[Any] = {
'''hidden_size''': '''emb_dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
'''n_words''': '''vocab_size''', # For backward compatibility
}
def __init__( self : List[Any] , lowerCAmelCase_ : Dict=3_0_1_4_5 , lowerCAmelCase_ : Union[str, Any]=2_0_4_8 , lowerCAmelCase_ : List[str]=1_2 , lowerCAmelCase_ : Optional[Any]=1_6 , lowerCAmelCase_ : Union[str, Any]=0.1 , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : List[str]=False , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : List[str]=False , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : Tuple=5_1_2 , lowerCAmelCase_ : Tuple=2_0_4_8**-0.5 , lowerCAmelCase_ : List[str]=1e-12 , lowerCAmelCase_ : Any=0.02 , lowerCAmelCase_ : int=0 , lowerCAmelCase_ : int=1 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : List[Any]=3 , lowerCAmelCase_ : int=5 , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : Union[str, Any]="first" , lowerCAmelCase_ : int=True , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : Any=True , lowerCAmelCase_ : int=0.1 , lowerCAmelCase_ : Tuple=5 , lowerCAmelCase_ : Tuple=5 , lowerCAmelCase_ : Optional[int]=0 , lowerCAmelCase_ : Optional[Any]=0 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : Dict=0 , **lowerCAmelCase_ : List[Any] , ):
"""simple docstring"""
_A: Dict = vocab_size
_A: Dict = emb_dim
_A: Optional[Any] = n_layers
_A: str = n_heads
_A: Optional[int] = dropout
_A: Union[str, Any] = attention_dropout
_A: Any = gelu_activation
_A: Optional[Any] = sinusoidal_embeddings
_A: Any = causal
_A: Optional[int] = asm
_A: List[str] = n_langs
_A: int = use_lang_emb
_A: Any = layer_norm_eps
_A: Tuple = bos_index
_A: Any = eos_index
_A: List[str] = pad_index
_A: str = unk_index
_A: str = mask_index
_A: List[Any] = is_encoder
_A: List[Any] = max_position_embeddings
_A: str = embed_init_std
_A: int = init_std
_A: List[str] = summary_type
_A: List[str] = summary_use_proj
_A: Tuple = summary_activation
_A: Dict = summary_proj_to_labels
_A: Tuple = summary_first_dropout
_A: str = start_n_top
_A: str = end_n_top
_A: Optional[Any] = mask_token_id
_A: Any = lang_id
if "n_words" in kwargs:
_A: Union[str, Any] = kwargs['''n_words''']
super().__init__(pad_token_id=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@property
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
if self.task == "multiple-choice":
_A: int = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_A: Tuple = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 121
| 1
|
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_UpperCAmelCase = 0
_UpperCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_UpperCAmelCase = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
_UpperCAmelCase = tuple[int, int]
class _UpperCamelCase :
def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Node | None , ) -> None:
"""simple docstring"""
UpperCamelCase_ = pos_x
UpperCamelCase_ = pos_y
UpperCamelCase_ = (pos_y, pos_x)
UpperCamelCase_ = goal_x
UpperCamelCase_ = goal_y
UpperCamelCase_ = g_cost
UpperCamelCase_ = parent
UpperCamelCase_ = self.calculate_heuristic()
UpperCamelCase_ = self.g_cost + self.h_cost
def lowercase ( self: List[Any] ) -> float:
"""simple docstring"""
UpperCamelCase_ = self.pos_x - self.goal_x
UpperCamelCase_ = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(_SCREAMING_SNAKE_CASE ) + abs(_SCREAMING_SNAKE_CASE )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self: List[Any] , _SCREAMING_SNAKE_CASE: Node ) -> bool:
"""simple docstring"""
return self.f_cost < other.f_cost
class _UpperCamelCase :
def __init__( self: Dict , _SCREAMING_SNAKE_CASE: TPosition , _SCREAMING_SNAKE_CASE: TPosition ) -> Any:
"""simple docstring"""
UpperCamelCase_ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 99999 , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = [self.start]
UpperCamelCase_ = []
UpperCamelCase_ = False
def lowercase ( self: Union[str, Any] ) -> list[TPosition]:
"""simple docstring"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
UpperCamelCase_ = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(_SCREAMING_SNAKE_CASE )
self.closed_nodes.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.get_successors(_SCREAMING_SNAKE_CASE )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(_SCREAMING_SNAKE_CASE )
else:
# retrieve the best current path
UpperCamelCase_ = self.open_nodes.pop(self.open_nodes.index(_SCREAMING_SNAKE_CASE ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(_SCREAMING_SNAKE_CASE )
else:
self.open_nodes.append(_SCREAMING_SNAKE_CASE )
return [self.start.pos]
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Node ) -> list[Node]:
"""simple docstring"""
UpperCamelCase_ = []
for action in delta:
UpperCamelCase_ = parent.pos_x + action[1]
UpperCamelCase_ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_SCREAMING_SNAKE_CASE ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , _SCREAMING_SNAKE_CASE , ) )
return successors
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Node | None ) -> list[TPosition]:
"""simple docstring"""
UpperCamelCase_ = node
UpperCamelCase_ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
UpperCamelCase_ = current_node.parent
path.reverse()
return path
class _UpperCamelCase :
def __init__( self: Optional[int] , _SCREAMING_SNAKE_CASE: TPosition , _SCREAMING_SNAKE_CASE: TPosition ) -> None:
"""simple docstring"""
UpperCamelCase_ = AStar(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = AStar(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = False
def lowercase ( self: int ) -> list[TPosition]:
"""simple docstring"""
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
UpperCamelCase_ = self.fwd_astar.open_nodes.pop(0 )
UpperCamelCase_ = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.fwd_astar.closed_nodes.append(_SCREAMING_SNAKE_CASE )
self.bwd_astar.closed_nodes.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = current_bwd_node
UpperCamelCase_ = current_fwd_node
UpperCamelCase_ = {
self.fwd_astar: self.fwd_astar.get_successors(_SCREAMING_SNAKE_CASE ),
self.bwd_astar: self.bwd_astar.get_successors(_SCREAMING_SNAKE_CASE ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(_SCREAMING_SNAKE_CASE )
else:
# retrieve the best current path
UpperCamelCase_ = astar.open_nodes.pop(
astar.open_nodes.index(_SCREAMING_SNAKE_CASE ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(_SCREAMING_SNAKE_CASE )
else:
astar.open_nodes.append(_SCREAMING_SNAKE_CASE )
return [self.fwd_astar.start.pos]
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: Node , _SCREAMING_SNAKE_CASE: Node ) -> list[TPosition]:
"""simple docstring"""
UpperCamelCase_ = self.fwd_astar.retrace_path(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.bwd_astar.retrace_path(_SCREAMING_SNAKE_CASE )
bwd_path.pop()
bwd_path.reverse()
UpperCamelCase_ = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
_UpperCAmelCase = (0, 0)
_UpperCAmelCase = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_UpperCAmelCase = time.time()
_UpperCAmelCase = AStar(init, goal)
_UpperCAmelCase = a_star.search()
_UpperCAmelCase = time.time() - start_time
print(f'''AStar execution time = {end_time:f} seconds''')
_UpperCAmelCase = time.time()
_UpperCAmelCase = BidirectionalAStar(init, goal)
_UpperCAmelCase = time.time() - bd_start_time
print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')
| 352
|
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
_UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCamelCase : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
_UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class _UpperCamelCase :
_UpperCamelCase : str = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
_UpperCamelCase : Optional[str] = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
_UpperCamelCase : Optional[int] = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_4_2 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
_UpperCamelCase : Optional[int] = field(
default=1_4_2 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
_UpperCamelCase : Optional[int] = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
_UpperCamelCase : Optional[str] = field(default=lowerCAmelCase_ , metadata={'''help''': '''Source language id for translation.'''} )
_UpperCamelCase : Optional[str] = field(default=lowerCAmelCase_ , metadata={'''help''': '''Target language id for translation.'''} )
_UpperCamelCase : Optional[int] = field(default=lowerCAmelCase_ , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
_UpperCamelCase : bool = field(
default=lowerCAmelCase_ , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
logger.info(F'''***** {split} metrics *****''' )
for key in sorted(metrics.keys() ):
logger.info(F''' {key} = {metrics[key]}''' )
save_json(UpperCamelCase_ , os.path.join(UpperCamelCase_ , F'''{split}_results.json''' ) )
def lowerCAmelCase_ ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
check_output_dir(UpperCamelCase_ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , UpperCamelCase_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
assert hasattr(UpperCamelCase_ , UpperCamelCase_ ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
setattr(UpperCamelCase_ , UpperCamelCase_ , getattr(UpperCamelCase_ , UpperCamelCase_ ) )
UpperCamelCase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCamelCase_ = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=UpperCamelCase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(UpperCamelCase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
UpperCamelCase_ = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(UpperCamelCase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase_ = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
UpperCamelCase_ = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(UpperCamelCase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
UpperCamelCase_ = SeqaSeqDataset
# Get datasets
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
UpperCamelCase_ = (
dataset_class(
UpperCamelCase_ , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
UpperCamelCase_ = (
build_compute_metrics_fn(data_args.task , UpperCamelCase_ ) if training_args.predict_with_generate else None
)
UpperCamelCase_ = SeqaSeqTrainer(
model=UpperCamelCase_ , args=UpperCamelCase_ , data_args=UpperCamelCase_ , train_dataset=UpperCamelCase_ , eval_dataset=UpperCamelCase_ , data_collator=SeqaSeqDataCollator(
UpperCamelCase_ , UpperCamelCase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=UpperCamelCase_ , tokenizer=UpperCamelCase_ , )
UpperCamelCase_ = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
UpperCamelCase_ = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
UpperCamelCase_ = train_result.metrics
UpperCamelCase_ = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
UpperCamelCase_ = trainer.evaluate(metric_key_prefix="val" )
UpperCamelCase_ = data_args.n_val
UpperCamelCase_ = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
if training_args.do_predict:
logger.info("*** Predict ***" )
UpperCamelCase_ = trainer.predict(test_dataset=UpperCamelCase_ , metric_key_prefix="test" )
UpperCamelCase_ = test_output.metrics
UpperCamelCase_ = data_args.n_test
if trainer.is_world_process_zero():
UpperCamelCase_ = round(metrics["test_loss"] , 4 )
handle_metrics("test" , UpperCamelCase_ , training_args.output_dir )
all_metrics.update(UpperCamelCase_ )
if training_args.predict_with_generate:
UpperCamelCase_ = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_ )
UpperCamelCase_ = lmap(str.strip , UpperCamelCase_ )
write_txt_file(UpperCamelCase_ , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(UpperCamelCase_ , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 328
| 0
|
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase_ = False
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Dict=32 ) -> Union[str, Any]:
"""simple docstring"""
set_seed(0 )
lowercase__ : Union[str, Any] = UNetaDModel(sample_size=_snake_case ,in_channels=3 ,out_channels=3 )
lowercase__ : Optional[int] = torch.optim.SGD(model.parameters() ,lr=0.0001 )
return model, optimizer
@slow
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Tuple = 'cpu' # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
lowercase__ : Any = DDPMScheduler(
num_train_timesteps=1_000 ,beta_start=0.0001 ,beta_end=0.02 ,beta_schedule='''linear''' ,clip_sample=_snake_case ,)
lowercase__ : Union[str, Any] = DDIMScheduler(
num_train_timesteps=1_000 ,beta_start=0.0001 ,beta_end=0.02 ,beta_schedule='''linear''' ,clip_sample=_snake_case ,)
assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
# shared batches for DDPM and DDIM
set_seed(0 )
lowercase__ : Optional[int] = [torch.randn((4, 3, 32, 32) ).clip(-1 ,1 ).to(_snake_case ) for _ in range(4 )]
lowercase__ : Tuple = [torch.randn((4, 3, 32, 32) ).to(_snake_case ) for _ in range(4 )]
lowercase__ : Optional[int] = [torch.randint(0 ,1_000 ,(4,) ).long().to(_snake_case ) for _ in range(4 )]
# train with a DDPM scheduler
lowercase__ : Optional[int] = self.get_model_optimizer(resolution=32 )
model.train().to(_snake_case )
for i in range(4 ):
optimizer.zero_grad()
lowercase__ : Optional[Any] = ddpm_scheduler.add_noise(clean_images[i] ,noise[i] ,timesteps[i] )
lowercase__ : List[Any] = model(_snake_case ,timesteps[i] ).sample
lowercase__ : str = torch.nn.functional.mse_loss(_snake_case ,noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
# recreate the model and optimizer, and retry with DDIM
lowercase__ : Any = self.get_model_optimizer(resolution=32 )
model.train().to(_snake_case )
for i in range(4 ):
optimizer.zero_grad()
lowercase__ : Dict = ddim_scheduler.add_noise(clean_images[i] ,noise[i] ,timesteps[i] )
lowercase__ : Optional[Any] = model(_snake_case ,timesteps[i] ).sample
lowercase__ : Optional[int] = torch.nn.functional.mse_loss(_snake_case ,noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
self.assertTrue(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
self.assertTrue(torch.allclose(_snake_case ,_snake_case ,atol=1e-5 ) )
| 16
|
import math
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
lowercase__ : Optional[Any] = []
lowercase__ : str = 2
lowercase__ : Optional[Any] = int(math.sqrt(SCREAMING_SNAKE_CASE_ ) ) # Size of every segment
lowercase__ : Dict = [True] * (end + 1)
lowercase__ : Union[str, Any] = []
while start <= end:
if temp[start] is True:
in_prime.append(SCREAMING_SNAKE_CASE_ )
for i in range(start * start , end + 1 , SCREAMING_SNAKE_CASE_ ):
lowercase__ : int = False
start += 1
prime += in_prime
lowercase__ : Optional[int] = end + 1
lowercase__ : List[str] = min(2 * end , SCREAMING_SNAKE_CASE_ )
while low <= n:
lowercase__ : str = [True] * (high - low + 1)
for each in in_prime:
lowercase__ : str = math.floor(low / each ) * each
if t < low:
t += each
for j in range(SCREAMING_SNAKE_CASE_ , high + 1 , SCREAMING_SNAKE_CASE_ ):
lowercase__ : Optional[Any] = False
for j in range(len(SCREAMING_SNAKE_CASE_ ) ):
if temp[j] is True:
prime.append(j + low )
lowercase__ : Optional[Any] = high + 1
lowercase__ : Optional[int] = min(high + end , SCREAMING_SNAKE_CASE_ )
return prime
print(sieve(10**6))
| 214
| 0
|
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowerCAmelCase__ ( _a : List[str] , _a : str , _a : Dict=10_24 , _a : str=10_24 , _a : int=False , **_a : int ):
snake_case_ : int = AutoTokenizer.from_pretrained(_a )
snake_case_ : str = SeqaSeqDataset(_a , _a , _a , _a , type_path="train" , **_a )
snake_case_ : Any = tok.pad_token_id
def get_lens(_a : List[str] ):
snake_case_ : Optional[Any] = tqdm(
DataLoader(_a , batch_size=5_12 , num_workers=8 , shuffle=_a , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
snake_case_ : List[Any] = []
for batch in dl:
snake_case_ : List[str] = batch["input_ids"].ne(_a ).sum(1 ).tolist()
snake_case_ : Union[str, Any] = batch["labels"].ne(_a ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(_a , _a ):
max_lens.append(max(_a , _a ) )
else:
max_lens.extend(_a )
return max_lens
snake_case_ : Dict = get_lens(_a )
snake_case_ : List[Any] = SeqaSeqDataset(_a , _a , _a , _a , type_path="val" , **_a )
snake_case_ : str = get_lens(_a )
pickle_save(_a , train_ds.len_file )
pickle_save(_a , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 36
|
def lowerCAmelCase__ ( _a : int = 50 ):
snake_case_ : Union[str, Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 36
| 1
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _snake_case ( snake_case__ : Dict , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : List[str] , ):
A , A = coefficient_matrix.shape
A , A = constant_matrix.shape
if rowsa != colsa:
A = F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
raise ValueError(snake_case__ )
if colsa != 1:
A = F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
raise ValueError(snake_case__ )
if rowsa != rowsa:
A = (
'Coefficient and constant matrices dimensions must be nxn and nx1 but '
F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
)
raise ValueError(snake_case__ )
if len(snake_case__ ) != rowsa:
A = (
'Number of initial values must be equal to number of rows in coefficient '
F'matrix but received {len(snake_case__ )} and {rowsa}'
)
raise ValueError(snake_case__ )
if iterations <= 0:
raise ValueError('Iterations must be at least 1' )
A = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
A , A = table.shape
strictly_diagonally_dominant(snake_case__ )
# Iterates the whole matrix for given number of times
for _ in range(snake_case__ ):
A = []
for row in range(snake_case__ ):
A = 0
for col in range(snake_case__ ):
if col == row:
A = table[row][col]
elif col == cols - 1:
A = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
A = (temp + val) / denom
new_val.append(snake_case__ )
A = new_val
return [float(snake_case__ ) for i in new_val]
def _snake_case ( snake_case__ : List[str] ):
A , A = table.shape
A = True
for i in range(0 , snake_case__ ):
A = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('Coefficient matrix is not strictly diagonally dominant' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 74
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ : Tuple = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : List[Any] = [
'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMAEForPreTraining',
'ViTMAELayer',
'ViTMAEModel',
'ViTMAEPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : int = [
'TFViTMAEForPreTraining',
'TFViTMAEModel',
'TFViTMAEPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowerCAmelCase__ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 98
| 0
|
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
UpperCAmelCase__ = "scheduler_config.json"
class __lowerCAmelCase ( __lowerCAmelCase ):
UpperCamelCase = 1
UpperCamelCase = 2
UpperCamelCase = 3
UpperCamelCase = 4
UpperCamelCase = 5
@dataclass
class __lowerCAmelCase ( __lowerCAmelCase ):
UpperCamelCase = 42
class __lowerCAmelCase :
UpperCamelCase = SCHEDULER_CONFIG_NAME
UpperCamelCase = ["dtype"]
UpperCamelCase = []
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : int , A : Dict[str, Any] = None , A : Optional[str] = None , A : int=False , **A : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = cls.load_config(
pretrained_model_name_or_path=lowerCamelCase__ , subfolder=lowerCamelCase__ , return_unused_kwargs=lowerCamelCase__ , **lowerCamelCase__ , )
_UpperCAmelCase = cls.from_config(lowerCamelCase__ , return_unused_kwargs=lowerCamelCase__ , **lowerCamelCase__)
if hasattr(lowerCamelCase__ , 'create_state') and getattr(lowerCamelCase__ , 'has_state' , lowerCamelCase__):
_UpperCAmelCase = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def _lowerCamelCase ( self : Optional[Any] , A : Union[str, os.PathLike] , A : bool = False , **A : Optional[Any]) -> str:
"""simple docstring"""
self.save_config(save_directory=lowerCamelCase__ , push_to_hub=lowerCamelCase__ , **lowerCamelCase__)
@property
def _lowerCamelCase ( self : List[str]) -> Tuple:
"""simple docstring"""
return self._get_compatibles()
@classmethod
def _lowerCamelCase ( cls : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = list(set([cls.__name__] + cls._compatibles))
_UpperCAmelCase = importlib.import_module(__name__.split('.')[0])
_UpperCAmelCase = [
getattr(lowerCamelCase__ , lowerCamelCase__) for c in compatible_classes_str if hasattr(lowerCamelCase__ , lowerCamelCase__)
]
return compatible_classes
def A ( _UpperCAmelCase : jnp.ndarray , _UpperCAmelCase : Tuple[int] ) -> Optional[int]:
'''simple docstring'''
assert len(_UpperCAmelCase ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCAmelCase ) - x.ndim) ) , _UpperCAmelCase )
def A ( _UpperCAmelCase : int , _UpperCAmelCase : Dict=0.999 , _UpperCAmelCase : Tuple=jnp.floataa ) -> Tuple:
'''simple docstring'''
def alpha_bar(_UpperCAmelCase : Tuple ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
_UpperCAmelCase = []
for i in range(_UpperCAmelCase ):
_UpperCAmelCase = i / num_diffusion_timesteps
_UpperCAmelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(_UpperCAmelCase ) / alpha_bar(_UpperCAmelCase ) , _UpperCAmelCase ) )
return jnp.array(_UpperCAmelCase , dtype=_UpperCAmelCase )
@flax.struct.dataclass
class __lowerCAmelCase :
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
@classmethod
def _lowerCamelCase ( cls : List[Any] , A : Union[str, Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = scheduler.config
if config.trained_betas is not None:
_UpperCAmelCase = jnp.asarray(config.trained_betas , dtype=scheduler.dtype)
elif config.beta_schedule == "linear":
_UpperCAmelCase = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype)
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_UpperCAmelCase = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype)
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_UpperCAmelCase = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype)
else:
raise NotImplementedError(
F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}")
_UpperCAmelCase = 1.0 - betas
_UpperCAmelCase = jnp.cumprod(lowerCamelCase__ , axis=0)
return cls(
alphas=lowerCamelCase__ , betas=lowerCamelCase__ , alphas_cumprod=lowerCamelCase__ , )
def A ( _UpperCAmelCase : CommonSchedulerState , _UpperCAmelCase : jnp.ndarray , _UpperCAmelCase : jnp.ndarray , _UpperCAmelCase : jnp.ndarray ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = state.alphas_cumprod
_UpperCAmelCase = alphas_cumprod[timesteps] ** 0.5
_UpperCAmelCase = sqrt_alpha_prod.flatten()
_UpperCAmelCase = broadcast_to_shape_from_left(_UpperCAmelCase , original_samples.shape )
_UpperCAmelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
_UpperCAmelCase = sqrt_one_minus_alpha_prod.flatten()
_UpperCAmelCase = broadcast_to_shape_from_left(_UpperCAmelCase , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def A ( _UpperCAmelCase : CommonSchedulerState , _UpperCAmelCase : jnp.ndarray , _UpperCAmelCase : jnp.ndarray , _UpperCAmelCase : jnp.ndarray ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = get_sqrt_alpha_prod(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def A ( _UpperCAmelCase : CommonSchedulerState , _UpperCAmelCase : jnp.ndarray , _UpperCAmelCase : jnp.ndarray , _UpperCAmelCase : jnp.ndarray ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = get_sqrt_alpha_prod(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 357
|
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 __lowerCAmelCase ( A ):
def _lowerCamelCase ( self : List[str]) -> int:
"""simple docstring"""
_UpperCAmelCase = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(A , 'hidden_sizes'))
self.parent.assertTrue(hasattr(A , 'neck_hidden_sizes'))
self.parent.assertTrue(hasattr(A , 'num_attention_heads'))
class __lowerCAmelCase :
def __init__( self : int , A : Tuple , A : List[str]=13 , A : List[str]=32 , A : List[str]=2 , A : List[str]=3 , A : List[Any]=6_40 , A : Any=4 , A : int="silu" , A : int=3 , A : Dict=32 , A : List[Any]=0.1 , A : Optional[Any]=0.1 , A : Optional[int]=0.1 , A : List[str]=0.0_2 , A : int=True , A : Any=True , A : List[str]=10 , A : Tuple=None , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = last_hidden_size
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = conv_kernel_size
_UpperCAmelCase = output_stride
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = classifier_dropout_prob
_UpperCAmelCase = use_labels
_UpperCAmelCase = is_training
_UpperCAmelCase = num_labels
_UpperCAmelCase = initializer_range
_UpperCAmelCase = scope
def _lowerCamelCase ( self : Union[str, Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels)
_UpperCAmelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels)
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCamelCase ( self : str) -> int:
"""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 _lowerCamelCase ( self : List[Any] , A : Dict , A : Tuple , A : int , A : Tuple) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = MobileViTModel(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A)
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 _lowerCamelCase ( self : int , A : Any , A : List[Any] , A : List[Any] , A : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = MobileViTForImageClassification(A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A , labels=A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _lowerCamelCase ( self : int , A : Tuple , A : Optional[Any] , A : Union[str, Any] , A : List[Any]) -> int:
"""simple docstring"""
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = MobileViTForSemanticSegmentation(A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A)
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
_UpperCAmelCase = model(A , labels=A)
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 _lowerCamelCase ( self : int) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( A , A , unittest.TestCase ):
UpperCamelCase = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
'''feature-extraction''': MobileViTModel,
'''image-classification''': MobileViTForImageClassification,
'''image-segmentation''': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def _lowerCamelCase ( self : str) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = MobileViTModelTester(self)
_UpperCAmelCase = MobileViTConfigTester(self , config_class=A , has_text_modality=A)
def _lowerCamelCase ( self : Optional[int]) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds')
def _lowerCamelCase ( self : Tuple) -> Dict:
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings')
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not output attentions')
def _lowerCamelCase ( self : Any) -> Optional[Any]:
"""simple docstring"""
pass
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(A)
_UpperCAmelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , A)
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
def _lowerCamelCase ( self : Union[str, Any]) -> List[str]:
"""simple docstring"""
pass
def _lowerCamelCase ( self : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
def check_hidden_states_output(A : List[str] , A : Union[str, Any] , A : int):
_UpperCAmelCase = model_class(A)
model.to(A)
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(A , A))
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = 5
self.assertEqual(len(A) , A)
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
_UpperCAmelCase = 2
for i in range(len(A)):
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)
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(A , A , A)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(A , A , A)
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A)
def _lowerCamelCase ( self : int) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A)
@slow
def _lowerCamelCase ( self : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = MobileViTModel.from_pretrained(A)
self.assertIsNotNone(A)
def A ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def _lowerCamelCase ( self : Tuple) -> Dict:
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small') if is_vision_available() else None
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small').to(A)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=A , return_tensors='pt').to(A)
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**A)
# verify the logits
_UpperCAmelCase = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , A)
_UpperCAmelCase = torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]).to(A)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A , atol=1E-4))
@slow
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
_UpperCAmelCase = model.to(A)
_UpperCAmelCase = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=A , return_tensors='pt').to(A)
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**A)
_UpperCAmelCase = outputs.logits
# verify the logits
_UpperCAmelCase = torch.Size((1, 21, 32, 32))
self.assertEqual(logits.shape , A)
_UpperCAmelCase = 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=A , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , A , atol=1E-4))
@slow
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
_UpperCAmelCase = model.to(A)
_UpperCAmelCase = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=A , return_tensors='pt').to(A)
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**A)
_UpperCAmelCase = outputs.logits.detach().cpu()
_UpperCAmelCase = image_processor.post_process_semantic_segmentation(outputs=A , target_sizes=[(50, 60)])
_UpperCAmelCase = torch.Size((50, 60))
self.assertEqual(segmentation[0].shape , A)
_UpperCAmelCase = image_processor.post_process_semantic_segmentation(outputs=A)
_UpperCAmelCase = torch.Size((32, 32))
self.assertEqual(segmentation[0].shape , A)
| 290
| 0
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class snake_case :
"""simple docstring"""
_lowerCamelCase = MBartConfig
_lowerCamelCase = {}
_lowerCamelCase = "gelu"
def __init__( self , UpperCamelCase , UpperCamelCase=13 , UpperCamelCase=7 , UpperCamelCase=True , UpperCamelCase=False , UpperCamelCase=99 , UpperCamelCase=32 , UpperCamelCase=2 , UpperCamelCase=4 , UpperCamelCase=37 , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=20 , UpperCamelCase=2 , UpperCamelCase=1 , UpperCamelCase=0 , ):
"""simple docstring"""
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = eos_token_id
lowerCamelCase_ = pad_token_id
lowerCamelCase_ = bos_token_id
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowerCamelCase_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowerCamelCase_ = tf.concat([input_ids, eos_tensor] , axis=1 )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , 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 , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowerCamelCase_ = prepare_mbart_inputs_dict(UpperCamelCase , UpperCamelCase , UpperCamelCase )
return config, inputs_dict
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = TFMBartModel(config=UpperCamelCase ).get_decoder()
lowerCamelCase_ = inputs_dict["input_ids"]
lowerCamelCase_ = input_ids[:1, :]
lowerCamelCase_ = inputs_dict["attention_mask"][:1, :]
lowerCamelCase_ = inputs_dict["head_mask"]
lowerCamelCase_ = 1
# first forward pass
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase , head_mask=UpperCamelCase , use_cache=UpperCamelCase )
lowerCamelCase_ ,lowerCamelCase_ = outputs.to_tuple()
lowerCamelCase_ = past_key_values[1]
def __snake_case ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : List[Any]=None , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Any=None , ):
if attention_mask is None:
lowerCamelCase_ = tf.cast(tf.math.not_equal(UpperCAmelCase_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowerCamelCase_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
lowerCamelCase_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCamelCase_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowerCamelCase_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class snake_case ( lowercase , lowercase , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
_lowerCamelCase = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
_lowerCamelCase = (
{
"conversational": TFMBartForConditionalGeneration,
"feature-extraction": TFMBartModel,
"summarization": TFMBartForConditionalGeneration,
"text2text-generation": TFMBartForConditionalGeneration,
"translation": TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowerCamelCase = True
_lowerCamelCase = False
_lowerCamelCase = False
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFMBartModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase )
@require_sentencepiece
@require_tokenizers
@require_tf
class snake_case ( unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = [
" UN Chief Says There Is No Military Solution in Syria",
]
_lowerCamelCase = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
]
_lowerCamelCase = "facebook/mbart-large-en-ro"
@cached_property
def snake_case ( self ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def snake_case ( self , **UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = self.translate_src_text(**UpperCamelCase )
self.assertListEqual(self.expected_text , UpperCamelCase )
def snake_case ( self , **UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = self.tokenizer(self.src_text , **UpperCamelCase , return_tensors="tf" )
lowerCamelCase_ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
lowerCamelCase_ = self.tokenizer.batch_decode(UpperCamelCase , skip_special_tokens=UpperCamelCase )
return generated_words
@slow
def snake_case ( self ):
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 55
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] ={
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Union[str, Any] =[
'''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BigBirdPegasusForCausalLM''',
'''BigBirdPegasusForConditionalGeneration''',
'''BigBirdPegasusForQuestionAnswering''',
'''BigBirdPegasusForSequenceClassification''',
'''BigBirdPegasusModel''',
'''BigBirdPegasusPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
A__ : Optional[Any] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 70
| 0
|
"""simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
A__ = FunnelConfig.from_json_file(_a )
print(F'''Building PyTorch model from configuration: {config}''' )
A__ = FunnelBaseModel(_a ) if base_model else FunnelModel(_a )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(_a , _a , _a )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , _a )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_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 model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
__lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 361
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
"TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TimesformerModel",
"TimesformerForVideoClassification",
"TimesformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
_lowerCamelCase : Tuple = random.Random()
def __lowerCamelCase ( A__ , A__=1.0 , A__=None , A__=None ) -> Union[str, Any]:
"""simple docstring"""
if rng is None:
UpperCamelCase = global_rng
UpperCamelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any]=7 , UpperCamelCase__ : List[str]=4_0_0 , UpperCamelCase__ : str=2_0_0_0 , UpperCamelCase__ : Any=1 , UpperCamelCase__ : Optional[Any]=0.0 , UpperCamelCase__ : Tuple=1_6_0_0_0 , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : Dict=True , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = min_seq_length
UpperCamelCase = max_seq_length
UpperCamelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase = feature_size
UpperCamelCase = padding_value
UpperCamelCase = sampling_rate
UpperCamelCase = return_attention_mask
UpperCamelCase = do_normalize
def A ( self : Optional[int] ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def A ( self : Union[str, Any] , UpperCamelCase__ : Optional[int]=False , UpperCamelCase__ : Union[str, Any]=False ):
"""simple docstring"""
def _flatten(UpperCamelCase__ : Optional[Any] ):
return list(itertools.chain(*UpperCamelCase__ ) )
if equal_length:
UpperCamelCase = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
UpperCamelCase = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase = [np.asarray(UpperCamelCase__ ) for x in speech_inputs]
return speech_inputs
class SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor
def A ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = WavaVecaFeatureExtractionTester(self )
def A ( self : Optional[Any] , UpperCamelCase__ : List[str] ):
"""simple docstring"""
self.assertTrue(np.all(np.mean(UpperCamelCase__ , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__ , axis=0 ) - 1 ) < 1E-3 ) )
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = [np.asarray(UpperCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
UpperCamelCase = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
UpperCamelCase = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 ) )
# Test batched
UpperCamelCase = feat_extract(UpperCamelCase__ , return_tensors='np' ).input_values
UpperCamelCase = feat_extract(UpperCamelCase__ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
UpperCamelCase = np.asarray(UpperCamelCase__ )
UpperCamelCase = feat_extract(UpperCamelCase__ , return_tensors='np' ).input_values
UpperCamelCase = feat_extract(UpperCamelCase__ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 ) )
def A ( self : Dict ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = ['longest', 'max_length', 'do_not_pad']
UpperCamelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = feat_extract(UpperCamelCase__ , padding=UpperCamelCase__ , max_length=UpperCamelCase__ , return_tensors='np' )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def A ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = range(8_0_0 , 1_4_0_0 , 2_0_0 )
UpperCamelCase = [floats_list((1, x) )[0] for x in lengths]
UpperCamelCase = ['longest', 'max_length', 'do_not_pad']
UpperCamelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = feat_extract(UpperCamelCase__ , max_length=UpperCamelCase__ , padding=UpperCamelCase__ )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def A ( self : str ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = feat_extract(
UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=1_0_0_0 , padding='max_length' , return_tensors='np' )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = feat_extract(
UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=1_0_0_0 , padding='longest' , return_tensors='np' )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1_0_0_0) )
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
UpperCamelCase = feat_extract(
UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=2_0_0_0 , padding='longest' , return_tensors='np' )
UpperCamelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1_2_0_0) )
@require_torch
def A ( self : Optional[Any] ):
"""simple docstring"""
import torch
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase = np.random.rand(1_0_0 ).astype(np.floataa )
UpperCamelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
UpperCamelCase = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
@slow
@require_torch
def A ( self : Any ):
"""simple docstring"""
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
UpperCamelCase = WavaVecaConfig.from_pretrained(UpperCamelCase__ )
UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__ )
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == 'layer' )
| 28
|
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[Any] ):
__a : Any = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
'`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
F"""{test_file} instead.""" )
__a : Tuple = components[-1]
if not test_fn.endswith('py' ):
raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead.""" )
if not test_fn.startswith('test_modeling_' ):
raise ValueError(
F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.""" )
__a : List[str] = components[:-1] + [test_fn.replace('.py' , '' )]
__a : Optional[Any] = '.'.join(_SCREAMING_SNAKE_CASE )
return test_module_path
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple ):
__a : List[str] = get_module_path(_SCREAMING_SNAKE_CASE )
__a : Dict = importlib.import_module(_SCREAMING_SNAKE_CASE )
return test_module
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple ):
__a : List[str] = []
__a : List[str] = get_test_module(_SCREAMING_SNAKE_CASE )
for attr in dir(_SCREAMING_SNAKE_CASE ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple ):
__a : Any = []
__a : str = get_test_module(_SCREAMING_SNAKE_CASE )
for attr in dir(_SCREAMING_SNAKE_CASE ):
__a : int = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
__a : Optional[Any] = getattr(_SCREAMING_SNAKE_CASE , 'all_model_classes' , [] )
if len(_SCREAMING_SNAKE_CASE ) > 0:
test_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : str = get_test_classes(_SCREAMING_SNAKE_CASE )
__a : Any = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : Tuple = test_class()
if hasattr(_SCREAMING_SNAKE_CASE , 'setUp' ):
test.setUp()
__a : List[Any] = None
if hasattr(_SCREAMING_SNAKE_CASE , 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
__a : List[str] = test.model_tester.__class__
return model_tester
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[Any] ):
__a : str = get_test_classes(_SCREAMING_SNAKE_CASE )
__a : int = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[str] ):
__a : List[Any] = get_test_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Any = []
for test_class in test_classes:
__a : Any = get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE )
if tester_class is not None:
tester_classes.append(_SCREAMING_SNAKE_CASE )
# sort with class names
return sorted(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : x.__name__ )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] ):
__a : str = get_test_classes(_SCREAMING_SNAKE_CASE )
__a : int = {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE ) for test_class in test_classes}
return test_tester_mapping
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
__a : Optional[Any] = get_model_classes(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = {
model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for model_class in model_classes
}
return model_test_mapping
def lowerCamelCase (_SCREAMING_SNAKE_CASE : List[str] ):
__a : Optional[Any] = get_model_classes(_SCREAMING_SNAKE_CASE )
__a : str = {
model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for model_class in model_classes
}
return model_to_tester_mapping
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return o
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return o.__name__
elif isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ):
return [to_json(_SCREAMING_SNAKE_CASE ) for x in o]
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return {to_json(_SCREAMING_SNAKE_CASE ): to_json(_SCREAMING_SNAKE_CASE ) for k, v in o.items()}
else:
return o
| 27
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import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, 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_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = StableDiffusionXLImgaImgPipeline
__SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
__SCREAMING_SNAKE_CASE = PipelineTesterMixin.required_optional_params - {'''latents'''}
__SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__SCREAMING_SNAKE_CASE = IMAGE_TO_IMAGE_IMAGE_PARAMS
__SCREAMING_SNAKE_CASE = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase ( self ):
torch.manual_seed(0 )
A__ = 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'''),attention_head_dim=(2, 4),use_linear_projection=__lowerCamelCase,addition_embed_type='''text_time''',addition_time_embed_dim=8,transformer_layers_per_block=(1, 2),projection_class_embeddings_input_dim=80,cross_attention_dim=64,)
A__ = EulerDiscreteScheduler(
beta_start=0.00085,beta_end=0.012,steps_offset=1,beta_schedule='''scaled_linear''',timestep_spacing='''leading''',)
torch.manual_seed(0 )
A__ = 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 )
A__ = 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=1000,hidden_act='''gelu''',projection_dim=32,)
A__ = CLIPTextModel(__lowerCamelCase )
A__ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''',local_files_only=__lowerCamelCase )
A__ = CLIPTextModelWithProjection(__lowerCamelCase )
A__ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''',local_files_only=__lowerCamelCase )
A__ = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''text_encoder_2''': text_encoder_a,
'''tokenizer_2''': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase=0 ):
A__ = floats_tensor((1, 3, 32, 32),rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
A__ = image / 2 + 0.5
if str(__lowerCamelCase ).startswith('''mps''' ):
A__ = torch.manual_seed(__lowerCamelCase )
else:
A__ = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
A__ = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 5.0,
'''output_type''': '''numpy''',
'''strength''': 0.75,
}
return inputs
def UpperCamelCase ( self ):
A__ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
A__ = self.get_dummy_components()
A__ = StableDiffusionXLImgaImgPipeline(**__lowerCamelCase )
A__ = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
A__ = self.get_dummy_inputs(__lowerCamelCase )
A__ = sd_pipe(**__lowerCamelCase ).images
A__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A__ = np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def UpperCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def UpperCamelCase ( self ):
pass
def UpperCamelCase ( self ):
A__ = self.get_dummy_components()
A__ = StableDiffusionXLImgaImgPipeline(**__lowerCamelCase )
A__ = sd_pipe.to(__lowerCamelCase )
A__ = sd_pipe.to(__lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )
# forward without prompt embeds
A__ = self.get_dummy_inputs(__lowerCamelCase )
A__ = 3 * ['''this is a negative prompt''']
A__ = negative_prompt
A__ = 3 * [inputs['''prompt''']]
A__ = sd_pipe(**__lowerCamelCase )
A__ = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
A__ = self.get_dummy_inputs(__lowerCamelCase )
A__ = 3 * ['''this is a negative prompt''']
A__ = 3 * [inputs.pop('''prompt''' )]
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) = sd_pipe.encode_prompt(__lowerCamelCase,negative_prompt=__lowerCamelCase )
A__ = sd_pipe(
**__lowerCamelCase,prompt_embeds=__lowerCamelCase,negative_prompt_embeds=__lowerCamelCase,pooled_prompt_embeds=__lowerCamelCase,negative_pooled_prompt_embeds=__lowerCamelCase,)
A__ = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase="cpu",__lowerCamelCase=torch.floataa,__lowerCamelCase=0 ):
A__ = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
A__ = np.random.RandomState(__lowerCamelCase ).standard_normal((1, 4, 64, 64) )
A__ = torch.from_numpy(__lowerCamelCase ).to(device=__lowerCamelCase,dtype=__lowerCamelCase )
A__ = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def UpperCamelCase ( self ):
A__ = DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''' )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
A__ = self.get_inputs(__lowerCamelCase )
A__ = pipe(**__lowerCamelCase ).images
A__ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
A__ = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 39
|
def UpperCamelCase__( UpperCamelCase__ : int = 1_00 )->int:
A__ = (n * (n + 1) // 2) ** 2
A__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F"{solution() = }")
| 39
| 1
|
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[str] = CodeGenTokenizer
__lowerCamelCase : Any = CodeGenTokenizerFast
__lowerCamelCase : Any = True
__lowerCamelCase : Any = {"add_prefix_space": True}
__lowerCamelCase : int = False
def _lowerCAmelCase ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A : str = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
"""<|endoftext|>""",
]
A : List[str] = dict(zip(lowerCamelCase__, range(len(lowerCamelCase__ ) ) ) )
A : List[Any] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
A : int = {"""unk_token""": """<unk>"""}
A : Union[str, Any] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["""vocab_file"""] )
A : Union[str, Any] = os.path.join(self.tmpdirname, 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 _lowerCAmelCase ( self, **lowerCamelCase__ ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : Dict = """lower newer"""
A : Any = """lower newer"""
return input_text, output_text
def _lowerCAmelCase ( self ):
A : int = CodeGenTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map )
A : Any = """lower newer"""
A : Optional[Any] = ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
A : Optional[Any] = tokenizer.tokenize(lowerCamelCase__, add_prefix_space=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
A : Any = tokens + [tokenizer.unk_token]
A : Tuple = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ), lowerCamelCase__ )
def _lowerCAmelCase ( self ):
if not self.test_rust_tokenizer:
return
A : str = self.get_tokenizer()
A : str = self.get_rust_tokenizer(add_prefix_space=lowerCamelCase__ )
A : Any = """lower newer"""
# Testing tokenization
A : str = tokenizer.tokenize(lowerCamelCase__, add_prefix_space=lowerCamelCase__ )
A : Dict = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
# Testing conversion to ids without special tokens
A : Union[str, Any] = tokenizer.encode(lowerCamelCase__, add_special_tokens=lowerCamelCase__, add_prefix_space=lowerCamelCase__ )
A : int = rust_tokenizer.encode(lowerCamelCase__, add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
# Testing conversion to ids with special tokens
A : List[Any] = self.get_rust_tokenizer(add_prefix_space=lowerCamelCase__ )
A : Union[str, Any] = tokenizer.encode(lowerCamelCase__, add_prefix_space=lowerCamelCase__ )
A : Any = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
# Testing the unknown token
A : Tuple = tokens + [rust_tokenizer.unk_token]
A : Any = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCamelCase__ ), lowerCamelCase__ )
def _lowerCAmelCase ( self, *lowerCamelCase__, **lowerCamelCase__ ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def _lowerCAmelCase ( self, lowerCamelCase__=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
A : Any = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__, **lowerCamelCase__ )
# Simple input
A : Tuple = """This is a simple input"""
A : List[Any] = ["""This is a simple input 1""", """This is a simple input 2"""]
A : Union[str, Any] = ("""This is a simple input""", """This is a pair""")
A : List[Any] = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(lowerCamelCase__, tokenizer_r.encode, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""" )
# Simple input
self.assertRaises(lowerCamelCase__, tokenizer_r.encode_plus, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""" )
# Simple input
self.assertRaises(
lowerCamelCase__, tokenizer_r.batch_encode_plus, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""", )
# Pair input
self.assertRaises(lowerCamelCase__, tokenizer_r.encode, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""" )
# Pair input
self.assertRaises(lowerCamelCase__, tokenizer_r.encode_plus, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""" )
# Pair input
self.assertRaises(
lowerCamelCase__, tokenizer_r.batch_encode_plus, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""", )
def _lowerCAmelCase ( self ):
A : str = CodeGenTokenizer.from_pretrained(self.tmpdirname, pad_token="""<pad>""" )
# Simple input
A : Optional[Any] = """This is a simple input"""
A : List[str] = ["""This is a simple input looooooooong""", """This is a simple input"""]
A : int = ("""This is a simple input""", """This is a pair""")
A : Union[str, Any] = [
("""This is a simple input loooooong""", """This is a simple input"""),
("""This is a simple pair loooooong""", """This is a simple pair"""),
]
A : List[Any] = tokenizer.pad_token_id
A : Dict = tokenizer(lowerCamelCase__, padding="""max_length""", max_length=30, return_tensors="""np""" )
A : Optional[int] = tokenizer(lowerCamelCase__, padding=lowerCamelCase__, truncate=lowerCamelCase__, return_tensors="""np""" )
A : Union[str, Any] = tokenizer(*lowerCamelCase__, padding="""max_length""", max_length=60, return_tensors="""np""" )
A : Dict = tokenizer(lowerCamelCase__, padding=lowerCamelCase__, truncate=lowerCamelCase__, return_tensors="""np""" )
# s
# test single string max_length padding
self.assertEqual(out_s["""input_ids"""].shape[-1], 30 )
self.assertTrue(pad_token_id in out_s["""input_ids"""] )
self.assertTrue(0 in out_s["""attention_mask"""] )
# s2
# test automatic padding
self.assertEqual(out_sa["""input_ids"""].shape[-1], 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["""input_ids"""][0] )
self.assertFalse(0 in out_sa["""attention_mask"""][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["""input_ids"""][1] )
self.assertTrue(0 in out_sa["""attention_mask"""][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["""input_ids"""].shape[-1], 60 )
self.assertTrue(pad_token_id in out_p["""input_ids"""] )
self.assertTrue(0 in out_p["""attention_mask"""] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["""input_ids"""].shape[-1], 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["""input_ids"""][0] )
self.assertFalse(0 in out_pa["""attention_mask"""][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["""input_ids"""][1] )
self.assertTrue(0 in out_pa["""attention_mask"""][1] )
def _lowerCAmelCase ( self ):
A : str = """$$$"""
A : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname, bos_token=lowerCamelCase__, add_bos_token=lowerCamelCase__ )
A : Union[str, Any] = """This is a simple input"""
A : Union[str, Any] = ["""This is a simple input 1""", """This is a simple input 2"""]
A : Dict = tokenizer.bos_token_id
A : List[Any] = tokenizer(lowerCamelCase__ )
A : str = tokenizer(lowerCamelCase__ )
self.assertEqual(out_s.input_ids[0], lowerCamelCase__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
A : Optional[int] = tokenizer.decode(out_s.input_ids )
A : Tuple = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0], lowerCamelCase__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def _lowerCAmelCase ( self ):
A : Optional[int] = CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono""" )
A : Union[str, Any] = """\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"""
A : Any = """\nif len_a > len_b: result = a\nelse: result = b"""
A : Optional[int] = tokenizer.encode(lowerCamelCase__ )
A : Tuple = ["""^#""", re.escape("""<|endoftext|>""" ), """^'''""", """^\"\"\"""", """\n\n\n"""]
A : Any = tokenizer.decode(lowerCamelCase__, truncate_before_pattern=lowerCamelCase__ )
self.assertEqual(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
pass
| 116
|
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self, lowerCamelCase__, lowerCamelCase__=13, lowerCamelCase__=7, lowerCamelCase__=True, lowerCamelCase__=True, lowerCamelCase__=True, 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__=0.02, lowerCamelCase__=4, ):
A : List[str] = parent
A : Optional[int] = batch_size
A : Union[str, Any] = seq_length
A : Any = is_training
A : List[str] = use_attention_mask
A : Union[str, Any] = use_token_type_ids
A : Any = use_labels
A : str = vocab_size
A : Union[str, Any] = hidden_size
A : str = num_hidden_layers
A : List[Any] = num_attention_heads
A : Optional[int] = intermediate_size
A : Optional[Any] = hidden_act
A : Dict = hidden_dropout_prob
A : List[Any] = attention_probs_dropout_prob
A : Optional[int] = max_position_embeddings
A : int = type_vocab_size
A : str = type_sequence_label_size
A : List[Any] = initializer_range
A : str = num_choices
def _lowerCAmelCase ( self ):
A : Optional[int] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
A : Union[str, Any] = None
if self.use_attention_mask:
A : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
A : int = None
if self.use_token_type_ids:
A : List[Any] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
A : Optional[int] = AlbertConfig(
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, )
return config, input_ids, token_type_ids, attention_mask
def _lowerCAmelCase ( self ):
A : Dict = self.prepare_config_and_inputs()
A , A , A , A : str = config_and_inputs
A : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Any = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCAmelCase ( self ):
A : Dict = FlaxAlbertModelTester(self )
@slow
def _lowerCAmelCase ( self ):
for model_class_name in self.all_model_classes:
A : Dict = model_class_name.from_pretrained("""albert-base-v2""" )
A : Optional[int] = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCAmelCase ( self ):
A : Dict = FlaxAlbertModel.from_pretrained("""albert-base-v2""" )
A : List[str] = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
A : str = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
A : Optional[int] = model(lowerCamelCase__, attention_mask=lowerCamelCase__ )[0]
A : str = (1, 11, 768)
self.assertEqual(output.shape, lowerCamelCase__ )
A : Optional[int] = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4], lowerCamelCase__, atol=1e-4 ) )
| 116
| 1
|
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
_SCREAMING_SNAKE_CASE = get_logger()
_SCREAMING_SNAKE_CASE = None
class _lowerCAmelCase ( TensorFormatter[Mapping, "jax.Array", Mapping] ):
"""simple docstring"""
def __init__( self : Optional[Any] , __snake_case : Dict=None , __snake_case : Any=None , **__snake_case : Any )-> List[Any]:
super().__init__(features=__snake_case )
import jax
from jaxlib.xla_client import Device
if isinstance(__snake_case , __snake_case ):
raise ValueError(
f'''Expected {device} to be a `str` not {type(__snake_case )}, as `jaxlib.xla_extension.Device` '''
"""is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
"""the device with `str()` to get its string identifier that will be internally mapped """
"""to the actual `jaxlib.xla_extension.Device`.""" )
snake_case = device if isinstance(__snake_case , __snake_case ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
snake_case = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f'''Device with string identifier {self.device} not listed among the available '''
f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
f'''device: {str(jax.devices()[0] )}.''' )
snake_case = str(jax.devices()[0] )
snake_case = jnp_array_kwargs
@staticmethod
def lowerCAmelCase ( )-> Dict[str, "jaxlib.xla_extension.Device"]:
import jax
return {str(__snake_case ): device for device in jax.devices()}
def lowerCAmelCase ( self : Dict , __snake_case : str )-> Optional[int]:
import jax
import jax.numpy as jnp
if isinstance(__snake_case , __snake_case ) and column:
if all(
isinstance(__snake_case , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__snake_case , axis=0 )
return column
def lowerCAmelCase ( self : Optional[int] , __snake_case : Optional[Any] )-> Union[str, Any]:
import jax
import jax.numpy as jnp
if isinstance(__snake_case , (str, bytes, type(__snake_case )) ):
return value
elif isinstance(__snake_case , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
snake_case = {}
if isinstance(__snake_case , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
snake_case = {"""dtype""": jnp.intaa}
else:
snake_case = {"""dtype""": jnp.intaa}
elif isinstance(__snake_case , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
snake_case = {"""dtype""": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__snake_case , PIL.Image.Image ):
snake_case = np.asarray(__snake_case )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
snake_case = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(__snake_case , **{**default_dtype, **self.jnp_array_kwargs} )
def lowerCAmelCase ( self : List[Any] , __snake_case : Tuple )-> List[Any]:
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__snake_case , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__snake_case , """__array__""" ) and not isinstance(__snake_case , jax.Array ):
snake_case = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__snake_case , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__snake_case ) for substruct in data_struct] )
elif isinstance(__snake_case , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__snake_case ) for substruct in data_struct] )
return self._tensorize(__snake_case )
def lowerCAmelCase ( self : Optional[int] , __snake_case : dict )-> str:
return map_nested(self._recursive_tensorize , __snake_case , map_list=__snake_case )
def lowerCAmelCase ( self : List[str] , __snake_case : pa.Table )-> Mapping:
snake_case = self.numpy_arrow_extractor().extract_row(__snake_case )
snake_case = self.python_features_decoder.decode_row(__snake_case )
return self.recursive_tensorize(__snake_case )
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : pa.Table )-> "jax.Array":
snake_case = self.numpy_arrow_extractor().extract_column(__snake_case )
snake_case = self.python_features_decoder.decode_column(__snake_case , pa_table.column_names[0] )
snake_case = self.recursive_tensorize(__snake_case )
snake_case = self._consolidate(__snake_case )
return column
def lowerCAmelCase ( self : Any , __snake_case : pa.Table )-> Mapping:
snake_case = self.numpy_arrow_extractor().extract_batch(__snake_case )
snake_case = self.python_features_decoder.decode_batch(__snake_case )
snake_case = self.recursive_tensorize(__snake_case )
for column_name in batch:
snake_case = self._consolidate(batch[column_name] )
return batch
| 3
|
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __lowerCamelCase ( __lowerCAmelCase : dict ) -> tuple:
return (data["data"], data["target"])
def __lowerCamelCase ( __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray ) -> XGBClassifier:
snake_case = XGBClassifier()
classifier.fit(__lowerCAmelCase , __lowerCAmelCase )
return classifier
def __lowerCamelCase ( ) -> None:
snake_case = load_iris()
snake_case , snake_case = data_handling(__lowerCAmelCase )
snake_case , snake_case , snake_case , snake_case = train_test_split(
__lowerCAmelCase , __lowerCAmelCase , test_size=0.25 )
snake_case = iris["""target_names"""]
# Create an XGBoost Classifier from the training data
snake_case = xgboost(__lowerCAmelCase , __lowerCAmelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , display_labels=__lowerCAmelCase , cmap="""Blues""" , normalize="""true""" , )
plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 3
| 1
|
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
A : Union[str, Any] = logging.getLogger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] ="""sequence-classification"""
def __init__( self , __a ):
if type(__a ) == dict:
__lowerCAmelCase = Namespace(**__a )
__lowerCAmelCase = glue_output_modes[hparams.task]
__lowerCAmelCase = glue_tasks_num_labels[hparams.task]
super().__init__(__a , __a , self.mode )
def snake_case ( self , **__a ):
return self.model(**__a )
def snake_case ( self , __a , __a ):
__lowerCAmelCase = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
__lowerCAmelCase = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
__lowerCAmelCase = self(**__a )
__lowerCAmelCase = outputs[0]
__lowerCAmelCase = self.trainer.lr_schedulers[0]["scheduler"]
__lowerCAmelCase = {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def snake_case ( self ):
__lowerCAmelCase = self.hparams
__lowerCAmelCase = processors[args.task]()
__lowerCAmelCase = processor.get_labels()
for mode in ["train", "dev"]:
__lowerCAmelCase = self._feature_file(__a )
if os.path.exists(__a ) and not args.overwrite_cache:
logger.info("Loading features from cached file %s" , __a )
else:
logger.info("Creating features from dataset file at %s" , args.data_dir )
__lowerCAmelCase = (
processor.get_dev_examples(args.data_dir )
if mode == "dev"
else processor.get_train_examples(args.data_dir )
)
__lowerCAmelCase = convert_examples_to_features(
__a , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info("Saving features into cached file %s" , __a )
torch.save(__a , __a )
def snake_case ( self , __a , __a , __a = False ):
__lowerCAmelCase = "dev" if mode == "test" else mode
__lowerCAmelCase = self._feature_file(__a )
logger.info("Loading features from cached file %s" , __a )
__lowerCAmelCase = torch.load(__a )
__lowerCAmelCase = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
__lowerCAmelCase = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
__lowerCAmelCase = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
__lowerCAmelCase = torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
__lowerCAmelCase = torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__a , __a , __a , __a ) , batch_size=__a , shuffle=__a , )
def snake_case ( self , __a , __a ):
__lowerCAmelCase = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
__lowerCAmelCase = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None
__lowerCAmelCase = self(**__a )
__lowerCAmelCase , __lowerCAmelCase = outputs[:2]
__lowerCAmelCase = logits.detach().cpu().numpy()
__lowerCAmelCase = inputs["labels"].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def snake_case ( self , __a ):
__lowerCAmelCase = torch.stack([x["val_loss"] for x in outputs] ).mean().detach().cpu().item()
__lowerCAmelCase = np.concatenate([x["pred"] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
__lowerCAmelCase = np.argmax(__a , axis=1 )
elif self.hparams.glue_output_mode == "regression":
__lowerCAmelCase = np.squeeze(__a )
__lowerCAmelCase = np.concatenate([x["target"] for x in outputs] , axis=0 )
__lowerCAmelCase = [[] for _ in range(out_label_ids.shape[0] )]
__lowerCAmelCase = [[] for _ in range(out_label_ids.shape[0] )]
__lowerCAmelCase = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task , __a , __a )}
__lowerCAmelCase = dict(results.items() )
__lowerCAmelCase = results
return ret, preds_list, out_label_list
def snake_case ( self , __a ):
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self._eval_end(__a )
__lowerCAmelCase = ret["log"]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def snake_case ( self , __a ):
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self._eval_end(__a )
__lowerCAmelCase = ret["log"]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def snake_case ( __a , __a ):
BaseTransformer.add_model_specific_args(__a , __a )
parser.add_argument(
"--max_seq_length" , default=1_28 , type=__a , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--task" , default="" , type=__a , required=__a , help="The GLUE task to run" , )
parser.add_argument(
"--gpus" , default=0 , type=__a , help="The number of GPUs allocated for this, it is by default 0 meaning none" , )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
return parser
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = argparse.ArgumentParser()
add_generic_args(_UpperCamelCase , os.getcwd() )
__lowerCAmelCase = GLUETransformer.add_model_specific_args(_UpperCamelCase , os.getcwd() )
__lowerCAmelCase = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
__lowerCAmelCase = os.path.join(
"./results" , f"{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}" , )
os.makedirs(args.output_dir )
__lowerCAmelCase = GLUETransformer(_UpperCamelCase )
__lowerCAmelCase = generic_train(_UpperCamelCase , _UpperCamelCase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
__lowerCAmelCase = sorted(glob.glob(os.path.join(args.output_dir , "checkpoint-epoch=*.ckpt" ) , recursive=_UpperCamelCase ) )
__lowerCAmelCase = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(_UpperCamelCase )
if __name__ == "__main__":
main()
| 57
|
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : Union[str, Any] = 3_8_4
if "tiny" in model_name:
UpperCAmelCase__ : int = [3, 3, 9, 3]
UpperCAmelCase__ : Union[str, Any] = [9_6, 1_9_2, 3_8_4, 7_6_8]
if "small" in model_name:
UpperCAmelCase__ : Optional[int] = [3, 3, 2_7, 3]
UpperCAmelCase__ : Dict = [9_6, 1_9_2, 3_8_4, 7_6_8]
if "base" in model_name:
UpperCAmelCase__ : List[str] = [3, 3, 2_7, 3]
UpperCAmelCase__ : str = [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
UpperCAmelCase__ : Optional[int] = 5_1_2
if "large" in model_name:
UpperCAmelCase__ : Optional[Any] = [3, 3, 2_7, 3]
UpperCAmelCase__ : Optional[int] = [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
UpperCAmelCase__ : Optional[int] = 7_6_8
if "xlarge" in model_name:
UpperCAmelCase__ : Tuple = [3, 3, 2_7, 3]
UpperCAmelCase__ : int = [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
UpperCAmelCase__ : int = 1_0_2_4
# set label information
UpperCAmelCase__ : Tuple = 1_5_0
UpperCAmelCase__ : Union[str, Any] = """huggingface/label-files"""
UpperCAmelCase__ : Tuple = """ade20k-id2label.json"""
UpperCAmelCase__ : Dict = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
UpperCAmelCase__ : str = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
UpperCAmelCase__ : Union[str, Any] = {v: k for k, v in idalabel.items()}
UpperCAmelCase__ : str = ConvNextConfig(
depths=UpperCamelCase__ , hidden_sizes=UpperCamelCase__ , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
UpperCAmelCase__ : Union[str, Any] = UperNetConfig(
backbone_config=UpperCamelCase__ , auxiliary_in_channels=UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ , )
return config
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : str = []
# fmt: off
# stem
rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''') )
if i > 0:
rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''') )
rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''') )
rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''') )
# decode head
rename_keys.extend(
[
("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
] )
# fmt: on
return rename_keys
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Tuple = dct.pop(UpperCamelCase__ )
UpperCAmelCase__ : Dict = val
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : List[str] = {
"""upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
"""upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
"""upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
"""upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
"""upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
}
UpperCAmelCase__ : Tuple = model_name_to_url[model_name]
UpperCAmelCase__ : int = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location="""cpu""" )["""state_dict"""]
UpperCAmelCase__ : int = get_upernet_config(UpperCamelCase__ )
UpperCAmelCase__ : Tuple = UperNetForSemanticSegmentation(UpperCamelCase__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
UpperCAmelCase__ : Optional[Any] = state_dict.pop(UpperCamelCase__ )
if "bn" in key:
UpperCAmelCase__ : List[str] = key.replace("""bn""" , """batch_norm""" )
UpperCAmelCase__ : List[Any] = val
# rename keys
UpperCAmelCase__ : Any = create_rename_keys(UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
# verify on image
UpperCAmelCase__ : Optional[int] = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
UpperCAmelCase__ : Optional[Any] = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw ).convert("""RGB""" )
UpperCAmelCase__ : Optional[int] = SegformerImageProcessor()
UpperCAmelCase__ : Dict = processor(UpperCamelCase__ , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
UpperCAmelCase__ : Dict = model(UpperCamelCase__ )
if model_name == "upernet-convnext-tiny":
UpperCAmelCase__ : Any = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] )
elif model_name == "upernet-convnext-small":
UpperCAmelCase__ : Dict = torch.tensor(
[[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]] )
elif model_name == "upernet-convnext-base":
UpperCAmelCase__ : Optional[Any] = torch.tensor(
[[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]] )
elif model_name == "upernet-convnext-large":
UpperCAmelCase__ : str = torch.tensor(
[[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]] )
elif model_name == "upernet-convnext-xlarge":
UpperCAmelCase__ : List[str] = torch.tensor(
[[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , UpperCamelCase__ , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCamelCase__ )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print(f'''Pushing model and processor for {model_name} to hub''' )
model.push_to_hub(f'''openmmlab/{model_name}''' )
processor.push_to_hub(f'''openmmlab/{model_name}''' )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='upernet-convnext-tiny',
type=str,
choices=[f"""upernet-convnext-{size}""" for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
help='Name of the ConvNext UperNet 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 or not to push the converted model to the 🤗 hub.'
)
__A =parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 163
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import 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
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case__ , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(snake_case__ , "num_attention_heads" ) )
self.parent.assertTrue(hasattr(snake_case__ , "num_encoder_blocks" ) )
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=64 , snake_case__=3 , snake_case__=4 , snake_case__=[2, 2, 2, 2] , snake_case__=[8, 4, 2, 1] , snake_case__=[16, 32, 64, 128] , snake_case__=[1, 4, 8, 16] , snake_case__=[1, 2, 4, 8] , snake_case__=True , snake_case__=True , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.02 , snake_case__=3 , snake_case__=None , ):
'''simple docstring'''
lowercase__ : List[str]= parent
lowercase__ : Optional[int]= batch_size
lowercase__ : int= image_size
lowercase__ : Optional[int]= num_channels
lowercase__ : str= num_encoder_blocks
lowercase__ : str= sr_ratios
lowercase__ : List[str]= depths
lowercase__ : List[str]= hidden_sizes
lowercase__ : str= downsampling_rates
lowercase__ : str= num_attention_heads
lowercase__ : Tuple= is_training
lowercase__ : Any= use_labels
lowercase__ : Any= hidden_act
lowercase__ : Optional[Any]= hidden_dropout_prob
lowercase__ : Tuple= attention_probs_dropout_prob
lowercase__ : Dict= initializer_range
lowercase__ : Union[str, Any]= num_labels
lowercase__ : Dict= scope
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Dict= None
if self.use_labels:
lowercase__ : List[Any]= ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase__ : Dict= self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : List[Any]= SegformerModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase__ : List[str]= model(snake_case__ )
lowercase__ : Any= self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.num_labels
lowercase__ : Union[str, Any]= SegformerForSemanticSegmentation(snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase__ : Dict= model(snake_case__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
lowercase__ : Dict= model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : str= 1
lowercase__ : List[str]= SegformerForSemanticSegmentation(config=snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase__ : Union[str, Any]= torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(snake_case__ )
lowercase__ : Optional[Any]= model(snake_case__ , labels=snake_case__ )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= self.prepare_config_and_inputs()
lowercase__, lowercase__, lowercase__ : List[str]= config_and_inputs
lowercase__ : Tuple= {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
__lowerCamelCase = (
{
"feature-extraction": SegformerModel,
"image-classification": SegformerForImageClassification,
"image-segmentation": SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= SegformerModelTester(self )
lowercase__ : Optional[Any]= SegformerConfigTester(self , config_class=snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[str]= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Any= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*snake_case__ )
@unittest.skip("SegFormer does not use inputs_embeds" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__, lowercase__ : Dict= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple= model_class(snake_case__ )
lowercase__ : List[str]= inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : List[str]= [*signature.parameters.keys()]
lowercase__ : Optional[int]= ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__, lowercase__ : Optional[int]= self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : str= True
for model_class in self.all_model_classes:
lowercase__ : Dict= True
lowercase__ : Any= False
lowercase__ : Optional[int]= True
lowercase__ : Any= model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
with torch.no_grad():
lowercase__ : Any= model(**self._prepare_for_class(snake_case__ , snake_case__ ) )
lowercase__ : str= outputs.attentions
lowercase__ : Dict= sum(self.model_tester.depths )
self.assertEqual(len(snake_case__ ) , snake_case__ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ : Union[str, Any]= True
lowercase__ : List[str]= model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
with torch.no_grad():
lowercase__ : List[str]= model(**self._prepare_for_class(snake_case__ , snake_case__ ) )
lowercase__ : Tuple= outputs.attentions
self.assertEqual(len(snake_case__ ) , snake_case__ )
# verify the first attentions (first block, first layer)
lowercase__ : Union[str, Any]= (self.model_tester.image_size // 4) ** 2
lowercase__ : str= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
lowercase__ : Any= (self.model_tester.image_size // 32) ** 2
lowercase__ : Union[str, Any]= (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
lowercase__ : Optional[int]= len(snake_case__ )
# Check attention is always last and order is fine
lowercase__ : Optional[int]= True
lowercase__ : Optional[int]= True
lowercase__ : str= model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
with torch.no_grad():
lowercase__ : List[Any]= model(**self._prepare_for_class(snake_case__ , snake_case__ ) )
self.assertEqual(out_len + 1 , len(snake_case__ ) )
lowercase__ : Union[str, Any]= outputs.attentions
self.assertEqual(len(snake_case__ ) , snake_case__ )
# verify the first attentions (first block, first layer)
lowercase__ : Optional[int]= (self.model_tester.image_size // 4) ** 2
lowercase__ : Any= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
def check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ ):
lowercase__ : str= model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
with torch.no_grad():
lowercase__ : str= model(**self._prepare_for_class(snake_case__ , snake_case__ ) )
lowercase__ : int= outputs.hidden_states
lowercase__ : int= self.model_tester.num_encoder_blocks
self.assertEqual(len(snake_case__ ) , snake_case__ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
lowercase__, lowercase__ : Tuple= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple= True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : List[Any]= True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
if not self.model_tester.is_training:
return
lowercase__, lowercase__ : Dict= self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int= True
for model_class in self.all_model_classes:
if model_class in get_values(snake_case__ ):
continue
lowercase__ : Any= model_class(snake_case__ )
model.to(snake_case__ )
model.train()
lowercase__ : Any= self._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
lowercase__ : Dict= model(**snake_case__ ).loss
loss.backward()
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : Tuple= SegformerModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def lowercase__() ->int:
"""simple docstring"""
lowercase__ : List[str]= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
# only resize + normalize
lowercase__ : Optional[int]= SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=snake_case__ , align=snake_case__ , do_random_crop=snake_case__ )
lowercase__ : Optional[Any]= SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512" ).to(
snake_case__ )
lowercase__ : Any= prepare_img()
lowercase__ : Dict= image_processor(images=snake_case__ , return_tensors="pt" )
lowercase__ : List[str]= encoded_inputs.pixel_values.to(snake_case__ )
with torch.no_grad():
lowercase__ : List[Any]= model(snake_case__ )
lowercase__ : Union[str, Any]= torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , snake_case__ )
lowercase__ : int= torch.tensor(
[
[[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
[[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
[[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
] ).to(snake_case__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , snake_case__ , atol=1e-4 ) )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
# only resize + normalize
lowercase__ : List[Any]= SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=snake_case__ , align=snake_case__ , do_random_crop=snake_case__ )
lowercase__ : Tuple= SegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b1-finetuned-cityscapes-1024-1024" ).to(snake_case__ )
lowercase__ : List[Any]= prepare_img()
lowercase__ : Optional[int]= image_processor(images=snake_case__ , return_tensors="pt" )
lowercase__ : Optional[Any]= encoded_inputs.pixel_values.to(snake_case__ )
with torch.no_grad():
lowercase__ : Union[str, Any]= model(snake_case__ )
lowercase__ : str= torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , snake_case__ )
lowercase__ : Union[str, Any]= torch.tensor(
[
[[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
[[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
[[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
] ).to(snake_case__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , snake_case__ , atol=1e-1 ) )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
# only resize + normalize
lowercase__ : str= SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=snake_case__ , align=snake_case__ , do_random_crop=snake_case__ )
lowercase__ : Dict= SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512" ).to(
snake_case__ )
lowercase__ : int= prepare_img()
lowercase__ : Union[str, Any]= image_processor(images=snake_case__ , return_tensors="pt" )
lowercase__ : Tuple= encoded_inputs.pixel_values.to(snake_case__ )
with torch.no_grad():
lowercase__ : int= model(snake_case__ )
lowercase__ : Tuple= outputs.logits.detach().cpu()
lowercase__ : Union[str, Any]= image_processor.post_process_semantic_segmentation(outputs=snake_case__ , target_sizes=[(500, 300)] )
lowercase__ : Optional[Any]= torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , snake_case__ )
lowercase__ : str= image_processor.post_process_semantic_segmentation(outputs=snake_case__ )
lowercase__ : str= torch.Size((128, 128) )
self.assertEqual(segmentation[0].shape , snake_case__ )
| 150
|
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
a : List[Any] = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
a : Dict = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def lowercase__(A ) ->Optional[int]:
"""simple docstring"""
lowercase__ : Any= torch.load(A , map_location="cpu" )
return sd
def lowercase__(A , A , A=rename_keys_prefix ) ->List[str]:
"""simple docstring"""
lowercase__ : int= OrderedDict()
lowercase__ : Optional[Any]= torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
lowercase__ : Union[str, Any]= key
for name_pair in rename_keys_prefix:
lowercase__ : str= new_key.replace(name_pair[0] , name_pair[1] )
lowercase__ : Union[str, Any]= d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
lowercase__ : Optional[int]= new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def lowercase__(A , A ) ->str:
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), f'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''
# Get Config
if "pre" in checkpoint_path:
lowercase__ : Union[str, Any]= "pretraining"
if "vcr" in checkpoint_path:
lowercase__ : str= {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
lowercase__ : Optional[Any]= {"visual_embedding_dim": 2_048}
elif "vqa" in checkpoint_path:
lowercase__ : int= {"visual_embedding_dim": 2_048}
elif "nlvr" in checkpoint_path:
lowercase__ : Tuple= {"visual_embedding_dim": 1_024}
else:
raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.''' )
else:
if "vcr" in checkpoint_path:
lowercase__ : int= {"visual_embedding_dim": 512}
lowercase__ : int= "multichoice"
elif "vqa_advanced" in checkpoint_path:
lowercase__ : Dict= {"visual_embedding_dim": 2_048}
lowercase__ : Optional[Any]= "vqa_advanced"
elif "vqa" in checkpoint_path:
lowercase__ : Optional[int]= {"visual_embedding_dim": 2_048, "num_labels": 3_129}
lowercase__ : List[str]= "vqa"
elif "nlvr" in checkpoint_path:
lowercase__ : Dict= {
"visual_embedding_dim": 1_024,
"num_labels": 2,
}
lowercase__ : Any= "nlvr"
lowercase__ : List[Any]= VisualBertConfig(**A )
# Load State Dict
lowercase__ : Union[str, Any]= load_state_dict(A )
lowercase__ : List[str]= get_new_dict(A , A )
if model_type == "pretraining":
lowercase__ : Optional[Any]= VisualBertForPreTraining(A )
elif model_type == "vqa":
lowercase__ : Any= VisualBertForQuestionAnswering(A )
elif model_type == "nlvr":
lowercase__ : Union[str, Any]= VisualBertForVisualReasoning(A )
elif model_type == "multichoice":
lowercase__ : str= VisualBertForMultipleChoice(A )
model.load_state_dict(A )
# Save Checkpoints
Path(A ).mkdir(exist_ok=A )
model.save_pretrained(A )
if __name__ == "__main__":
a : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
a : Dict = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 150
| 1
|
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
_UpperCamelCase = Mapping[str, np.ndarray]
_UpperCamelCase = Mapping[str, Any] # Is a nested dict.
_UpperCamelCase = 0.01
@dataclasses.dataclass(frozen=_UpperCAmelCase )
class __lowercase :
_UpperCamelCase = 42 # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
_UpperCamelCase = 42 # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
_UpperCamelCase = 42 # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
_UpperCamelCase = 42 # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
_UpperCamelCase = 42 # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
_UpperCamelCase = None
# Optional remark about the protein. Included as a comment in output PDB
# files
_UpperCamelCase = None
# Templates used to generate this protein (prediction-only)
_UpperCamelCase = None
# Chain corresponding to each parent
_UpperCamelCase = None
def _lowercase ( lowercase__ ):
__lowerCAmelCase : Optional[int] = r'''(\[[A-Z]+\]\n)'''
__lowerCAmelCase : List[str] = [tag.strip() for tag in re.split(lowercase__ , lowercase__ ) if len(lowercase__ ) > 0]
__lowerCAmelCase : Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split('''\n''' ) for l in tags[1::2]] )
__lowerCAmelCase : List[str] = ["N", "CA", "C"]
__lowerCAmelCase : Dict = None
__lowerCAmelCase : List[str] = None
__lowerCAmelCase : int = None
for g in groups:
if "[PRIMARY]" == g[0]:
__lowerCAmelCase : Optional[Any] = g[1][0].strip()
for i in range(len(lowercase__ ) ):
if seq[i] not in residue_constants.restypes:
__lowerCAmelCase : Any = '''X''' # FIXME: strings are immutable
__lowerCAmelCase : str = np.array(
[residue_constants.restype_order.get(lowercase__ , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
__lowerCAmelCase : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(lowercase__ , g[1][axis].split() ) ) )
__lowerCAmelCase : List[str] = np.array(lowercase__ )
__lowerCAmelCase : str = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(lowercase__ ):
__lowerCAmelCase : str = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
__lowerCAmelCase : Optional[int] = np.array(list(map({'''-''': 0, '''+''': 1}.get , g[1][0].strip() ) ) )
__lowerCAmelCase : Union[str, Any] = np.zeros(
(
len(lowercase__ ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(lowercase__ ):
__lowerCAmelCase : List[Any] = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=lowercase__ , atom_mask=lowercase__ , aatype=lowercase__ , residue_index=np.arange(len(lowercase__ ) ) , b_factors=lowercase__ , )
def _lowercase ( lowercase__ , lowercase__ = 0 ):
__lowerCAmelCase : List[str] = []
__lowerCAmelCase : Tuple = prot.remark
if remark is not None:
pdb_headers.append(f"""REMARK {remark}""" )
__lowerCAmelCase : Optional[Any] = prot.parents
__lowerCAmelCase : Tuple = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
__lowerCAmelCase : List[str] = [p for i, p in zip(lowercase__ , lowercase__ ) if i == chain_id]
if parents is None or len(lowercase__ ) == 0:
__lowerCAmelCase : Dict = ['''N/A''']
pdb_headers.append(f"""PARENT {" ".join(lowercase__ )}""" )
return pdb_headers
def _lowercase ( lowercase__ , lowercase__ ):
__lowerCAmelCase : List[str] = []
__lowerCAmelCase : str = pdb_str.split('''\n''' )
__lowerCAmelCase : Union[str, Any] = prot.remark
if remark is not None:
out_pdb_lines.append(f"""REMARK {remark}""" )
__lowerCAmelCase : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
__lowerCAmelCase : Tuple = []
if prot.parents_chain_index is not None:
__lowerCAmelCase : Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(lowercase__ ) , [] )
parent_dict[str(lowercase__ )].append(lowercase__ )
__lowerCAmelCase : int = max([int(lowercase__ ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
__lowerCAmelCase : Dict = parent_dict.get(str(lowercase__ ) , ['''N/A'''] )
parents_per_chain.append(lowercase__ )
else:
parents_per_chain.append(list(prot.parents ) )
else:
__lowerCAmelCase : Union[str, Any] = [['''N/A''']]
def make_parent_line(lowercase__ ) -> str:
return f"""PARENT {" ".join(lowercase__ )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
__lowerCAmelCase : Optional[Any] = 0
for i, l in enumerate(lowercase__ ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(lowercase__ )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(lowercase__ ):
__lowerCAmelCase : Tuple = parents_per_chain[chain_counter]
else:
__lowerCAmelCase : Tuple = ['''N/A''']
out_pdb_lines.append(make_parent_line(lowercase__ ) )
return "\n".join(lowercase__ )
def _lowercase ( lowercase__ ):
__lowerCAmelCase : Union[str, Any] = residue_constants.restypes + ['''X''']
def res_atoa(lowercase__ ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , '''UNK''' )
__lowerCAmelCase : Tuple = residue_constants.atom_types
__lowerCAmelCase : List[str] = []
__lowerCAmelCase : Any = prot.atom_mask
__lowerCAmelCase : Dict = prot.aatype
__lowerCAmelCase : Any = prot.atom_positions
__lowerCAmelCase : List[Any] = prot.residue_index.astype(np.intaa )
__lowerCAmelCase : int = prot.b_factors
__lowerCAmelCase : Any = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError('''Invalid aatypes.''' )
__lowerCAmelCase : Optional[Any] = get_pdb_headers(lowercase__ )
if len(lowercase__ ) > 0:
pdb_lines.extend(lowercase__ )
__lowerCAmelCase : Optional[int] = aatype.shape[0]
__lowerCAmelCase : Any = 1
__lowerCAmelCase : Optional[int] = 0
__lowerCAmelCase : Union[str, Any] = string.ascii_uppercase
__lowerCAmelCase : List[str] = None
# Add all atom sites.
for i in range(lowercase__ ):
__lowerCAmelCase : Any = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(lowercase__ , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
__lowerCAmelCase : Optional[int] = '''ATOM'''
__lowerCAmelCase : List[str] = atom_name if len(lowercase__ ) == 4 else f""" {atom_name}"""
__lowerCAmelCase : List[Any] = ''''''
__lowerCAmelCase : Optional[Any] = ''''''
__lowerCAmelCase : Optional[int] = 1.0_0
__lowerCAmelCase : List[Any] = atom_name[0] # Protein supports only C, N, O, S, this works.
__lowerCAmelCase : List[Any] = ''''''
__lowerCAmelCase : Tuple = '''A'''
if chain_index is not None:
__lowerCAmelCase : int = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
__lowerCAmelCase : Union[str, Any] = (
f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
f"""{res_name_a:>3} {chain_tag:>1}"""
f"""{residue_index[i]:>4}{insertion_code:>1} """
f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
f"""{occupancy:>6.2f}{b_factor:>6.2f} """
f"""{element:>2}{charge:>2}"""
)
pdb_lines.append(lowercase__ )
atom_index += 1
__lowerCAmelCase : str = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
__lowerCAmelCase : Union[str, Any] = True
__lowerCAmelCase : Optional[Any] = chain_index[i + 1]
if should_terminate:
# Close the chain.
__lowerCAmelCase : Tuple = '''TER'''
__lowerCAmelCase : Optional[int] = (
f"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(lowercase__ )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(lowercase__ , lowercase__ ) )
pdb_lines.append('''END''' )
pdb_lines.append('''''' )
return "\n".join(lowercase__ )
def _lowercase ( lowercase__ ):
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def _lowercase ( lowercase__ , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , ):
return Protein(
aatype=features['''aatype'''] , atom_positions=result['''final_atom_positions'''] , atom_mask=result['''final_atom_mask'''] , residue_index=features['''residue_index'''] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result['''final_atom_mask'''] ) , chain_index=lowercase__ , remark=lowercase__ , parents=lowercase__ , parents_chain_index=lowercase__ , )
| 275
|
def _lowercase ( lowercase__ , lowercase__ ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 275
| 1
|
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase_ ( UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase_ : str = GPTSanJapaneseTokenizer
UpperCamelCase_ : Union[str, Any] = False
UpperCamelCase_ : Dict = {"""do_clean_text""": False, """add_prefix_space""": False}
def _snake_case ( self : Any ) -> str:
'''simple docstring'''
super().setUp()
# fmt: off
A: Optional[Any] = ['''こん''', '''こんに''', '''にちは''', '''ばんは''', '''世界,㔺界''', '''、''', '''。''', '''<BR>''', '''<SP>''', '''<TAB>''', '''<URL>''', '''<EMAIL>''', '''<TEL>''', '''<DATE>''', '''<PRICE>''', '''<BLOCK>''', '''<KIGOU>''', '''<U2000U2BFF>''', '''<|emoji1|>''', '''<unk>''', '''<|bagoftoken|>''', '''<|endoftext|>''']
# fmt: on
A: Union[str, Any] = {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}} # 😀
A: Union[str, Any] = {'''unk_token''': '''<unk>'''}
A: Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
A: List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''emoji_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
with open(self.emoji_file , '''w''' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def _snake_case ( self : Optional[int] , **SCREAMING_SNAKE_CASE_ : str ) -> List[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str] ) -> Optional[int]:
'''simple docstring'''
A: Union[str, Any] = '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
A: Union[str, Any] = '''こんにちは、世界。 \nこんばんは、世界。😀'''
return input_text, output_text
def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : int ) -> Optional[int]:
'''simple docstring'''
A , A: int = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
A: Union[str, Any] = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
A: Optional[int] = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def _snake_case ( self : int ) -> Optional[Any]:
'''simple docstring'''
pass # TODO add if relevant
def _snake_case ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
pass # TODO add if relevant
def _snake_case ( self : List[str] ) -> str:
'''simple docstring'''
pass # TODO add if relevant
def _snake_case ( self : int ) -> str:
'''simple docstring'''
A: Optional[Any] = self.get_tokenizer()
# Testing tokenization
A: Dict = '''こんにちは、世界。 こんばんは、㔺界。'''
A: str = ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
A: str = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
A: int = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
A: Any = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
A: int = tokens + [tokenizer.unk_token]
A: int = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
A: List[Any] = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Any ) -> List[Any]:
'''simple docstring'''
A: Tuple = self.get_tokenizer()
# Testing tokenization
A: List[str] = '''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
A: Tuple = '''こんにちは、、、、世界。こんばんは、、、、世界。'''
A: str = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
A: List[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : str ) -> List[str]:
'''simple docstring'''
A: List[Any] = self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''' )
# Testing tokenization
A: Optional[int] = '''こんにちは、世界。'''
A: str = '''こんばんは、㔺界。😀'''
A: str = '''こんにちは、世界。こんばんは、世界。😀'''
A: Optional[Any] = tokenizer.encode(prefix_text + input_text )
A: Dict = tokenizer.encode('''''' , prefix_text=prefix_text + input_text )
A: str = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
A: Optional[int] = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
A: List[str] = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
A: Optional[int] = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : List[Any] ) -> Dict:
'''simple docstring'''
A: str = self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''' )
# Testing tokenization
A: Tuple = '''こんにちは、世界。'''
A: List[Any] = '''こんばんは、㔺界。😀'''
A: int = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
A: str = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
A: List[Any] = [1] + [0] * (len_prefix + len_text + 1)
A: Any = [1] * (len_prefix + len_text + 1) + [0]
A: Optional[Any] = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
A: Tuple = tokenizer(prefix_text + input_text ).token_type_ids
A: int = tokenizer('''''' , prefix_text=prefix_text + input_text ).token_type_ids
A: List[str] = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
A: Tuple = self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''' )
A: Dict = tokenizer.encode('''あンいワ''' )
A: str = tokenizer.encode('''''' , prefix_text='''あンいワ''' )
A: Dict = tokenizer.encode('''いワ''' , prefix_text='''あン''' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def _snake_case ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
A: Optional[int] = self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''' )
A: List[Any] = [['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
A: Tuple = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
A: str = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
A: Optional[Any] = [[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
A: Optional[int] = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
A: List[str] = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Any ) -> Dict:
'''simple docstring'''
pass
def _snake_case ( self : int ) -> List[Any]:
'''simple docstring'''
pass
| 334
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Optional[Any] = ["""input_features""", """attention_mask"""]
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple=80 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=1_60_00 , SCREAMING_SNAKE_CASE_ : int=80 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , **SCREAMING_SNAKE_CASE_ : List[str] , ) -> List[Any]:
'''simple docstring'''
super().__init__(feature_size=SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , padding_value=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
A: Union[str, Any] = num_mel_bins
A: str = do_ceptral_normalize
A: int = normalize_means
A: List[Any] = normalize_vars
A: Any = True
def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : np.ndarray , ) -> np.ndarray:
'''simple docstring'''
A: Optional[int] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
A: Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 )
A: List[Any] = ta_kaldi.fbank(SCREAMING_SNAKE_CASE_ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def _snake_case ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[bool] = True , SCREAMING_SNAKE_CASE_ : Optional[bool] = True , SCREAMING_SNAKE_CASE_ : float = 0.0 , ) -> np.ndarray:
'''simple docstring'''
if normalize_means:
A: str = x[:input_length].mean(axis=0 )
A: Dict = np.subtract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if normalize_vars:
A: Tuple = x[:input_length].std(axis=0 )
A: List[Any] = np.divide(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if input_length < x.shape[0]:
A: Optional[int] = padding_value
# make sure array is in float32
A: Optional[Any] = x.astype(np.floataa )
return x
def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[np.ndarray] , SCREAMING_SNAKE_CASE_ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
'''simple docstring'''
A: int = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
]
def __call__( self : Tuple , SCREAMING_SNAKE_CASE_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , SCREAMING_SNAKE_CASE_ : Union[bool, str, PaddingStrategy] = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , **SCREAMING_SNAKE_CASE_ : Dict , ) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
f""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
A: Any = isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
A: Optional[Any] = is_batched_numpy or (
isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
A: Optional[int] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ):
A: int = np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa )
elif isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A: Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A: Union[str, Any] = [raw_speech]
# extract fbank features
A: str = [self._extract_fbank_features(SCREAMING_SNAKE_CASE_ ) for waveform in raw_speech]
# convert into correct format for padding
A: int = BatchFeature({'''input_features''': features} )
A: int = self.pad(
SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
# make sure list is in array format
A: List[str] = padded_inputs.get('''input_features''' )
if isinstance(input_features[0] , SCREAMING_SNAKE_CASE_ ):
A: Optional[Any] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for feature in input_features]
A: List[Any] = padded_inputs.get('''attention_mask''' )
if attention_mask is not None:
A: Dict = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
A: Dict = (
np.array(SCREAMING_SNAKE_CASE_ , dtype=np.intaa )
if self._get_padding_strategies(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
A: List[Any] = self.normalize(
padded_inputs['''input_features'''] , attention_mask=SCREAMING_SNAKE_CASE_ )
if return_tensors is not None:
A: Dict = padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_ )
return padded_inputs
| 334
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 62
|
from math import ceil, sqrt
def _lowerCAmelCase ( __lowerCAmelCase = 1000000 ) -> int:
"""simple docstring"""
snake_case__ : Dict = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
snake_case__ : Any = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
snake_case__ : int = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 230
| 0
|
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE ) -> Union[str, Any]: # noqa: E741
__lowerCAmelCase: Union[str, Any] = len(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Tuple = 0
__lowerCAmelCase: List[Any] = [0] * n
__lowerCAmelCase: Tuple = [False] * n
__lowerCAmelCase: Optional[Any] = [False] * n
def dfs(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if parent == root:
out_edge_count += 1
__lowerCAmelCase: List[Any] = True
__lowerCAmelCase: Optional[int] = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
__lowerCAmelCase: Dict = dfs(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[Any] = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
__lowerCAmelCase: List[str] = True
# AP found via cycle
if at == low[to]:
__lowerCAmelCase: Optional[int] = True
else:
__lowerCAmelCase: Optional[Any] = min(low[at] , __SCREAMING_SNAKE_CASE )
return out_edge_count
for i in range(__SCREAMING_SNAKE_CASE ):
if not visited[i]:
__lowerCAmelCase: List[str] = 0
__lowerCAmelCase: Any = dfs(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , -1 , __SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Any = out_edge_count > 1
for x in range(len(__SCREAMING_SNAKE_CASE ) ):
if is_art[x] is True:
print(__SCREAMING_SNAKE_CASE )
# Adjacency list of graph
__A = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 356
|
"""simple docstring"""
from __future__ import annotations
from math import pi
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if inductance < 0:
raise ValueError("Inductance cannot be negative" )
if frequency < 0:
raise ValueError("Frequency cannot be negative" )
if reactance < 0:
raise ValueError("Inductive reactance cannot be negative" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 108
| 0
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __A ( metaclass=UpperCamelCase__ ):
a__ : List[str] = ["""onnx"""]
def __init__(self : List[Any] , *__a : Dict , **__a : Optional[Any] ):
requires_backends(self , ["onnx"] )
@classmethod
def _lowercase (cls : List[str] , *__a : Any , **__a : List[Any] ):
requires_backends(cls , ["onnx"] )
@classmethod
def _lowercase (cls : Optional[int] , *__a : Any , **__a : int ):
requires_backends(cls , ["onnx"] )
| 1
|
'''simple docstring'''
import math
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
UpperCAmelCase_ = input("Enter message: " )
UpperCAmelCase_ = int(input(f"""Enter key [2-{len(snake_case_ ) - 1}]: """ ) )
UpperCAmelCase_ = input("Encryption/Decryption [e/d]: " )
if mode.lower().startswith("e" ):
UpperCAmelCase_ = encrypt_message(snake_case_ , snake_case_ )
elif mode.lower().startswith("d" ):
UpperCAmelCase_ = decrypt_message(snake_case_ , snake_case_ )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f"""Output:\n{text + "|"}""" )
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : str ) -> str:
'''simple docstring'''
UpperCAmelCase_ = [""] * key
for col in range(snake_case_ ):
UpperCAmelCase_ = col
while pointer < len(snake_case_ ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(snake_case_ )
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : str ) -> str:
'''simple docstring'''
UpperCAmelCase_ = math.ceil(len(snake_case_ ) / key )
UpperCAmelCase_ = key
UpperCAmelCase_ = (num_cols * num_rows) - len(snake_case_ )
UpperCAmelCase_ = [""] * num_cols
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
UpperCAmelCase_ = 0
row += 1
return "".join(snake_case_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 1
| 1
|
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
'''simple docstring'''
return x + 2
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
snake_case : Optional[Any] = '''x = 3'''
snake_case : List[str] = {}
snake_case : Union[str, Any] = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
assert result == 3
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3} )
snake_case : int = '''x = y'''
snake_case : str = {'''y''': 5}
snake_case : str = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCamelCase__ , {'''x''': 5, '''y''': 5} )
def lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Optional[int] = '''y = add_two(x)'''
snake_case : Optional[int] = {'''x''': 3}
snake_case : Any = evaluate(UpperCamelCase__ , {'''add_two''': add_two} , state=UpperCamelCase__ )
assert result == 5
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''y''': 5} )
# Won't work without the tool
with CaptureStdout() as out:
snake_case : Union[str, Any] = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCAmelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Union[str, Any] = '''x = 3'''
snake_case : Tuple = {}
snake_case : Optional[Any] = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
assert result == 3
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3} )
def lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
snake_case : Optional[Any] = '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
snake_case : Tuple = {'''x''': 3}
snake_case : int = evaluate(UpperCamelCase__ , {'''add_two''': add_two} , state=UpperCamelCase__ )
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''y''': 5} )
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} )
def lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
snake_case : Optional[int] = '''x = 3\ny = 5'''
snake_case : List[Any] = {}
snake_case : Dict = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''y''': 5} )
def lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
snake_case : Any = '''text = f\'This is x: {x}.\''''
snake_case : Any = {'''x''': 3}
snake_case : Dict = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''text''': '''This is x: 3.'''} )
def lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
snake_case : Union[str, Any] = '''if x <= 3:\n y = 2\nelse:\n y = 5'''
snake_case : List[str] = {'''x''': 3}
snake_case : Dict = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''y''': 2} )
snake_case : List[str] = {'''x''': 8}
snake_case : int = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCamelCase__ , {'''x''': 8, '''y''': 5} )
def lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
snake_case : str = '''test_list = [x, add_two(x)]'''
snake_case : Dict = {'''x''': 3}
snake_case : str = evaluate(UpperCamelCase__ , {'''add_two''': add_two} , state=UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , [3, 5] )
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''test_list''': [3, 5]} )
def lowerCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
snake_case : List[Any] = '''y = x'''
snake_case : List[Any] = {'''x''': 3}
snake_case : Dict = evaluate(UpperCamelCase__ , {} , state=UpperCamelCase__ )
assert result == 3
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''y''': 3} )
def lowerCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
snake_case : Dict = '''test_list = [x, add_two(x)]\ntest_list[1]'''
snake_case : int = {'''x''': 3}
snake_case : Tuple = evaluate(UpperCamelCase__ , {'''add_two''': add_two} , state=UpperCamelCase__ )
assert result == 5
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''test_list''': [3, 5]} )
snake_case : int = '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
snake_case : Any = {'''x''': 3}
snake_case : str = evaluate(UpperCamelCase__ , {'''add_two''': add_two} , state=UpperCamelCase__ )
assert result == 5
self.assertDictEqual(UpperCamelCase__ , {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} )
def lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
snake_case : Any = '''x = 0\nfor i in range(3):\n x = i'''
snake_case : Optional[int] = {}
snake_case : Tuple = evaluate(UpperCamelCase__ , {'''range''': range} , state=UpperCamelCase__ )
assert result == 2
self.assertDictEqual(UpperCamelCase__ , {'''x''': 2, '''i''': 2} )
| 83
|
'''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 snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : List[str]=3 , UpperCamelCase__ : List[Any]=32 , UpperCamelCase__ : Optional[int]=3 , UpperCamelCase__ : Dict=10 , UpperCamelCase__ : Any=[10, 20, 30, 40] , UpperCamelCase__ : Any=[1, 1, 2, 1] , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : str="relu" , UpperCamelCase__ : Union[str, Any]=3 , UpperCamelCase__ : Tuple=None , ) -> List[str]:
"""simple docstring"""
snake_case : List[str] = parent
snake_case : Tuple = batch_size
snake_case : int = image_size
snake_case : Any = num_channels
snake_case : Optional[int] = embeddings_size
snake_case : Optional[int] = hidden_sizes
snake_case : str = depths
snake_case : Tuple = is_training
snake_case : List[str] = use_labels
snake_case : List[str] = hidden_act
snake_case : Tuple = num_labels
snake_case : Tuple = scope
snake_case : List[str] = len(UpperCamelCase__ )
def lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
snake_case : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case : Any = self.get_config()
return config, pixel_values
def lowerCAmelCase ( self : List[str] ) -> str:
"""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 lowerCAmelCase ( self : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int ) -> Tuple:
"""simple docstring"""
snake_case : List[str] = FlaxRegNetModel(config=UpperCamelCase__ )
snake_case : str = model(UpperCamelCase__ )
# 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 lowerCAmelCase ( self : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Dict ) -> Dict:
"""simple docstring"""
snake_case : int = self.num_labels
snake_case : List[str] = FlaxRegNetForImageClassification(config=UpperCamelCase__ )
snake_case : Any = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
snake_case : str = self.prepare_config_and_inputs()
snake_case ,snake_case : Tuple = config_and_inputs
snake_case : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class snake_case__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
def lowerCAmelCase ( self : List[str] ) -> None:
"""simple docstring"""
snake_case : List[str] = FlaxRegNetModelTester(self )
snake_case : Union[str, Any] = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def lowerCAmelCase ( self : Any ) -> str:
"""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 lowerCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
return
def lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def lowerCAmelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
pass
def lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
snake_case ,snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : Union[str, Any] = model_class(UpperCamelCase__ )
snake_case : Union[str, Any] = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case : int = [*signature.parameters.keys()]
snake_case : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def lowerCAmelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple ):
snake_case : Union[str, Any] = model_class(UpperCamelCase__ )
snake_case : Any = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
snake_case : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case : Optional[int] = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase__ ) , expected_num_stages + 1 )
snake_case ,snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : Tuple = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case : List[str] = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
snake_case ,snake_case : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
snake_case : Any = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
snake_case : Optional[Any] = model_class(UpperCamelCase__ )
@jax.jit
def model_jitted(UpperCamelCase__ : Dict , **UpperCamelCase__ : Tuple ):
return model(pixel_values=UpperCamelCase__ , **UpperCamelCase__ )
with self.subTest('''JIT Enabled''' ):
snake_case : Optional[int] = model_jitted(**UpperCamelCase__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
snake_case : Tuple = model_jitted(**UpperCamelCase__ ).to_tuple()
self.assertEqual(len(UpperCamelCase__ ) , len(UpperCamelCase__ ) )
for jitted_output, output in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
def _UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
snake_case : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def lowerCAmelCase ( self : int ) -> int:
"""simple docstring"""
snake_case : str = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
snake_case : Any = self.default_image_processor
snake_case : Any = prepare_img()
snake_case : Union[str, Any] = image_processor(images=UpperCamelCase__ , return_tensors='''np''' )
snake_case : List[str] = model(**UpperCamelCase__ )
# verify the logits
snake_case : Optional[int] = (1, 1000)
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
snake_case : Dict = jnp.array([-0.4_180, -1.5_051, -3.4_836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1e-4 ) )
| 83
| 1
|
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( _lowerCamelCase , unittest.TestCase ):
snake_case_ = LayoutLMTokenizer
snake_case_ = LayoutLMTokenizerFast
snake_case_ = True
snake_case_ = True
def A_ ( self : Optional[Any] ):
super().setUp()
snake_case_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
snake_case_ = 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 A_ ( self : Any , **lowercase_ : Optional[Any] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def A_ ( self : Union[str, Any] , lowercase_ : Optional[int] ):
snake_case_ = '''UNwant\u00E9d,running'''
snake_case_ = '''unwanted, running'''
return input_text, output_text
def A_ ( self : Dict ):
snake_case_ = self.tokenizer_class(self.vocab_file )
snake_case_ = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(lowercase_ , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , [7, 4, 5, 10, 8, 9] )
def A_ ( self : Any ):
pass
| 56
|
from __future__ import annotations
lowercase__ : str = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
'''simple docstring'''
snake_case_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_A ) )
] # the reference grid
snake_case_ = 1
snake_case_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_A ) )
] # the action grid
snake_case_ = init[0]
snake_case_ = init[1]
snake_case_ = 0
snake_case_ = g + heuristic[x][y] # cost from starting cell to destination cell
snake_case_ = [[f, g, x, y]]
snake_case_ = False # flag that is set when search is complete
snake_case_ = False # flag set if we can't find expand
while not found and not resign:
if len(_A ) == 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()
snake_case_ = cell.pop()
snake_case_ = next_cell[2]
snake_case_ = next_cell[3]
snake_case_ = next_cell[1]
if x == goal[0] and y == goal[1]:
snake_case_ = True
else:
for i in range(len(_A ) ): # to try out different valid actions
snake_case_ = x + DIRECTIONS[i][0]
snake_case_ = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(_A ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
snake_case_ = g + cost
snake_case_ = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
snake_case_ = 1
snake_case_ = i
snake_case_ = []
snake_case_ = goal[0]
snake_case_ = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
snake_case_ = x - DIRECTIONS[action[x][y]][0]
snake_case_ = y - DIRECTIONS[action[x][y]][1]
snake_case_ = xa
snake_case_ = ya
invpath.append([x, y] )
snake_case_ = []
for i in range(len(_A ) ):
path.append(invpath[len(_A ) - 1 - i] )
return path, action
if __name__ == "__main__":
lowercase__ : Union[str, Any] = [
[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],
]
lowercase__ : Optional[Any] = [0, 0]
# all coordinates are given in format [y,x]
lowercase__ : Tuple = [len(grid) - 1, len(grid[0]) - 1]
lowercase__ : Dict = 1
# the cost map which pushes the path closer to the goal
lowercase__ : 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])):
lowercase__ : Union[str, Any] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
lowercase__ : int = 99
lowercase__ , lowercase__ : Tuple = 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])
| 187
| 0
|
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
snake_case : Tuple = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention.self''',
'''self.proj''': '''output.dense''',
'''attention.self_mask''': '''attn_mask''',
'''mlp.fc1''': '''intermediate.dense''',
'''mlp.fc2''': '''output.dense''',
'''norm1''': '''layernorm_before''',
'''norm2''': '''layernorm_after''',
'''bn0''': '''batch_norm''',
}
snake_case : Dict = AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')
def __lowerCamelCase ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : str=False ):
"""simple docstring"""
a , a :str = create_model(
'''HTSAT-tiny''' , '''roberta''' , UpperCAmelCase_ , precision='''fp32''' , device='''cuda:0''' if torch.cuda.is_available() else '''cpu''' , enable_fusion=UpperCAmelCase_ , fusion_type='''aff_2d''' if enable_fusion else None , )
return model, model_cfg
def __lowerCamelCase ( UpperCAmelCase_ : Optional[Any] ):
"""simple docstring"""
a :Dict = {}
a :List[Any] = R'''.*sequential.(\d+).*'''
a :List[str] = R'''.*_projection.(\d+).*'''
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
a :int = key.replace(UpperCAmelCase_ , UpperCAmelCase_ )
if re.match(UpperCAmelCase_ , UpperCAmelCase_ ):
# replace sequential layers with list
a :Dict = re.match(UpperCAmelCase_ , UpperCAmelCase_ ).group(1 )
a :Any = key.replace(F'''sequential.{sequential_layer}.''' , F'''layers.{int(UpperCAmelCase_ )//3}.linear.''' )
elif re.match(UpperCAmelCase_ , UpperCAmelCase_ ):
a :Tuple = int(re.match(UpperCAmelCase_ , UpperCAmelCase_ ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
a :str = 1 if projecton_layer == 0 else 2
a :int = key.replace(F'''_projection.{projecton_layer}.''' , F'''_projection.linear{transformers_projection_layer}.''' )
if "audio" and "qkv" in key:
# split qkv into query key and value
a :Optional[Any] = value
a :Any = mixed_qkv.size(0 ) // 3
a :int = mixed_qkv[:qkv_dim]
a :Any = mixed_qkv[qkv_dim : qkv_dim * 2]
a :List[Any] = mixed_qkv[qkv_dim * 2 :]
a :Union[str, Any] = query_layer
a :int = key_layer
a :Optional[Any] = value_layer
else:
a :Optional[int] = value
return model_state_dict
def __lowerCamelCase ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any , UpperCAmelCase_ : str , UpperCAmelCase_ : int=False ):
"""simple docstring"""
a , a :Tuple = init_clap(UpperCAmelCase_ , enable_fusion=UpperCAmelCase_ )
clap_model.eval()
a :Tuple = clap_model.state_dict()
a :List[Any] = rename_state_dict(UpperCAmelCase_ )
a :Any = ClapConfig()
a :Optional[int] = enable_fusion
a :List[Any] = ClapModel(UpperCAmelCase_ )
# ignore the spectrogram embedding layer
model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ )
model.save_pretrained(UpperCAmelCase_ )
transformers_config.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
snake_case : Any = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
snake_case : Any = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 281
|
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
if n_term == "":
return []
a :list = []
for temp in range(int(UpperCAmelCase_ ) ):
series.append(F'''1/{temp + 1}''' if series else '''1''' )
return series
if __name__ == "__main__":
snake_case : Tuple = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 281
| 1
|
'''simple docstring'''
class a__ :
def __init__( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Tuple = n
_lowercase : Any = [None] * self.n
_lowercase : Tuple = 0 # index of the first element
_lowercase : Union[str, Any] = 0
_lowercase : str = 0
def __len__( self ):
"""simple docstring"""
return self.size
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.size == 0
def _lowerCamelCase ( self ):
"""simple docstring"""
return False if self.is_empty() else self.array[self.front]
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
if self.size >= self.n:
raise Exception("QUEUE IS FULL" )
_lowercase : Optional[int] = data
_lowercase : Dict = (self.rear + 1) % self.n
self.size += 1
return self
def _lowerCamelCase ( self ):
"""simple docstring"""
if self.size == 0:
raise Exception("UNDERFLOW" )
_lowercase : Optional[Any] = self.array[self.front]
_lowercase : List[Any] = None
_lowercase : int = (self.front + 1) % self.n
self.size -= 1
return temp
| 250
|
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
_snake_case = pytest.mark.integration
_snake_case = {'comet'}
_snake_case = importlib.util.find_spec('fairseq') is not None
_snake_case = {'code_eval'}
_snake_case = os.name == 'nt'
_snake_case = {'bertscore', 'frugalscore', 'perplexity'}
_snake_case = importlib.util.find_spec('transformers') is not None
def _A ( snake_case ) -> Tuple:
@wraps(snake_case )
def wrapper(self , snake_case ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("\"test requires Fairseq\"" )
else:
test_case(self , snake_case )
return wrapper
def _A ( snake_case ) -> Optional[int]:
@wraps(snake_case )
def wrapper(self , snake_case ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("\"test requires transformers\"" )
else:
test_case(self , snake_case )
return wrapper
def _A ( snake_case ) -> List[Any]:
@wraps(snake_case )
def wrapper(self , snake_case ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("\"test not supported on Windows\"" )
else:
test_case(self , snake_case )
return wrapper
def _A ( ) -> List[Any]:
_lowercase : Any = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
@local
class a__ ( parameterized.TestCase ):
_SCREAMING_SNAKE_CASE : Any = {}
_SCREAMING_SNAKE_CASE : Any = None
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning" )
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Tuple = "[...]"
_lowercase : Optional[Any] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , _UpperCamelCase ) ).module_path )
_lowercase : Union[str, Any] = datasets.load.import_main_class(metric_module.__name__ , dataset=_UpperCamelCase )
# check parameters
_lowercase : str = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(_UpperCamelCase , metric_module.__name__ ):
with self.use_local_metrics():
try:
_lowercase : int = doctest.testmod(_UpperCamelCase , verbose=_UpperCamelCase , raise_on_error=_UpperCamelCase )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Any = "[...]"
_lowercase : Dict = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , _UpperCamelCase ) ).module_path )
# run doctest
with self.use_local_metrics():
_lowercase : str = doctest.testmod(_UpperCamelCase , verbose=_UpperCamelCase , raise_on_error=_UpperCamelCase )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](_UpperCamelCase ):
yield
else:
yield
@contextmanager
def _lowerCamelCase ( self ):
"""simple docstring"""
def load_local_metric(_UpperCamelCase , *_UpperCamelCase , **_UpperCamelCase ):
return load_metric(os.path.join("metrics" , _UpperCamelCase ) , *_UpperCamelCase , **_UpperCamelCase )
with patch("datasets.load_metric" ) as mock_load_metric:
_lowercase : List[Any] = load_local_metric
yield
@classmethod
def _lowerCamelCase ( cls , _UpperCamelCase ):
"""simple docstring"""
def wrapper(_UpperCamelCase ):
_lowercase : str = contextmanager(_UpperCamelCase )
_lowercase : Any = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def _A ( snake_case ) -> List[Any]:
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("sv" , "" , "" ) # handle pytest cli flags
class a__ ( lowerCamelCase_ ):
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
assert len(input_dict["input_ids"] ) == 2
return np.array([1.0_3, 1.0_4] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
_lowercase : List[Any] = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def _A ( snake_case ) -> Tuple:
import torch
def bert_cos_score_idf(snake_case , snake_case , *snake_case , **snake_case ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(snake_case ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("bert_score.scorer.get_model" ), patch(
"bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
_lowercase : List[str] = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("comet" )
def _A ( snake_case ) -> Optional[int]:
def load_from_checkpoint(snake_case ):
class a__ :
def _lowerCamelCase ( self , _UpperCamelCase , *_UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
assert len(_UpperCamelCase ) == 2
_lowercase : Tuple = [0.1_9, 0.9_2]
return scores, sum(_UpperCamelCase ) / len(_UpperCamelCase )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("comet.download_model" ) as mock_download_model:
_lowercase : Union[str, Any] = None
with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
_lowercase : str = load_from_checkpoint
yield
def _A ( ) -> Optional[Any]:
_lowercase : str = load_metric(os.path.join("metrics" , "seqeval" ) )
_lowercase : Optional[int] = "ERROR"
_lowercase : Any = F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
with pytest.raises(snake_case , match=re.escape(snake_case ) ):
metric.compute(predictions=[] , references=[] , scheme=snake_case )
| 250
| 1
|
"""simple docstring"""
from datetime import datetime
import requests
def __lowerCAmelCase ( lowercase : List[Any] ) -> bytes:
"""simple docstring"""
snake_case : Dict = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
snake_case : Tuple = requests.get(base_url + url ).json()[0]['urls'][0]['src']
return requests.get(lowerCAmelCase_ ).content
if __name__ == "__main__":
__snake_case = input("""Enter Video/IGTV url: """).strip()
__snake_case = F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
with open(file_name, """wb""") as fp:
fp.write(download_video(url))
print(F'''Done. Video saved to disk as {file_name}.''')
| 369
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCAmelCase ( unittest.TestCase ):
@property
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
snake_case : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : Any = self.dummy_uncond_unet
snake_case : Tuple = KarrasVeScheduler()
snake_case : int = KarrasVePipeline(unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Optional[Any] = torch.manual_seed(0 )
snake_case : List[Any] = pipe(num_inference_steps=2 , generator=UpperCamelCase__ , output_type="numpy" ).images
snake_case : Dict = torch.manual_seed(0 )
snake_case : Dict = pipe(num_inference_steps=2 , generator=UpperCamelCase__ , output_type="numpy" , return_dict=UpperCamelCase__ )[0]
snake_case : Tuple = image[0, -3:, -3:, -1]
snake_case : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case : int = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[Any] = "google/ncsnpp-celebahq-256"
snake_case : List[str] = UNetaDModel.from_pretrained(UpperCamelCase__ )
snake_case : Optional[Any] = KarrasVeScheduler()
snake_case : Optional[int] = KarrasVePipeline(unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Dict = torch.manual_seed(0 )
snake_case : Union[str, Any] = pipe(num_inference_steps=20 , generator=UpperCamelCase__ , output_type="numpy" ).images
snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
snake_case : Any = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 112
| 0
|
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __SCREAMING_SNAKE_CASE :
snake_case_ = None
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Tuple =json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __lowercase )
def __magic_name__ ( self : str ) -> int:
SCREAMING_SNAKE_CASE__ : int =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Dict =os.path.join(__lowercase , '''feat_extract.json''' )
feat_extract_first.to_json_file(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.feature_extraction_class.from_json_file(__lowercase )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def __magic_name__ ( self : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : int =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Optional[int] =feat_extract_first.save_pretrained(__lowercase )[0]
check_json_file_has_correct_format(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.feature_extraction_class.from_pretrained(__lowercase )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.feature_extraction_class()
self.assertIsNotNone(__lowercase )
| 152
|
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =AlbertConfig.from_json_file(UpperCamelCase__ )
print(f"Building PyTorch model from configuration: {config}" )
SCREAMING_SNAKE_CASE__ : Any =AlbertForPreTraining(UpperCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict(), UpperCamelCase__ )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--albert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained ALBERT 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_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 152
| 1
|
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@require_torch
def UpperCAmelCase ( self ):
'''simple docstring'''
__UpperCamelCase = pipeline(
task='zero-shot-audio-classification' , model='hf-internal-testing/tiny-clap-htsat-unfused' )
__UpperCamelCase = load_dataset('ashraq/esc50' )
__UpperCamelCase = dataset['train']['audio'][-1]['array']
__UpperCamelCase = audio_classifier(__UpperCAmelCase , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [{'score': 0.5_0_1, 'label': 'Sound of a dog'}, {'score': 0.4_9_9, 'label': 'Sound of vaccum cleaner'}] , )
@unittest.skip('No models are available in TF' )
def UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
@require_torch
def UpperCAmelCase ( self ):
'''simple docstring'''
__UpperCamelCase = pipeline(
task='zero-shot-audio-classification' , model='laion/clap-htsat-unfused' , )
# This is an audio of a dog
__UpperCamelCase = load_dataset('ashraq/esc50' )
__UpperCamelCase = dataset['train']['audio'][-1]['array']
__UpperCamelCase = audio_classifier(__UpperCAmelCase , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
{'score': 0.9_9_9, 'label': 'Sound of a dog'},
{'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
] , )
__UpperCamelCase = audio_classifier([audio] * 5 , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
[
{'score': 0.9_9_9, 'label': 'Sound of a dog'},
{'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
],
]
* 5 , )
__UpperCamelCase = audio_classifier(
[audio] * 5 , candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'] , batch_size=5 )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
[
{'score': 0.9_9_9, 'label': 'Sound of a dog'},
{'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
],
]
* 5 , )
@unittest.skip('No models are available in TF' )
def UpperCAmelCase ( self ):
'''simple docstring'''
pass
| 263
|
"""simple docstring"""
def A ( snake_case :list[list[int]] , snake_case :int , snake_case :int , snake_case :list[int] ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def A ( snake_case :list[list[int]] , snake_case :list[int] , snake_case :int ) -> bool:
# Base Case
if curr_ind == len(snake_case ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(snake_case ) ):
if valid_connection(snake_case , snake_case , snake_case , snake_case ):
# Insert current vertex into path as next transition
__UpperCamelCase = next_ver
# Validate created path
if util_hamilton_cycle(snake_case , snake_case , curr_ind + 1 ):
return True
# Backtrack
__UpperCamelCase = -1
return False
def A ( snake_case :list[list[int]] , snake_case :int = 0 ) -> list[int]:
__UpperCamelCase = [-1] * (len(snake_case ) + 1)
# initialize start and end of path with starting index
__UpperCamelCase = __UpperCamelCase = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(snake_case , snake_case , 1 ) else []
| 263
| 1
|
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def a__ ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 108
|
"""simple docstring"""
from __future__ import annotations
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Any = size
# approximate the overall size of segment tree with given value
lowerCAmelCase : Optional[int] = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
lowerCAmelCase : List[str] = [0 for i in range(0 , 4 * size )]
lowerCAmelCase : Dict = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
return idx * 2
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
return idx * 2 + 1
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
if left_element == right_element:
lowerCAmelCase : List[str] = a[left_element - 1]
else:
lowerCAmelCase : Tuple = (left_element + right_element) // 2
self.build(self.left(snake_case__ ) , snake_case__ , snake_case__ , snake_case__ )
self.build(self.right(snake_case__ ) , mid + 1 , snake_case__ , snake_case__ )
lowerCAmelCase : Tuple = max(
self.segment_tree[self.left(snake_case__ )] , self.segment_tree[self.right(snake_case__ )] )
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
if self.flag[idx] is True:
lowerCAmelCase : Optional[int] = self.lazy[idx]
lowerCAmelCase : List[str] = False
if left_element != right_element:
lowerCAmelCase : Optional[Any] = self.lazy[idx]
lowerCAmelCase : List[Any] = self.lazy[idx]
lowerCAmelCase : List[Any] = True
lowerCAmelCase : Optional[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
lowerCAmelCase : str = val
if left_element != right_element:
lowerCAmelCase : Optional[Any] = val
lowerCAmelCase : Union[str, Any] = val
lowerCAmelCase : int = True
lowerCAmelCase : int = True
return True
lowerCAmelCase : List[str] = (left_element + right_element) // 2
self.update(self.left(snake_case__ ) , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
self.update(self.right(snake_case__ ) , mid + 1 , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase : Optional[int] = max(
self.segment_tree[self.left(snake_case__ )] , self.segment_tree[self.right(snake_case__ )] )
return True
def lowercase__ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
if self.flag[idx] is True:
lowerCAmelCase : List[Any] = self.lazy[idx]
lowerCAmelCase : str = False
if left_element != right_element:
lowerCAmelCase : Tuple = self.lazy[idx]
lowerCAmelCase : List[Any] = self.lazy[idx]
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : str = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
lowerCAmelCase : Any = (left_element + right_element) // 2
lowerCAmelCase : Optional[int] = self.query(self.left(snake_case__ ) , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase : Dict = self.query(self.right(snake_case__ ) , mid + 1 , snake_case__ , snake_case__ , snake_case__ )
return max(snake_case__ , snake_case__ )
def __str__( self ):
"""simple docstring"""
return str([self.query(1 , 1 , self.size , snake_case__ , snake_case__ ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
lowerCAmelCase__ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
lowerCAmelCase__ = 15
lowerCAmelCase__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt)
| 108
| 1
|
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase :int = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def lowerCamelCase ( lowerCAmelCase : Tuple ):
"""simple docstring"""
__magic_name__ : Optional[Any] = test_results.split(' ' )
__magic_name__ : Tuple = 0
__magic_name__ : List[str] = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
__magic_name__ : Optional[int] = expressions[-2] if '=' in expressions[-1] else expressions[-1]
for i, expression in enumerate(lowerCAmelCase ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def lowerCamelCase ( lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
__magic_name__ : Optional[int] = {}
__magic_name__ : Optional[Any] = None
__magic_name__ : Optional[Any] = False
for line in failures_short_lines.split('\n' ):
if re.search(R'_ \[doctest\]' , lowerCAmelCase ):
__magic_name__ : int = True
__magic_name__ : Optional[Any] = line.split(' ' )[2]
elif in_error and not line.split(' ' )[0].isdigit():
__magic_name__ : Optional[int] = line
__magic_name__ : List[Any] = False
return failures
class _lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[int] , _A : str , _A : Dict ) -> int:
__magic_name__ : int = title
__magic_name__ : Optional[int] = doc_test_results['time_spent'].split(',' )[0]
__magic_name__ : int = doc_test_results['success']
__magic_name__ : List[Any] = doc_test_results['failures']
__magic_name__ : Any = self.n_success + self.n_failures
# Failures and success of the modeling tests
__magic_name__ : Optional[Any] = doc_test_results
@property
def __lowerCAmelCase ( self : Dict ) -> str:
__magic_name__ : Union[str, Any] = [self._time_spent]
__magic_name__ : str = 0
for time in time_spent:
__magic_name__ : Any = time.split(':' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(_A ) == 1:
__magic_name__ : Union[str, Any] = [0, 0, time_parts[0]]
__magic_name__ , __magic_name__ , __magic_name__ : int = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3600 + minutes * 60 + seconds
__magic_name__ , __magic_name__ , __magic_name__ : int = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
return F'{int(_A )}h{int(_A )}m{int(_A )}s'
@property
def __lowerCAmelCase ( self : str ) -> Dict:
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def __lowerCAmelCase ( self : Dict ) -> Dict:
return {
"type": "section",
"text": {
"type": "plain_text",
"text": F'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
@property
def __lowerCAmelCase ( self : Optional[Any] ) -> Dict:
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
F'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
F' {self.time}.'
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
@property
def __lowerCAmelCase ( self : str ) -> Dict:
__magic_name__ : List[Any] = 40
__magic_name__ : int = {k: v['failed'] for k, v in doc_test_results.items() if isinstance(_A , _A )}
__magic_name__ : Optional[Any] = ''
for category, failures in category_failures.items():
if len(_A ) == 0:
continue
if report != "":
report += "\n\n"
report += F'*{category} failures*:'.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(_A )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": F'The following examples had failures:\n\n\n{report}\n',
},
}
@property
def __lowerCAmelCase ( self : List[str] ) -> str:
__magic_name__ : int = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(_A )
@staticmethod
def __lowerCAmelCase ( ) -> int:
__magic_name__ : Dict = [
{
'type': 'section',
'text': {
'type': 'plain_text',
'text': 'There was an issue running the tests.',
},
'accessory': {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
'url': F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
]
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(_A )} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , text='There was an issue running the tests.' , blocks=_A , )
def __lowerCAmelCase ( self : List[str] ) -> Dict:
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(self.payload )} ) )
__magic_name__ : Any = F'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else 'All tests passed.'
__magic_name__ : str = client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , blocks=self.payload , text=_A , )
def __lowerCAmelCase ( self : Any , _A : List[str] , _A : int , _A : List[str] , _A : str ) -> Any:
__magic_name__ : Union[str, Any] = ''
for key, value in failures.items():
__magic_name__ : Tuple = value[:200] + ' [Truncated]' if len(_A ) > 250 else value
failures_text += F'*{key}*\n_{value}_\n\n'
__magic_name__ : List[Any] = job_name
__magic_name__ : Any = {'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}
if job_link is not None:
__magic_name__ : Optional[Any] = {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
'url': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def __lowerCAmelCase ( self : Union[str, Any] ) -> str:
if self.thread_ts is None:
raise ValueError('Can only post reply if a post has been made.' )
__magic_name__ : Optional[int] = self.doc_test_results.pop('job_link' )
self.doc_test_results.pop('failures' )
self.doc_test_results.pop('success' )
self.doc_test_results.pop('time_spent' )
__magic_name__ : Tuple = sorted(self.doc_test_results.items() , key=lambda _A : t[0] )
for job, job_result in sorted_dict:
if len(job_result['failures'] ):
__magic_name__ : Tuple = F'*Num failures* :{len(job_result["failed"] )} \n'
__magic_name__ : str = job_result['failures']
__magic_name__ : Optional[Any] = self.get_reply_blocks(_A , _A , _A , text=_A )
print('Sending the following reply' )
print(json.dumps({'blocks': blocks} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , text=F'Results for {job}' , blocks=_A , thread_ts=self.thread_ts['ts'] , )
time.sleep(1 )
def lowerCamelCase ( ):
"""simple docstring"""
__magic_name__ : Any = os.environ['GITHUB_RUN_ID']
__magic_name__ : str = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
__magic_name__ : str = requests.get(lowerCAmelCase ).json()
__magic_name__ : Optional[Any] = {}
try:
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
__magic_name__ : Optional[int] = math.ceil((result['total_count'] - 100) / 100 )
for i in range(lowerCAmelCase ):
__magic_name__ : int = requests.get(url + f'&page={i + 2}' ).json()
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
return jobs
except Exception as e:
print('Unknown error, could not fetch links.' , lowerCAmelCase )
return {}
def lowerCamelCase ( lowerCAmelCase : str ):
"""simple docstring"""
__magic_name__ : Dict = {}
if os.path.exists(lowerCAmelCase ):
__magic_name__ : int = os.listdir(lowerCAmelCase )
for file in files:
try:
with open(os.path.join(lowerCAmelCase , lowerCAmelCase ) , encoding='utf-8' ) as f:
__magic_name__ : Dict = f.read()
except UnicodeDecodeError as e:
raise ValueError(f'Could not open {os.path.join(lowerCAmelCase , lowerCAmelCase )}.' ) from e
return _artifact
def lowerCamelCase ( ):
"""simple docstring"""
class _lowerCamelCase :
'''simple docstring'''
def __init__( self : Any , _A : str ) -> Optional[int]:
__magic_name__ : Any = name
__magic_name__ : Optional[int] = []
def __str__( self : List[Any] ) -> Dict:
return self.name
def __lowerCAmelCase ( self : Dict , _A : str ) -> str:
self.paths.append({'name': self.name, 'path': path} )
__magic_name__ : Dict[str, Artifact] = {}
__magic_name__ : int = filter(os.path.isdir , os.listdir() )
for directory in directories:
__magic_name__ : List[str] = directory
if artifact_name not in _available_artifacts:
__magic_name__ : str = Artifact(lowerCAmelCase )
_available_artifacts[artifact_name].add_path(lowerCAmelCase )
return _available_artifacts
if __name__ == "__main__":
lowerCAmelCase :Optional[int] = get_job_links()
lowerCAmelCase :Any = retrieve_available_artifacts()
lowerCAmelCase :Union[str, Any] = collections.OrderedDict(
[
('''*.py''', '''API Examples'''),
('''*.md''', '''MD Examples'''),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
lowerCAmelCase :int = {
v: {
'''failed''': [],
'''failures''': {},
}
for v in docs.values()
}
# Link to the GitHub Action job
lowerCAmelCase :List[str] = github_actions_job_links.get('''run_doctests''')
lowerCAmelCase :List[str] = available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
lowerCAmelCase :Optional[int] = retrieve_artifact(artifact_path['''name'''])
if "stats" in artifact:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase :str = handle_test_results(artifact['''stats'''])
lowerCAmelCase :Any = failed
lowerCAmelCase :Any = success
lowerCAmelCase :int = time_spent[1:-1] + ''', '''
lowerCAmelCase :Tuple = extract_first_line_failure(artifact['''failures_short'''])
for line in artifact["summary_short"].split('''\n'''):
if re.search('''FAILED''', line):
lowerCAmelCase :Union[str, Any] = line.replace('''FAILED ''', '''''')
lowerCAmelCase :List[Any] = line.split()[0].replace('''\n''', '''''')
if "::" in line:
lowerCAmelCase , lowerCAmelCase :Dict = line.split('''::''')
else:
lowerCAmelCase , lowerCAmelCase :Tuple = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
lowerCAmelCase :str = docs[file_regex]
doc_test_results[category]["failed"].append(test)
lowerCAmelCase :Optional[Any] = all_failures[test] if test in all_failures else '''N/A'''
lowerCAmelCase :Dict = failure
break
lowerCAmelCase :Dict = Message('''🤗 Results of the doc tests.''', doc_test_results)
message.post()
message.post_reply()
| 275
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase :str = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :List[str] = ['''VisionEncoderDecoderModel''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :List[str] = ['''TFVisionEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase :Optional[Any] = ['''FlaxVisionEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
lowerCAmelCase :Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 275
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
A: str = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Optional[Any] = ['input_features', 'attention_mask']
def __init__( self , _SCREAMING_SNAKE_CASE=80 , _SCREAMING_SNAKE_CASE=16000 , _SCREAMING_SNAKE_CASE=80 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , **_SCREAMING_SNAKE_CASE , ) -> Dict:
'''simple docstring'''
super().__init__(feature_size=_SCREAMING_SNAKE_CASE , sampling_rate=_SCREAMING_SNAKE_CASE , padding_value=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = do_ceptral_normalize
UpperCAmelCase : Optional[int] = normalize_means
UpperCAmelCase : Any = normalize_vars
UpperCAmelCase : Any = True
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase : Optional[int] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
UpperCAmelCase : List[Any] = torch.from_numpy(_SCREAMING_SNAKE_CASE ).unsqueeze(0 )
UpperCAmelCase : Dict = ta_kaldi.fbank(_SCREAMING_SNAKE_CASE , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = 0.0 , ) -> np.ndarray:
'''simple docstring'''
if normalize_means:
UpperCAmelCase : Tuple = x[:input_length].mean(axis=0 )
UpperCAmelCase : Optional[Any] = np.subtract(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if normalize_vars:
UpperCAmelCase : Tuple = x[:input_length].std(axis=0 )
UpperCAmelCase : Dict = np.divide(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if input_length < x.shape[0]:
UpperCAmelCase : Optional[int] = padding_value
# make sure array is in float32
UpperCAmelCase : Any = x.astype(np.floataa )
return x
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[np.ndarray]:
'''simple docstring'''
UpperCAmelCase : Any = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
]
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
F" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
F" {self.sampling_rate} and not {sampling_rate}." )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
UpperCAmelCase : Union[str, Any] = isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"Only mono-channel audio is supported for input to {self}" )
UpperCAmelCase : Union[str, Any] = is_batched_numpy or (
isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase : Any = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ):
UpperCAmelCase : Union[str, Any] = np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase : List[str] = [raw_speech]
# extract fbank features
UpperCAmelCase : Optional[int] = [self._extract_fbank_features(_SCREAMING_SNAKE_CASE ) for waveform in raw_speech]
# convert into correct format for padding
UpperCAmelCase : Optional[Any] = BatchFeature({"""input_features""": features} )
UpperCAmelCase : Tuple = self.pad(
_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# make sure list is in array format
UpperCAmelCase : str = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase : Optional[int] = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa ) for feature in input_features]
UpperCAmelCase : Optional[Any] = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
UpperCAmelCase : int = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
UpperCAmelCase : List[str] = (
np.array(_SCREAMING_SNAKE_CASE , dtype=np.intaa )
if self._get_padding_strategies(_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE ) is not PaddingStrategy.DO_NOT_PAD
else None
)
UpperCAmelCase : Optional[int] = self.normalize(
padded_inputs["""input_features"""] , attention_mask=_SCREAMING_SNAKE_CASE )
if return_tensors is not None:
UpperCAmelCase : List[str] = padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE )
return padded_inputs
| 109
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class __lowercase (unittest.TestCase ):
_UpperCamelCase = ViTImageProcessor if is_vision_available() else None
@property
def UpperCamelCase__ ( self ) ->str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self ) ->str:
'''simple docstring'''
__lowerCAmelCase : Tuple = (3, 32, 128)
__lowerCAmelCase : List[str] = tempfile.mkdtemp()
# fmt: off
__lowerCAmelCase : List[str] = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
__lowerCAmelCase : Optional[int] = dict(zip(A_ , range(len(A_ ) ) ) )
__lowerCAmelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
__lowerCAmelCase : Union[str, Any] = {
'''do_normalize''': False,
'''do_resize''': True,
'''image_processor_type''': '''ViTImageProcessor''',
'''resample''': 3,
'''size''': {'''height''': 32, '''width''': 128},
}
__lowerCAmelCase : Optional[Any] = os.path.join(self.tmpdirname , A_ )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(A_ , A_ )
def UpperCamelCase__ ( self , **A_ ) ->Tuple:
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **A_ )
def UpperCamelCase__ ( self , **A_ ) ->Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **A_ )
def UpperCamelCase__ ( self ) ->Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCamelCase__ ( self ) ->Optional[Any]:
'''simple docstring'''
__lowerCAmelCase : Tuple = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
__lowerCAmelCase : str = Image.fromarray(np.moveaxis(A_ , 0 , -1 ) )
return image_input
def UpperCamelCase__ ( self ) ->Any:
'''simple docstring'''
__lowerCAmelCase : Dict = self.get_tokenizer()
__lowerCAmelCase : List[Any] = self.get_image_processor()
__lowerCAmelCase : List[Any] = MgpstrProcessor(tokenizer=A_ , image_processor=A_ )
processor.save_pretrained(self.tmpdirname )
__lowerCAmelCase : Union[str, Any] = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=A_ )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , A_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def UpperCamelCase__ ( self ) ->Optional[int]:
'''simple docstring'''
__lowerCAmelCase : Any = self.get_tokenizer()
__lowerCAmelCase : Union[str, Any] = self.get_image_processor()
__lowerCAmelCase : List[Any] = MgpstrProcessor(tokenizer=A_ , image_processor=A_ )
processor.save_pretrained(self.tmpdirname )
__lowerCAmelCase : List[Any] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__lowerCAmelCase : int = self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
__lowerCAmelCase : int = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=A_ , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , A_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def UpperCamelCase__ ( self ) ->List[str]:
'''simple docstring'''
__lowerCAmelCase : Any = self.get_image_processor()
__lowerCAmelCase : Optional[Any] = self.get_tokenizer()
__lowerCAmelCase : int = MgpstrProcessor(tokenizer=A_ , image_processor=A_ )
__lowerCAmelCase : Optional[int] = self.prepare_image_inputs()
__lowerCAmelCase : Optional[Any] = image_processor(A_ , return_tensors='''np''' )
__lowerCAmelCase : Tuple = processor(images=A_ , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCamelCase__ ( self ) ->str:
'''simple docstring'''
__lowerCAmelCase : str = self.get_image_processor()
__lowerCAmelCase : Union[str, Any] = self.get_tokenizer()
__lowerCAmelCase : Optional[Any] = MgpstrProcessor(tokenizer=A_ , image_processor=A_ )
__lowerCAmelCase : Any = '''test'''
__lowerCAmelCase : Dict = processor(text=A_ )
__lowerCAmelCase : str = tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase__ ( self ) ->Optional[int]:
'''simple docstring'''
__lowerCAmelCase : Dict = self.get_image_processor()
__lowerCAmelCase : Any = self.get_tokenizer()
__lowerCAmelCase : str = MgpstrProcessor(tokenizer=A_ , image_processor=A_ )
__lowerCAmelCase : List[Any] = '''test'''
__lowerCAmelCase : int = self.prepare_image_inputs()
__lowerCAmelCase : int = processor(text=A_ , images=A_ )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''labels'''] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def UpperCamelCase__ ( self ) ->Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase : List[Any] = self.get_image_processor()
__lowerCAmelCase : int = self.get_tokenizer()
__lowerCAmelCase : Any = MgpstrProcessor(tokenizer=A_ , image_processor=A_ )
__lowerCAmelCase : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
__lowerCAmelCase : Optional[int] = processor.char_decode(A_ )
__lowerCAmelCase : Tuple = tokenizer.batch_decode(A_ )
__lowerCAmelCase : Any = [seq.replace(''' ''' , '''''' ) for seq in decoded_tok]
self.assertListEqual(A_ , A_ )
def UpperCamelCase__ ( self ) ->Any:
'''simple docstring'''
__lowerCAmelCase : str = self.get_image_processor()
__lowerCAmelCase : Any = self.get_tokenizer()
__lowerCAmelCase : int = MgpstrProcessor(tokenizer=A_ , image_processor=A_ )
__lowerCAmelCase : Union[str, Any] = None
__lowerCAmelCase : Optional[Any] = self.prepare_image_inputs()
__lowerCAmelCase : List[Any] = processor(text=A_ , images=A_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def UpperCamelCase__ ( self ) ->List[str]:
'''simple docstring'''
__lowerCAmelCase : List[str] = self.get_image_processor()
__lowerCAmelCase : List[str] = self.get_tokenizer()
__lowerCAmelCase : Any = MgpstrProcessor(tokenizer=A_ , image_processor=A_ )
__lowerCAmelCase : List[Any] = torch.randn(1 , 27 , 38 )
__lowerCAmelCase : Optional[int] = torch.randn(1 , 27 , 5_0257 )
__lowerCAmelCase : Optional[Any] = torch.randn(1 , 27 , 3_0522 )
__lowerCAmelCase : List[str] = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds'''] )
| 275
| 0
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for attribute in key.split('''.''' ):
__UpperCamelCase :List[Any] = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if weight_type is not None:
__UpperCamelCase :Dict = getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).shape
else:
__UpperCamelCase :int = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
__UpperCamelCase :Dict = value
elif weight_type == "weight_g":
__UpperCamelCase :Optional[Any] = value
elif weight_type == "weight_v":
__UpperCamelCase :str = value
elif weight_type == "bias":
__UpperCamelCase :List[Any] = value
else:
__UpperCamelCase :Any = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = []
__UpperCamelCase :Dict = fairseq_model.state_dict()
__UpperCamelCase :Optional[Any] = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
__UpperCamelCase :List[Any] = False
if "conv_layers" in name:
load_conv_layer(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == '''group''' , )
__UpperCamelCase :List[Any] = True
else:
for key, mapped_key in MAPPING.items():
__UpperCamelCase :Tuple = '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or (key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0] and not is_finetuned):
__UpperCamelCase :Tuple = True
if "*" in mapped_key:
__UpperCamelCase :str = name.split(SCREAMING_SNAKE_CASE )[0].split('''.''' )[-2]
__UpperCamelCase :Union[str, Any] = mapped_key.replace('''*''' , SCREAMING_SNAKE_CASE )
if "weight_g" in name:
__UpperCamelCase :List[Any] = '''weight_g'''
elif "weight_v" in name:
__UpperCamelCase :Any = '''weight_v'''
elif "weight" in name:
__UpperCamelCase :Optional[int] = '''weight'''
elif "bias" in name:
__UpperCamelCase :Union[str, Any] = '''bias'''
else:
__UpperCamelCase :List[Any] = None
set_recursively(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(SCREAMING_SNAKE_CASE )
logger.warning(f"""Unused weights: {unused_weights}""" )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :int = full_name.split('''conv_layers.''' )[-1]
__UpperCamelCase :Union[str, Any] = name.split('''.''' )
__UpperCamelCase :Tuple = int(items[0] )
__UpperCamelCase :Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
__UpperCamelCase :Optional[int] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
__UpperCamelCase :Any = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
__UpperCamelCase :int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
__UpperCamelCase :Tuple = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(SCREAMING_SNAKE_CASE )
@torch.no_grad()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True ):
'''simple docstring'''
if config_path is not None:
__UpperCamelCase :List[Any] = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :str = HubertConfig()
if is_finetuned:
if dict_path:
__UpperCamelCase :str = Dictionary.load(SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__UpperCamelCase :List[str] = target_dict.pad_index
__UpperCamelCase :int = target_dict.bos_index
__UpperCamelCase :List[str] = target_dict.eos_index
__UpperCamelCase :Any = len(target_dict.symbols )
__UpperCamelCase :str = os.path.join(SCREAMING_SNAKE_CASE , '''vocab.json''' )
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE ) )
return
os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[Any] = WavaVecaCTCTokenizer(
SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=SCREAMING_SNAKE_CASE , )
__UpperCamelCase :Optional[int] = True if config.feat_extract_norm == '''layer''' else False
__UpperCamelCase :Union[str, Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=SCREAMING_SNAKE_CASE , return_attention_mask=SCREAMING_SNAKE_CASE , )
__UpperCamelCase :Optional[Any] = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
processor.save_pretrained(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Tuple = HubertForCTC(SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :Optional[Any] = HubertModel(SCREAMING_SNAKE_CASE )
if is_finetuned:
__UpperCamelCase :Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
__UpperCamelCase :str = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
__UpperCamelCase :int = model[0].eval()
recursively_load_weights(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
__lowercase = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 354
|
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
__UpperCamelCase :Union[str, Any] = grid[0]
for row_n in range(1 , len(SCREAMING_SNAKE_CASE ) ):
__UpperCamelCase :Optional[int] = grid[row_n]
__UpperCamelCase :Dict = fill_row(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__UpperCamelCase :Any = grid[row_n]
return grid[-1][-1]
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
current_row[0] += row_above[0]
for cell_n in range(1 , len(SCREAMING_SNAKE_CASE ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 105
| 0
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = DDIMPipeline
_a = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
_a = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
_a = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
_a = False
def snake_case ( self : str )-> Optional[Any]:
torch.manual_seed(0 )
lowerCamelCase__ : Union[str, Any] =UNetaDModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''), up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''), )
lowerCamelCase__ : Optional[Any] =DDIMScheduler()
lowerCamelCase__ : List[Any] ={'''unet''': unet, '''scheduler''': scheduler}
return components
def snake_case ( self : Optional[Any], lowerCamelCase : Dict, lowerCamelCase : Optional[Any]=0 )-> Optional[int]:
if str(lowerCamelCase ).startswith('''mps''' ):
lowerCamelCase__ : Dict =torch.manual_seed(lowerCamelCase )
else:
lowerCamelCase__ : Optional[int] =torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
lowerCamelCase__ : Tuple ={
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def snake_case ( self : Dict )-> str:
lowerCamelCase__ : Optional[Any] ='''cpu'''
lowerCamelCase__ : int =self.get_dummy_components()
lowerCamelCase__ : Optional[int] =self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : List[str] =self.get_dummy_inputs(lowerCamelCase )
lowerCamelCase__ : Any =pipe(**lowerCamelCase ).images
lowerCamelCase__ : Optional[Any] =image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 32, 32, 3) )
lowerCamelCase__ : Tuple =np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
lowerCamelCase__ : str =np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase, 1E-3 )
def snake_case ( self : Union[str, Any] )-> List[Any]:
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def snake_case ( self : Union[str, Any] )-> int:
super().test_save_load_local(expected_max_difference=3E-3 )
def snake_case ( self : List[Any] )-> List[Any]:
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def snake_case ( self : Optional[Any] )-> Optional[int]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self : Optional[Any] )-> List[str]:
lowerCamelCase__ : Optional[Any] ='''google/ddpm-cifar10-32'''
lowerCamelCase__ : Union[str, Any] =UNetaDModel.from_pretrained(lowerCamelCase )
lowerCamelCase__ : Optional[int] =DDIMScheduler()
lowerCamelCase__ : int =DDIMPipeline(unet=lowerCamelCase, scheduler=lowerCamelCase )
ddim.to(lowerCamelCase )
ddim.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : Tuple =torch.manual_seed(0 )
lowerCamelCase__ : int =ddim(generator=lowerCamelCase, eta=0.0, output_type='''numpy''' ).images
lowerCamelCase__ : Tuple =image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase__ : Any =np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case ( self : Optional[int] )-> Any:
lowerCamelCase__ : str ='''google/ddpm-ema-bedroom-256'''
lowerCamelCase__ : Optional[int] =UNetaDModel.from_pretrained(lowerCamelCase )
lowerCamelCase__ : Any =DDIMScheduler.from_pretrained(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =DDIMPipeline(unet=lowerCamelCase, scheduler=lowerCamelCase )
ddpm.to(lowerCamelCase )
ddpm.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : List[str] =torch.manual_seed(0 )
lowerCamelCase__ : Optional[Any] =ddpm(generator=lowerCamelCase, output_type='''numpy''' ).images
lowerCamelCase__ : Any =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowerCamelCase__ : Any =np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 238
|
"""simple docstring"""
import argparse
import os
import re
_lowercase : str = "src/diffusers"
# Pattern that looks at the indentation in a line.
_lowercase : List[Any] = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase : int = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_lowercase : Optional[int] = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
_lowercase : List[Any] = re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_lowercase : str = re.compile(r"\[([^\]]+)\]")
def snake_case__ ( __lowerCamelCase : Optional[int] ):
"""simple docstring"""
lowerCamelCase__ : List[str] =_re_indent.search(__lowerCamelCase )
return "" if search is None else search.groups()[0]
def snake_case__ ( __lowerCamelCase : Any , __lowerCamelCase : List[Any]="" , __lowerCamelCase : int=None , __lowerCamelCase : Optional[int]=None ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : Any =code.split('''\n''' )
if start_prompt is not None:
while not lines[index].startswith(__lowerCamelCase ):
index += 1
lowerCamelCase__ : Dict =['''\n'''.join(lines[:index] )]
else:
lowerCamelCase__ : Tuple =[]
# We split into blocks until we get to the `end_prompt` (or the end of the block).
lowerCamelCase__ : int =[lines[index]]
index += 1
while index < len(__lowerCamelCase ) and (end_prompt is None or not lines[index].startswith(__lowerCamelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__lowerCamelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ''' ''' ):
current_block.append(lines[index] )
blocks.append('''\n'''.join(__lowerCamelCase ) )
if index < len(__lowerCamelCase ) - 1:
lowerCamelCase__ : str =[lines[index + 1]]
index += 1
else:
lowerCamelCase__ : str =[]
else:
blocks.append('''\n'''.join(__lowerCamelCase ) )
lowerCamelCase__ : str =[lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__lowerCamelCase ) > 0:
blocks.append('''\n'''.join(__lowerCamelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__lowerCamelCase ):
blocks.append('''\n'''.join(lines[index:] ) )
return blocks
def snake_case__ ( __lowerCamelCase : Any ):
"""simple docstring"""
def _inner(__lowerCamelCase : Any ):
return key(__lowerCamelCase ).lower().replace('''_''' , '''''' )
return _inner
def snake_case__ ( __lowerCamelCase : int , __lowerCamelCase : Any=None ):
"""simple docstring"""
# If no key is provided, we use a noop.
def noop(__lowerCamelCase : List[str] ):
return x
if key is None:
lowerCamelCase__ : Tuple =noop
# Constants are all uppercase, they go first.
lowerCamelCase__ : Union[str, Any] =[obj for obj in objects if key(__lowerCamelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
lowerCamelCase__ : Optional[int] =[obj for obj in objects if key(__lowerCamelCase )[0].isupper() and not key(__lowerCamelCase ).isupper()]
# Functions begin with a lowercase, they go last.
lowerCamelCase__ : Optional[Any] =[obj for obj in objects if not key(__lowerCamelCase )[0].isupper()]
lowerCamelCase__ : int =ignore_underscore(__lowerCamelCase )
return sorted(__lowerCamelCase , key=__lowerCamelCase ) + sorted(__lowerCamelCase , key=__lowerCamelCase ) + sorted(__lowerCamelCase , key=__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
# This inner function sort imports between [ ].
def _replace(__lowerCamelCase : Optional[Any] ):
lowerCamelCase__ : Dict =match.groups()[0]
if "," not in imports:
return f'''[{imports}]'''
lowerCamelCase__ : List[Any] =[part.strip().replace('''"''' , '''''' ) for part in imports.split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCamelCase__ : Optional[int] =keys[:-1]
return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(__lowerCamelCase )] ) + "]"
lowerCamelCase__ : List[Any] =import_statement.split('''\n''' )
if len(__lowerCamelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
lowerCamelCase__ : Tuple =2 if lines[1].strip() == '''[''' else 1
lowerCamelCase__ : Any =[(i, _re_strip_line.search(__lowerCamelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
lowerCamelCase__ : List[Any] =sort_objects(__lowerCamelCase , key=lambda __lowerCamelCase : x[1] )
lowerCamelCase__ : Union[str, Any] =[lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__lowerCamelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
lowerCamelCase__ : List[str] =_re_bracket_content.sub(_replace , lines[1] )
else:
lowerCamelCase__ : str =[part.strip().replace('''"''' , '''''' ) for part in lines[1].split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCamelCase__ : Any =keys[:-1]
lowerCamelCase__ : List[Any] =get_indent(lines[1] ) + ''', '''.join([f'''"{k}"''' for k in sort_objects(__lowerCamelCase )] )
return "\n".join(__lowerCamelCase )
else:
# Finally we have to deal with imports fitting on one line
lowerCamelCase__ : Union[str, Any] =_re_bracket_content.sub(_replace , __lowerCamelCase )
return import_statement
def snake_case__ ( __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any]=True ):
"""simple docstring"""
with open(__lowerCamelCase , '''r''' ) as f:
lowerCamelCase__ : Optional[int] =f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
lowerCamelCase__ : int =split_code_in_indented_blocks(
__lowerCamelCase , start_prompt='''_import_structure = {''' , end_prompt='''if TYPE_CHECKING:''' )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__lowerCamelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
lowerCamelCase__ : Optional[Any] =main_blocks[block_idx]
lowerCamelCase__ : List[str] =block.split('''\n''' )
# Get to the start of the imports.
lowerCamelCase__ : Any =0
while line_idx < len(__lowerCamelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
lowerCamelCase__ : Tuple =len(__lowerCamelCase )
else:
line_idx += 1
if line_idx >= len(__lowerCamelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
lowerCamelCase__ : Any ='''\n'''.join(block_lines[line_idx:-1] )
lowerCamelCase__ : Dict =get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
lowerCamelCase__ : List[Any] =split_code_in_indented_blocks(__lowerCamelCase , indent_level=__lowerCamelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
lowerCamelCase__ : List[Any] =_re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
lowerCamelCase__ : Union[str, Any] =[(pattern.search(__lowerCamelCase ).groups()[0] if pattern.search(__lowerCamelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
lowerCamelCase__ : Optional[Any] =[(i, key) for i, key in enumerate(__lowerCamelCase ) if key is not None]
lowerCamelCase__ : List[Any] =[x[0] for x in sorted(__lowerCamelCase , key=lambda __lowerCamelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
lowerCamelCase__ : Optional[Any] =0
lowerCamelCase__ : Tuple =[]
for i in range(len(__lowerCamelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
lowerCamelCase__ : List[Any] =sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__lowerCamelCase )
count += 1
# And we put our main block back together with its first and last line.
lowerCamelCase__ : str ='''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__lowerCamelCase ):
if check_only:
return True
else:
print(f'''Overwriting {file}.''' )
with open(__lowerCamelCase , '''w''' ) as f:
f.write('''\n'''.join(__lowerCamelCase ) )
def snake_case__ ( __lowerCamelCase : Optional[Any]=True ):
"""simple docstring"""
lowerCamelCase__ : Any =[]
for root, _, files in os.walk(__lowerCamelCase ):
if "__init__.py" in files:
lowerCamelCase__ : Tuple =sort_imports(os.path.join(__lowerCamelCase , '''__init__.py''' ) , check_only=__lowerCamelCase )
if result:
lowerCamelCase__ : List[str] =[os.path.join(__lowerCamelCase , '''__init__.py''' )]
if len(__lowerCamelCase ) > 0:
raise ValueError(f'''Would overwrite {len(__lowerCamelCase )} files, run `make style`.''' )
if __name__ == "__main__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
_lowercase : List[Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 238
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : int = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class A ( __snake_case ):
__magic_name__ = '''vit_msn'''
def __init__( self , SCREAMING_SNAKE_CASE=768 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=3072 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=1e-06 , SCREAMING_SNAKE_CASE=224 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=True , **SCREAMING_SNAKE_CASE , ) -> int:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE )
A : List[str] = hidden_size
A : List[Any] = num_hidden_layers
A : Dict = num_attention_heads
A : str = intermediate_size
A : str = hidden_act
A : Union[str, Any] = hidden_dropout_prob
A : str = attention_probs_dropout_prob
A : List[str] = initializer_range
A : Dict = layer_norm_eps
A : Any = image_size
A : Optional[int] = patch_size
A : Optional[int] = num_channels
A : Union[str, Any] = qkv_bias
| 311
|
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : List[str] = {
'configuration_cpmant': ['CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CpmAntConfig'],
'tokenization_cpmant': ['CpmAntTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[Any] = [
'CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST',
'CpmAntForCausalLM',
'CpmAntModel',
'CpmAntPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
lowercase : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 311
| 1
|
from typing import List
import numpy as np
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
__lowercase : Optional[Any] = {key: len(__lowerCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__lowerCAmelCase , __lowerCAmelCase )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'''Sharding is ambiguous for this dataset: '''
+ '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
+ '''\n'''.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
+ '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
+ '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
) )
__lowercase : Union[str, Any] = max(lists_lengths.values() , default=0 )
return max(1 , __lowerCAmelCase )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> List[range]:
__lowercase : Dict = []
for group_idx in range(__lowerCAmelCase ):
__lowercase : Optional[Any] = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
__lowercase : Any = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
__lowercase : Dict = range(__lowerCAmelCase , start + num_shards_to_add )
shards_indices_per_group.append(__lowerCAmelCase )
return shards_indices_per_group
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> List[dict]:
__lowercase : Optional[Any] = _number_of_shards_in_gen_kwargs(__lowerCAmelCase )
if num_shards == 1:
return [dict(__lowerCAmelCase )]
else:
__lowercase : List[Any] = _distribute_shards(num_shards=__lowerCAmelCase , max_num_jobs=__lowerCAmelCase )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(__lowerCAmelCase , __lowerCAmelCase )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(__lowerCAmelCase ) )
]
def UpperCAmelCase_ ( __lowerCAmelCase ) -> dict:
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , __lowerCAmelCase )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> dict:
__lowercase : Dict = {len(__lowerCAmelCase ) for value in gen_kwargs.values() if isinstance(__lowerCAmelCase , __lowerCAmelCase )}
__lowercase : str = {}
for size in list_sizes:
__lowercase : Optional[Any] = list(range(__lowerCAmelCase ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
__lowercase : Union[str, Any] = dict(__lowerCAmelCase )
for key, value in shuffled_kwargs.items():
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
__lowercase : List[str] = [value[i] for i in indices_per_size[len(__lowerCAmelCase )]]
return shuffled_kwargs
| 156
|
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Optional[torch.FloatTensor] = None
A__ : torch.FloatTensor = None
A__ : Optional[Tuple[torch.FloatTensor]] = None
A__ : Optional[Tuple[torch.FloatTensor]] = None
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _snake_case : int=1 , _snake_case : int=0 , _snake_case : List[str]=2 , _snake_case : List[str]=512 , _snake_case : Tuple="cls" , _snake_case : Union[str, Any]=False , _snake_case : str=True , **_snake_case : Union[str, Any] , ):
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
__lowercase : Union[str, Any] = project_dim
__lowercase : str = pooler_fn
__lowercase : List[str] = learn_encoder
__lowercase : int = use_attention_mask
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Any = [r'''pooler''', r'''logit_scale''']
A__ : Dict = [r'''position_ids''', r'''predictions.decoder.bias''']
A__ : Union[str, Any] = '''roberta'''
A__ : str = RobertaSeriesConfig
def __init__( self : List[str] , _snake_case : Any ):
super().__init__(_snake_case )
__lowercase : Union[str, Any] = XLMRobertaModel(_snake_case )
__lowercase : Optional[int] = nn.Linear(config.hidden_size , config.project_dim )
__lowercase : Optional[int] = getattr(_snake_case , '''has_pre_transformation''' , _snake_case )
if self.has_pre_transformation:
__lowercase : Union[str, Any] = nn.Linear(config.hidden_size , config.project_dim )
__lowercase : Any = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def snake_case_ ( self : Dict , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[bool] = None , ):
__lowercase : List[str] = return_dict if return_dict is not None else self.config.use_return_dict
__lowercase : Any = self.base_model(
input_ids=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , position_ids=_snake_case , head_mask=_snake_case , inputs_embeds=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , output_attentions=_snake_case , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=_snake_case , )
if self.has_pre_transformation:
__lowercase : Optional[int] = outputs['''hidden_states'''][-2]
__lowercase : Union[str, Any] = self.pre_LN(_snake_case )
__lowercase : Optional[int] = self.transformation_pre(_snake_case )
return TransformationModelOutput(
projection_state=_snake_case , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
__lowercase : str = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=_snake_case , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 156
| 1
|
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__A = ' \"""\n Output class for the scheduler\'s step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"""\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n'
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ) -> int:
_lowerCAmelCase =tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , """schedulers/""" ) )
_lowerCAmelCase =self.diffusers_dir
shutil.copy(
os.path.join(__UpperCAmelCase , """src/diffusers/schedulers/scheduling_ddpm.py""" ) , os.path.join(self.diffusers_dir , """schedulers/scheduling_ddpm.py""" ) , )
def _lowerCAmelCase ( self ) -> Dict:
_lowerCAmelCase ="""src/diffusers"""
shutil.rmtree(self.diffusers_dir )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None ) -> Tuple:
_lowerCAmelCase =comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
if overwrite_result is not None:
_lowerCAmelCase =comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
_lowerCAmelCase =black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 )
_lowerCAmelCase =black.format_str(__UpperCAmelCase , mode=__UpperCAmelCase )
_lowerCAmelCase =os.path.join(self.diffusers_dir , """new_code.py""" )
with open(__UpperCAmelCase , """w""" , newline="""\n""" ) as f:
f.write(__UpperCAmelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__UpperCAmelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__UpperCAmelCase )
with open(__UpperCAmelCase , """r""" ) as f:
self.assertTrue(f.read() , __UpperCAmelCase )
def _lowerCAmelCase ( self ) -> Optional[int]:
_lowerCAmelCase =check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def _lowerCAmelCase ( self ) -> str:
# Base copy consistency
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , REFERENCE_CODE + """\n""" , )
# With no empty line at the end
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , __UpperCAmelCase , )
# Copy consistency with rename
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , re.sub("""DDPM""" , """Test""" , __UpperCAmelCase ) , )
# Copy consistency with a really long name
_lowerCAmelCase ="""TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
self.check_copy_consistency(
f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' , f'''{long_class_name}SchedulerOutput''' , re.sub("""Bert""" , __UpperCAmelCase , __UpperCAmelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , __UpperCAmelCase , overwrite_result=re.sub("""DDPM""" , """Test""" , __UpperCAmelCase ) , )
| 341
|
"""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 lowerCamelCase__ :
'''simple docstring'''
lowerCamelCase = XGLMConfig
lowerCamelCase = {}
lowerCamelCase = '''gelu'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=14 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=32 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=5_12 , __UpperCAmelCase=0.0_2 , ) -> List[str]:
_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 _lowerCAmelCase ( self ) -> Dict:
return XGLMConfig.from_pretrained("""facebook/xglm-564M""" )
def _lowerCAmelCase ( self ) -> str:
_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 _lowerCAmelCase ( self ) -> str:
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=__UpperCAmelCase , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=__UpperCAmelCase , )
def _lowerCAmelCase ( self ) -> Dict:
_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 lowerCamelCase__ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowerCamelCase = (TFXGLMForCausalLM,) if is_tf_available() else ()
lowerCamelCase = (
{'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
def _lowerCAmelCase ( self ) -> Tuple:
_lowerCAmelCase =TFXGLMModelTester(self )
_lowerCAmelCase =ConfigTester(self , config_class=__UpperCAmelCase , n_embd=37 )
def _lowerCAmelCase ( self ) -> int:
self.config_tester.run_common_tests()
@slow
def _lowerCAmelCase ( self ) -> Union[str, Any]:
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase =TFXGLMModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor.""" )
def _lowerCAmelCase ( self ) -> Union[str, Any]:
super().test_resize_token_embeddings()
@require_tf
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCAmelCase ( self , __UpperCAmelCase=True ) -> str:
_lowerCAmelCase =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
_lowerCAmelCase =tf.convert_to_tensor([[2, 2_68, 98_65]] , 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, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
# fmt: on
_lowerCAmelCase =model.generate(__UpperCAmelCase , do_sample=__UpperCAmelCase , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , __UpperCAmelCase )
@slow
def _lowerCAmelCase ( self ) -> Optional[Any]:
_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(__UpperCAmelCase , do_sample=__UpperCAmelCase , seed=[7, 0] )
_lowerCAmelCase =tokenizer.decode(output_ids[0] , skip_special_tokens=__UpperCAmelCase )
_lowerCAmelCase =(
"""Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
)
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
@slow
def _lowerCAmelCase ( self ) -> Union[str, Any]:
_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(__UpperCAmelCase , return_tensors="""tf""" , padding=__UpperCAmelCase )
_lowerCAmelCase =inputs["""input_ids"""]
_lowerCAmelCase =model.generate(input_ids=__UpperCAmelCase , attention_mask=inputs["""attention_mask"""] , max_new_tokens=12 )
_lowerCAmelCase =tokenizer(sentences[0] , return_tensors="""tf""" ).input_ids
_lowerCAmelCase =model.generate(input_ids=__UpperCAmelCase , max_new_tokens=12 )
_lowerCAmelCase =tokenizer(sentences[1] , return_tensors="""tf""" ).input_ids
_lowerCAmelCase =model.generate(input_ids=__UpperCAmelCase , max_new_tokens=12 )
_lowerCAmelCase =tokenizer.batch_decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase )
_lowerCAmelCase =tokenizer.decode(output_non_padded[0] , skip_special_tokens=__UpperCAmelCase )
_lowerCAmelCase =tokenizer.decode(output_padded[0] , skip_special_tokens=__UpperCAmelCase )
_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(__UpperCAmelCase , __UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , [non_padded_sentence, padded_sentence] )
| 341
| 1
|
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class lowercase :
_a = PegasusConfig
_a = {}
_a = "gelu"
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=32 , _a=2 , _a=4 , _a=37 , _a=0.1 , _a=0.1 , _a=40 , _a=2 , _a=1 , _a=0 , ) -> List[str]:
_A : List[Any] = parent
_A : Optional[Any] = batch_size
_A : Tuple = seq_length
_A : int = is_training
_A : List[Any] = use_labels
_A : int = vocab_size
_A : Dict = hidden_size
_A : Dict = num_hidden_layers
_A : int = num_attention_heads
_A : int = intermediate_size
_A : Dict = hidden_dropout_prob
_A : Dict = attention_probs_dropout_prob
_A : Union[str, Any] = max_position_embeddings
_A : int = eos_token_id
_A : Dict = pad_token_id
_A : List[str] = bos_token_id
def a__ ( self ) -> str:
_A : str = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_A : Optional[Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_A : Any = tf.concat([input_ids, eos_tensor] , axis=1 )
_A : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A : Optional[Any] = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , 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 , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_A : List[Any] = prepare_pegasus_inputs_dict(_a , _a , _a )
return config, inputs_dict
def a__ ( self , _a , _a ) -> Optional[Any]:
_A : Any = TFPegasusModel(config=_a ).get_decoder()
_A : Union[str, Any] = inputs_dict["""input_ids"""]
_A : Union[str, Any] = input_ids[:1, :]
_A : List[str] = inputs_dict["""attention_mask"""][:1, :]
_A : Any = inputs_dict["""head_mask"""]
_A : Any = 1
# first forward pass
_A : int = model(_a , attention_mask=_a , head_mask=_a , use_cache=_a )
_A , _A : Union[str, Any] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_A : List[str] = ids_tensor((self.batch_size, 3) , config.vocab_size )
_A : Optional[Any] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
_A : Dict = tf.concat([input_ids, next_tokens] , axis=-1 )
_A : int = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
_A : Any = model(_a , attention_mask=_a )[0]
_A : List[Any] = model(_a , attention_mask=_a , past_key_values=_a )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
_A : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
_A : Optional[int] = output_from_no_past[:, -3:, random_slice_idx]
_A : int = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_a , _a , rtol=1e-3 )
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_=None,snake_case_=None,snake_case_=None,snake_case_=None,snake_case_=None,):
if attention_mask is None:
_A : List[str] = tf.cast(tf.math.not_equal(snake_case_,config.pad_token_id ),tf.inta )
if decoder_attention_mask is None:
_A : int = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape,dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],config.pad_token_id ),tf.inta ),
],axis=-1,)
if head_mask is None:
_A : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_A : Dict = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_A : Optional[int] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class lowercase ( UpperCamelCase__,UpperCamelCase__,unittest.TestCase ):
_a = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
_a = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
_a = (
{
"conversational": TFPegasusForConditionalGeneration,
"feature-extraction": TFPegasusModel,
"summarization": TFPegasusForConditionalGeneration,
"text2text-generation": TFPegasusForConditionalGeneration,
"translation": TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
_a = True
_a = False
_a = False
def a__ ( self ) -> Optional[Any]:
_A : Tuple = TFPegasusModelTester(self )
_A : List[Any] = ConfigTester(self , config_class=_a )
def a__ ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def a__ ( self ) -> Any:
_A : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
@require_sentencepiece
@require_tokenizers
@require_tf
class lowercase ( unittest.TestCase ):
_a = [
" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
]
_a = [
"California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
" reduce the risk of wildfires.",
"N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
] # differs slightly from pytorch, likely due to numerical differences in linear layers
_a = "google/pegasus-xsum"
@cached_property
def a__ ( self ) -> List[Any]:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def a__ ( self ) -> List[Any]:
_A : str = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def a__ ( self , **_a ) -> Tuple:
_A : Dict = self.translate_src_text(**_a )
assert self.expected_text == generated_words
def a__ ( self , **_a ) -> Any:
_A : str = self.tokenizer(self.src_text , **_a , padding=_a , return_tensors="""tf""" )
_A : Tuple = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=_a , )
_A : List[str] = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=_a )
return generated_words
@slow
def a__ ( self ) -> Optional[int]:
self._assert_generated_batch_equal_expected()
| 26
|
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
lowerCamelCase : List[str] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( lowercase : Tuple , lowercase : Optional[int] , lowercase : Optional[int] ):
'''simple docstring'''
return [
int(10_00 * (box[0] / width) ),
int(10_00 * (box[1] / height) ),
int(10_00 * (box[2] / width) ),
int(10_00 * (box[3] / height) ),
]
def _SCREAMING_SNAKE_CASE ( lowercase : np.ndarray , lowercase : Optional[str] , lowercase : Optional[str] ):
'''simple docstring'''
lowerCamelCase_ = to_pil_image(lowercase )
lowerCamelCase_ , lowerCamelCase_ = pil_image.size
lowerCamelCase_ = pytesseract.image_to_data(lowercase , lang=lowercase , output_type='dict' , config=lowercase )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = data['text'], data['left'], data['top'], data['width'], data['height']
# filter empty words and corresponding coordinates
lowerCamelCase_ = [idx for idx, word in enumerate(lowercase ) if not word.strip()]
lowerCamelCase_ = [word for idx, word in enumerate(lowercase ) if idx not in irrelevant_indices]
lowerCamelCase_ = [coord for idx, coord in enumerate(lowercase ) if idx not in irrelevant_indices]
lowerCamelCase_ = [coord for idx, coord in enumerate(lowercase ) if idx not in irrelevant_indices]
lowerCamelCase_ = [coord for idx, coord in enumerate(lowercase ) if idx not in irrelevant_indices]
lowerCamelCase_ = [coord for idx, coord in enumerate(lowercase ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowerCamelCase_ = []
for x, y, w, h in zip(lowercase , lowercase , lowercase , lowercase ):
lowerCamelCase_ = [x, y, x + w, y + h]
actual_boxes.append(lowercase )
# finally, normalize the bounding boxes
lowerCamelCase_ = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(lowercase , lowercase , lowercase ) )
assert len(lowercase ) == len(lowercase ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = ['''pixel_values''']
def __init__( self : int , A_ : bool = True , A_ : Dict[str, int] = None , A_ : PILImageResampling = PILImageResampling.BILINEAR , A_ : bool = True , A_ : float = 1 / 255 , A_ : bool = True , A_ : Union[float, Iterable[float]] = None , A_ : Union[float, Iterable[float]] = None , A_ : bool = True , A_ : Optional[str] = None , A_ : Optional[str] = "" , **A_ : Optional[int] , ) -> None:
"""simple docstring"""
super().__init__(**A_ )
lowerCamelCase_ = size if size is not None else {'height': 224, 'width': 224}
lowerCamelCase_ = get_size_dict(A_ )
lowerCamelCase_ = do_resize
lowerCamelCase_ = size
lowerCamelCase_ = resample
lowerCamelCase_ = do_rescale
lowerCamelCase_ = rescale_value
lowerCamelCase_ = do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCamelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
lowerCamelCase_ = apply_ocr
lowerCamelCase_ = ocr_lang
lowerCamelCase_ = tesseract_config
def a__ ( self : str , A_ : np.ndarray , A_ : Dict[str, int] , A_ : PILImageResampling = PILImageResampling.BILINEAR , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : str , ) -> np.ndarray:
"""simple docstring"""
lowerCamelCase_ = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
lowerCamelCase_ = (size['height'], size['width'])
return resize(A_ , size=A_ , resample=A_ , data_format=A_ , **A_ )
def a__ ( self : Any , A_ : np.ndarray , A_ : Union[int, float] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Optional[Any] , ) -> np.ndarray:
"""simple docstring"""
return rescale(A_ , scale=A_ , data_format=A_ , **A_ )
def a__ ( self : Union[str, Any] , A_ : np.ndarray , A_ : Union[float, Iterable[float]] , A_ : Union[float, Iterable[float]] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : int , ) -> np.ndarray:
"""simple docstring"""
return normalize(A_ , mean=A_ , std=A_ , data_format=A_ , **A_ )
def a__ ( self : List[Any] , A_ : ImageInput , A_ : bool = None , A_ : Dict[str, int] = None , A_ : Dict=None , A_ : bool = None , A_ : float = None , A_ : bool = None , A_ : Union[float, Iterable[float]] = None , A_ : Union[float, Iterable[float]] = None , A_ : bool = None , A_ : Optional[str] = None , A_ : Optional[str] = None , A_ : Optional[Union[str, TensorType]] = None , A_ : ChannelDimension = ChannelDimension.FIRST , **A_ : Any , ) -> PIL.Image.Image:
"""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(A_ )
lowerCamelCase_ = resample if resample is not None else self.resample
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_ = apply_ocr if apply_ocr is not None else self.apply_ocr
lowerCamelCase_ = ocr_lang if ocr_lang is not None else self.ocr_lang
lowerCamelCase_ = tesseract_config if tesseract_config is not None else self.tesseract_config
lowerCamelCase_ = make_list_of_images(A_ )
if not valid_images(A_ ):
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_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('If do_normalize is True, image_mean and image_std must be specified.' )
# All transformations expect numpy arrays.
lowerCamelCase_ = [to_numpy_array(A_ ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , 'pytesseract' )
lowerCamelCase_ = []
lowerCamelCase_ = []
for image in images:
lowerCamelCase_ , lowerCamelCase_ = apply_tesseract(A_ , A_ , A_ )
words_batch.append(A_ )
boxes_batch.append(A_ )
if do_resize:
lowerCamelCase_ = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images]
if do_rescale:
lowerCamelCase_ = [self.rescale(image=A_ , scale=A_ ) for image in images]
if do_normalize:
lowerCamelCase_ = [self.normalize(image=A_ , mean=A_ , std=A_ ) for image in images]
lowerCamelCase_ = [to_channel_dimension_format(A_ , A_ ) for image in images]
lowerCamelCase_ = BatchFeature(data={'pixel_values': images} , tensor_type=A_ )
if apply_ocr:
lowerCamelCase_ = words_batch
lowerCamelCase_ = boxes_batch
return data
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|
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
SCREAMING_SNAKE_CASE : List[str] = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
'''simple docstring'''
lowercase : int =10000
lowercase : Optional[List[str]] =None
lowercase : Optional[datasets.Features] =None
class UpperCamelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
lowercase : Any =ParquetConfig
def UpperCamelCase ( self ):
return datasets.DatasetInfo(features=self.config.features )
def UpperCamelCase ( self , UpperCamelCase_ ):
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}" )
lowercase_ :Any = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase_ , (str, list, tuple) ):
lowercase_ :int = data_files
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
lowercase_ :Any = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowercase_ :int = [dl_manager.iter_files(UpperCamelCase_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
lowercase_ :Optional[int] = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
lowercase_ :Dict = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowercase_ :List[str] = [dl_manager.iter_files(UpperCamelCase_ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(UpperCamelCase_ ):
with open(UpperCamelCase_ , '''rb''' ) as f:
lowercase_ :Any = datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase_ ) )
break
splits.append(datasets.SplitGenerator(name=UpperCamelCase_ , gen_kwargs={'''files''': files} ) )
return splits
def UpperCamelCase ( self , UpperCamelCase_ ):
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
lowercase_ :List[Any] = table_cast(UpperCamelCase_ , self.info.features.arrow_schema )
return pa_table
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :Dict = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'" )
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_ ) ):
with open(UpperCamelCase_ , '''rb''' ) as f:
lowercase_ :Optional[int] = pq.ParquetFile(UpperCamelCase_ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
lowercase_ :str = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f"{file_idx}_{batch_idx}", self._cast_table(UpperCamelCase_ )
except ValueError as e:
logger.error(f"Failed to read file '{file}' with error {type(UpperCamelCase_ )}: {e}" )
raise
| 365
|
from __future__ import annotations
from random import random
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ = None ):
lowercase_ :Tuple = value
lowercase_ :Tuple = random()
lowercase_ :Node | None = None
lowercase_ :Node | None = None
def __repr__( self ):
from pprint import pformat
if self.left is None and self.right is None:
return f"'{self.value}: {self.prior:.5}'"
else:
return pformat(
{f"{self.value}: {self.prior:.5}": (self.left, self.right)} , indent=1 )
def __str__( self ):
lowercase_ :Optional[int] = str(self.value ) + ''' '''
lowercase_ :List[str] = str(self.left or '''''' )
lowercase_ :List[Any] = str(self.right or '''''' )
return value + left + right
def UpperCamelCase ( _a , _a ) -> tuple[Node | None, Node | None]:
'''simple docstring'''
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
lowercase_ , lowercase_ :List[Any] = split(root.left , _a )
return left, root
else:
lowercase_ , lowercase_ :Tuple = split(root.right , _a )
return root, right
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
lowercase_ :Tuple = merge(left.right , _a )
return left
else:
lowercase_ :Optional[int] = merge(_a , right.left )
return right
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
lowercase_ :str = Node(_a )
lowercase_ , lowercase_ :Dict = split(_a , _a )
return merge(merge(_a , _a ) , _a )
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
lowercase_ , lowercase_ :List[str] = split(_a , value - 1 )
lowercase_ , lowercase_ :Tuple = split(_a , _a )
return merge(_a , _a )
def UpperCamelCase ( _a ) -> None:
'''simple docstring'''
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=''',''' )
inorder(root.right )
def UpperCamelCase ( _a , _a ) -> Node | None:
'''simple docstring'''
for arg in args.split():
if arg[0] == "+":
lowercase_ :Any = insert(_a , int(arg[1:] ) )
elif arg[0] == "-":
lowercase_ :Optional[int] = erase(_a , int(arg[1:] ) )
else:
print('''Unknown command''' )
return root
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase_ :List[Any] = None
print(
'''enter numbers to create a tree, + value to add value into treap, '''
'''- value to erase all nodes with value. \'q\' to quit. ''' )
lowercase_ :Optional[Any] = input()
while args != "q":
lowercase_ :Union[str, Any] = interact_treap(_a , _a )
print(_a )
lowercase_ :str = input()
print('''good by!''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 252
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|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertConfig,
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
class _UpperCAmelCase :
def __init__( self : List[str] , A : Any , ) -> Dict:
lowercase_ : Tuple = parent
lowercase_ : str = 13
lowercase_ : Optional[Any] = 7
lowercase_ : Any = True
lowercase_ : str = True
lowercase_ : List[str] = True
lowercase_ : int = True
lowercase_ : Dict = True
lowercase_ : int = False
lowercase_ : Dict = False
lowercase_ : Union[str, Any] = False
lowercase_ : List[Any] = 2
lowercase_ : Optional[int] = 99
lowercase_ : List[Any] = 0
lowercase_ : Dict = 32
lowercase_ : List[Any] = 2
lowercase_ : Tuple = 4
lowercase_ : List[Any] = 0.1
lowercase_ : List[Any] = 0.1
lowercase_ : Optional[Any] = 5_12
lowercase_ : Optional[Any] = 16
lowercase_ : List[str] = 2
lowercase_ : str = 0.02
lowercase_ : Any = 3
lowercase_ : List[str] = 4
lowercase_ : Dict = '''last'''
lowercase_ : int = True
lowercase_ : str = None
lowercase_ : Dict = 0
def A ( self : List[str] ) -> List[Any]:
lowercase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] , dtype=tf.floataa )
lowercase_ : Any = None
if self.use_input_lengths:
lowercase_ : Optional[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowercase_ : List[Any] = None
if self.use_token_type_ids:
lowercase_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowercase_ : Optional[int] = None
lowercase_ : str = None
lowercase_ : Optional[Any] = None
if self.use_labels:
lowercase_ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase_ : Optional[int] = ids_tensor([self.batch_size] , 2 , dtype=tf.floataa )
lowercase_ : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
lowercase_ : Any = FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , bos_token_id=self.bos_token_id , )
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def A ( self : int , A : Dict , A : Dict , A : str , A : Tuple , A : Optional[Any] , A : str , A : Union[str, Any] , A : Dict , A : Optional[int] , ) -> int:
lowercase_ : str = TFFlaubertModel(config=A )
lowercase_ : int = {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
lowercase_ : int = model(A )
lowercase_ : Any = [input_ids, input_mask]
lowercase_ : Optional[int] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Optional[Any] , A : int , A : List[str] , A : Dict , A : int , A : int , A : Dict , A : Optional[int] , A : Dict , A : int , ) -> Optional[Any]:
lowercase_ : int = TFFlaubertWithLMHeadModel(A )
lowercase_ : List[Any] = {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
lowercase_ : List[str] = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[str] , A : Optional[int] , A : Tuple , A : Dict , A : List[Any] , A : Dict , A : Any , A : List[Any] , A : List[Any] , A : str , ) -> Union[str, Any]:
lowercase_ : List[Any] = TFFlaubertForQuestionAnsweringSimple(A )
lowercase_ : Tuple = {'''input_ids''': input_ids, '''lengths''': input_lengths}
lowercase_ : List[str] = model(A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : int , A : Optional[Any] , A : int , A : List[str] , A : Optional[Any] , A : Tuple , A : Dict , A : Any , A : Any , A : str , ) -> Optional[int]:
lowercase_ : str = TFFlaubertForSequenceClassification(A )
lowercase_ : Any = {'''input_ids''': input_ids, '''lengths''': input_lengths}
lowercase_ : Union[str, Any] = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A ( self : List[Any] , A : Tuple , A : int , A : Dict , A : Any , A : int , A : Optional[int] , A : str , A : str , A : int , ) -> Optional[int]:
lowercase_ : Optional[Any] = self.num_labels
lowercase_ : List[Any] = TFFlaubertForTokenClassification(config=A )
lowercase_ : int = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowercase_ : int = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : List[str] , A : str , A : Any , A : int , A : Dict , A : Tuple , A : List[Any] , A : Optional[Any] , A : List[Any] , A : Optional[int] , ) -> Union[str, Any]:
lowercase_ : Union[str, Any] = self.num_choices
lowercase_ : Union[str, Any] = TFFlaubertForMultipleChoice(config=A )
lowercase_ : Union[str, Any] = tf.tile(tf.expand_dims(A , 1 ) , (1, self.num_choices, 1) )
lowercase_ : Any = tf.tile(tf.expand_dims(A , 1 ) , (1, self.num_choices, 1) )
lowercase_ : Dict = tf.tile(tf.expand_dims(A , 1 ) , (1, self.num_choices, 1) )
lowercase_ : Any = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowercase_ : Any = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : Optional[int] ) -> str:
lowercase_ : List[Any] = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) : Dict = config_and_inputs
lowercase_ : Optional[int] = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''langs''': token_type_ids,
'''lengths''': input_lengths,
}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE_ : Dict = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
{
"feature-extraction": TFFlaubertModel,
"fill-mask": TFFlaubertWithLMHeadModel,
"question-answering": TFFlaubertForQuestionAnsweringSimple,
"text-classification": TFFlaubertForSequenceClassification,
"token-classification": TFFlaubertForTokenClassification,
"zero-shot": TFFlaubertForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
def A ( self : Any , A : Any , A : Union[str, Any] , A : Optional[int] , A : int , A : str ) -> Any:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def A ( self : Any ) -> Optional[int]:
lowercase_ : Dict = TFFlaubertModelTester(self )
lowercase_ : Optional[Any] = ConfigTester(self , config_class=A , emb_dim=37 )
def A ( self : List[str] ) -> Dict:
self.config_tester.run_common_tests()
def A ( self : List[str] ) -> int:
lowercase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*A )
def A ( self : List[str] ) -> int:
lowercase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*A )
def A ( self : List[str] ) -> int:
lowercase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*A )
def A ( self : Any ) -> List[Any]:
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*A )
def A ( self : Optional[int] ) -> str:
lowercase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_token_classification(*A )
def A ( self : Optional[Any] ) -> Tuple:
lowercase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_multiple_choice(*A )
@slow
def A ( self : List[Any] ) -> Optional[Any]:
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : Tuple = TFFlaubertModel.from_pretrained(A )
self.assertIsNotNone(A )
@require_tf
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( unittest.TestCase ):
@slow
def A ( self : str ) -> int:
lowercase_ : Any = TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''' )
lowercase_ : Optional[int] = tf.convert_to_tensor(
[[0, 1_58, 7_35, 25_92, 14_24, 67_27, 82, 1]] , dtype=tf.intaa , ) # "J'aime flaubert !"
lowercase_ : int = model(A )[0]
lowercase_ : str = tf.TensorShape((1, 8, 5_12) )
self.assertEqual(output.shape , A )
# compare the actual values for a slice.
lowercase_ : int = tf.convert_to_tensor(
[
[
[-1.8768773, -1.566555, 0.27072418],
[-1.6920038, -0.5873505, 1.9329599],
[-2.9563985, -1.6993835, 1.7972052],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 33
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected', [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1}, [range(10 )]),
({'num_shards': 10, 'max_num_jobs': 10}, [range(lowerCAmelCase_, i + 1 ) for i in range(10 )]),
({'num_shards': 1, 'max_num_jobs': 10}, [range(1 )]),
({'num_shards': 10, 'max_num_jobs': 3}, [range(0, 4 ), range(4, 7 ), range(7, 10 )]),
({'num_shards': 3, 'max_num_jobs': 10}, [range(0, 1 ), range(1, 2 ), range(2, 3 )]),
], )
def a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : Any ):
__lowerCAmelCase = _distribute_shards(**lowerCAmelCase_ )
assert out == expected
@pytest.mark.parametrize(
'gen_kwargs, max_num_jobs, expected', [
({'foo': 0}, 10, [{'foo': 0}]),
({'shards': [0, 1, 2, 3]}, 1, [{'shards': [0, 1, 2, 3]}]),
({'shards': [0, 1, 2, 3]}, 4, [{'shards': [0]}, {'shards': [1]}, {'shards': [2]}, {'shards': [3]}]),
({'shards': [0, 1]}, 4, [{'shards': [0]}, {'shards': [1]}]),
({'shards': [0, 1, 2, 3]}, 2, [{'shards': [0, 1]}, {'shards': [2, 3]}]),
], )
def a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : List[str], lowerCAmelCase_ : Optional[int] ):
__lowerCAmelCase = _split_gen_kwargs(lowerCAmelCase_, lowerCAmelCase_ )
assert out == expected
@pytest.mark.parametrize(
'gen_kwargs, expected', [
({'foo': 0}, 1),
({'shards': [0]}, 1),
({'shards': [0, 1, 2, 3]}, 4),
({'shards': [0, 1, 2, 3], 'foo': 0}, 4),
({'shards': [0, 1, 2, 3], 'other': (0, 1)}, 4),
({'shards': [0, 1, 2, 3], 'shards2': [0, 1]}, RuntimeError),
], )
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : Any ):
if expected is RuntimeError:
with pytest.raises(lowerCAmelCase_ ):
_number_of_shards_in_gen_kwargs(lowerCAmelCase_ )
else:
__lowerCAmelCase = _number_of_shards_in_gen_kwargs(lowerCAmelCase_ )
assert out == expected
| 284
| 0
|
"""simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
return 1 if input_a == input_a else 0
def UpperCAmelCase ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 361
|
"""simple docstring"""
from __future__ import annotations
import math
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> int:
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if len(UpperCAmelCase ) == 0:
raise ValueError('Scores cannot be empty' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) , minimax(depth + 1 , node_index * 2 + 1 , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) , )
return min(
minimax(depth + 1 , node_index * 2 , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) , minimax(depth + 1 , node_index * 2 + 1 , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) , )
def UpperCAmelCase ( ) -> None:
snake_case_ = [90, 23, 6, 33, 21, 65, 123, 34423]
snake_case_ = math.log(len(UpperCAmelCase ) , 2 )
print('Optimal value : ' , end='' )
print(minimax(0 , 0 , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 312
| 0
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class A ( _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = FlaxAutoencoderKL
@property
def snake_case__ ( self : Any )-> int:
'''simple docstring'''
A__ = 4
A__ = 3
A__ = (3_2, 3_2)
A__ = jax.random.PRNGKey(0 )
A__ = jax.random.uniform(lowercase_,((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def snake_case__ ( self : Any )-> str:
'''simple docstring'''
A__ = {
'block_out_channels': [3_2, 6_4],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 4,
}
A__ = self.dummy_input
return init_dict, inputs_dict
| 7
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'open-llama'
def __init__( self : Any,lowercase_ : Optional[int]=1_0_0_0_0_0,lowercase_ : Union[str, Any]=4_0_9_6,lowercase_ : Dict=1_1_0_0_8,lowercase_ : Dict=3_2,lowercase_ : Optional[int]=3_2,lowercase_ : Dict="silu",lowercase_ : Union[str, Any]=2_0_4_8,lowercase_ : Optional[int]=0.02,lowercase_ : Dict=1E-6,lowercase_ : Dict=True,lowercase_ : List[Any]=0,lowercase_ : Optional[int]=1,lowercase_ : str=2,lowercase_ : str=False,lowercase_ : str=True,lowercase_ : int=0.1,lowercase_ : List[Any]=0.1,lowercase_ : List[Any]=True,lowercase_ : Union[str, Any]=True,lowercase_ : Any=None,**lowercase_ : List[Any],)-> Tuple:
'''simple docstring'''
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = intermediate_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = hidden_act
A__ = initializer_range
A__ = rms_norm_eps
A__ = use_cache
A__ = kwargs.pop(
'use_memorry_efficient_attention',lowercase_ )
A__ = hidden_dropout_prob
A__ = attention_dropout_prob
A__ = use_stable_embedding
A__ = shared_input_output_embedding
A__ = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,tie_word_embeddings=lowercase_,**lowercase_,)
def snake_case__ ( self : str )-> str:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling,lowercase_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F'got {self.rope_scaling}' )
A__ = self.rope_scaling.get('type',lowercase_ )
A__ = self.rope_scaling.get('factor',lowercase_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(lowercase_,lowercase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 7
| 1
|
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class SCREAMING_SNAKE_CASE__ :
A_ : torch.Tensor # [batch_size x 3]
A_ : torch.Tensor # [batch_size x 3]
A_ : torch.Tensor # [batch_size x 3]
A_ : torch.Tensor # [batch_size x 3]
A_ : int
A_ : int
A_ : float
A_ : float
A_ : Tuple[int]
def a (self : List[str] ):
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def a (self : Optional[int] ):
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def a (self : Optional[int] ):
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def a (self : List[Any] ):
"""simple docstring"""
__snake_case = torch.arange(self.height * self.width )
__snake_case = torch.stack(
[
pixel_indices % self.width,
torch.div(a__ , self.width , rounding_mode='''trunc''' ),
] , axis=1 , )
return coords
@property
def a (self : Optional[int] ):
"""simple docstring"""
__snake_case , *__snake_case = self.shape
__snake_case = int(np.prod(a__ ) )
__snake_case = self.get_image_coords()
__snake_case = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
__snake_case = self.get_camera_rays(a__ )
__snake_case = rays.view(a__ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def a (self : Optional[int] , a__ : torch.Tensor ):
"""simple docstring"""
__snake_case , *__snake_case , __snake_case = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
__snake_case = coords.view(a__ , -1 , 2 )
__snake_case = self.resolution()
__snake_case = self.fov()
__snake_case = (flat.float() / (res - 1)) * 2 - 1
__snake_case = fracs * torch.tan(fov / 2 )
__snake_case = fracs.view(a__ , -1 , 2 )
__snake_case = (
self.z.view(a__ , 1 , 3 )
+ self.x.view(a__ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(a__ , 1 , 3 ) * fracs[:, :, 1:]
)
__snake_case = directions / directions.norm(dim=-1 , keepdim=a__ )
__snake_case = torch.stack(
[
torch.broadcast_to(self.origin.view(a__ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(a__ , *a__ , 2 , 3 )
def a (self : Any , a__ : int , a__ : int ):
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=a__ , height=a__ , x_fov=self.x_fov , y_fov=self.y_fov , )
def lowerCamelCase__ ( snake_case_ : int ) -> DifferentiableProjectiveCamera:
__snake_case = []
__snake_case = []
__snake_case = []
__snake_case = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
__snake_case = np.array([np.sin(snake_case_ ), np.cos(snake_case_ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
__snake_case = -z * 4
__snake_case = np.array([np.cos(snake_case_ ), -np.sin(snake_case_ ), 0.0] )
__snake_case = np.cross(snake_case_ , snake_case_ )
origins.append(snake_case_ )
xs.append(snake_case_ )
ys.append(snake_case_ )
zs.append(snake_case_ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(snake_case_ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(snake_case_ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(snake_case_ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(snake_case_ , axis=0 ) ).float() , width=snake_case_ , height=snake_case_ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(snake_case_ )) , )
| 361
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case_ = {
'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ['VivitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'VivitModel',
'VivitPreTrainedModel',
'VivitForVideoClassification',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 238
| 0
|
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a (UpperCamelCase__ ):
"""simple docstring"""
def __init__( self : Any , lowerCamelCase : VQModel , lowerCamelCase : UNetaDModel , lowerCamelCase : DDIMScheduler ) -> str:
super().__init__()
self.register_modules(vqvae=__lowerCamelCase , unet=__lowerCamelCase , scheduler=__lowerCamelCase )
@torch.no_grad()
def __call__( self : Optional[Any] , lowerCamelCase : int = 1 , lowerCamelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase : float = 0.0 , lowerCamelCase : int = 50 , lowerCamelCase : Optional[str] = "pil" , lowerCamelCase : bool = True , **lowerCamelCase : Dict , ) -> List[Any]:
__snake_case : str = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=__lowerCamelCase , )
__snake_case : List[str] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__snake_case : Optional[Any] = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(__lowerCamelCase )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
__snake_case : Dict = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__snake_case : int = {}
if accepts_eta:
__snake_case : List[str] = eta
for t in self.progress_bar(self.scheduler.timesteps ):
__snake_case : List[Any] = self.scheduler.scale_model_input(__lowerCamelCase , __lowerCamelCase )
# predict the noise residual
__snake_case : Union[str, Any] = self.unet(__lowerCamelCase , __lowerCamelCase ).sample
# compute the previous noisy sample x_t -> x_t-1
__snake_case : str = self.scheduler.step(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase ).prev_sample
# decode the image latents with the VAE
__snake_case : Any = self.vqvae.decode(__lowerCamelCase ).sample
__snake_case : Tuple = (image / 2 + 0.5).clamp(0 , 1 )
__snake_case : Any = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__snake_case : List[Any] = self.numpy_to_pil(__lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__lowerCamelCase )
| 123
|
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCAmelCase_ ( A_ ,A_ ,A_):
UpperCamelCase__: Dict = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
UpperCamelCase__: Dict = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(A_):
os.makedirs(A_)
UpperCamelCase__: Optional[Any] = model.state_dict()
def to_tf_var_name(A_):
for patt, repl in iter(A_):
UpperCamelCase__: Optional[Any] = name.replace(A_ ,A_)
return F"bert/{name}"
def create_tf_var(A_ ,A_ ,A_):
UpperCamelCase__: Any = tf.dtypes.as_dtype(tensor.dtype)
UpperCamelCase__: int = tf.get_variable(dtype=A_ ,shape=tensor.shape ,name=A_ ,initializer=tf.zeros_initializer())
session.run(tf.variables_initializer([tf_var]))
session.run(A_)
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
UpperCamelCase__: List[Any] = to_tf_var_name(A_)
UpperCamelCase__: List[str] = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose):
UpperCamelCase__: List[Any] = torch_tensor.T
UpperCamelCase__: int = create_tf_var(tensor=A_ ,name=A_ ,session=A_)
tf.keras.backend.set_value(A_ ,A_)
UpperCamelCase__: Optional[Any] = session.run(A_)
print(F"Successfully created {tf_name}: {np.allclose(A_ ,A_)}")
UpperCamelCase__: Tuple = tf.train.Saver(tf.trainable_variables())
saver.save(A_ ,os.path.join(A_ ,model_name.replace("-" ,"_") + ".ckpt"))
def lowerCAmelCase_ ( A_=None):
UpperCamelCase__: Tuple = argparse.ArgumentParser()
parser.add_argument("--model_name" ,type=A_ ,required=A_ ,help="model name e.g. bert-base-uncased")
parser.add_argument(
"--cache_dir" ,type=A_ ,default=A_ ,required=A_ ,help="Directory containing pytorch model")
parser.add_argument("--pytorch_model_path" ,type=A_ ,required=A_ ,help="/path/to/<pytorch-model-name>.bin")
parser.add_argument("--tf_cache_dir" ,type=A_ ,required=A_ ,help="Directory in which to save tensorflow model")
UpperCamelCase__: Any = parser.parse_args(A_)
UpperCamelCase__: List[Any] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name ,state_dict=torch.load(args.pytorch_model_path) ,cache_dir=args.cache_dir ,)
convert_pytorch_checkpoint_to_tf(model=A_ ,ckpt_dir=args.tf_cache_dir ,model_name=args.model_name)
if __name__ == "__main__":
main()
| 149
| 0
|
"""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 ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str]=3 , lowerCAmelCase_ : List[str]=3_2 , lowerCAmelCase_ : Union[str, Any]=3 , lowerCAmelCase_ : int=1_0 , lowerCAmelCase_ : Tuple=[1_0, 2_0, 3_0, 4_0] , lowerCAmelCase_ : Optional[Any]=[1, 1, 2, 1] , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[Any]="relu" , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : List[Any]=None , ):
"""simple docstring"""
_A: str = parent
_A: List[Any] = batch_size
_A: Optional[int] = image_size
_A: Dict = num_channels
_A: str = embeddings_size
_A: Any = hidden_sizes
_A: Dict = depths
_A: Any = is_training
_A: int = use_labels
_A: Tuple = hidden_act
_A: int = num_labels
_A: int = scope
_A: str = len(lowerCAmelCase_ )
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A: Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A: Union[str, Any] = self.get_config()
return config, pixel_values
def __magic_name__ ( self : str ):
"""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 __magic_name__ ( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str ):
"""simple docstring"""
_A: str = FlaxRegNetModel(config=lowerCAmelCase_ )
_A: Optional[int] = 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 // 3_2, self.image_size // 3_2) , )
def __magic_name__ ( self : str , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Union[str, Any] = self.num_labels
_A: Union[str, Any] = FlaxRegNetForImageClassification(config=lowerCAmelCase_ )
_A: str = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
_A: str = self.prepare_config_and_inputs()
_A: Optional[int] = config_and_inputs
_A: Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Tuple = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
__UpperCamelCase : Union[str, Any] = False
__UpperCamelCase : List[Any] = False
__UpperCamelCase : int = False
def __magic_name__ ( self : int ):
"""simple docstring"""
_A: int = FlaxRegNetModelTester(self )
_A: Union[str, Any] = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ )
def __magic_name__ ( self : str ):
"""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 __magic_name__ ( self : int ):
"""simple docstring"""
return
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
_A: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
_A: Optional[int] = 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 __magic_name__ ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def __magic_name__ ( self : Optional[int] ):
"""simple docstring"""
pass
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
_A: int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A: Union[str, Any] = model_class(lowerCAmelCase_ )
_A: Any = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A: Any = [*signature.parameters.keys()]
_A: Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def __magic_name__ ( self : str ):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple ):
_A: int = model_class(lowerCAmelCase_ )
_A: List[str] = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_A: str = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_A: Tuple = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase_ ) , expected_num_stages + 1 )
_A: List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A: Optional[Any] = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_A: int = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A: str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_A: int = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ )
_A: Union[str, Any] = model_class(lowerCAmelCase_ )
@jax.jit
def model_jitted(lowerCAmelCase_ : Optional[Any] , **lowerCAmelCase_ : Optional[Any] ):
return model(pixel_values=lowerCAmelCase_ , **lowerCAmelCase_ )
with self.subTest('''JIT Enabled''' ):
_A: str = model_jitted(**lowerCAmelCase_ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
_A: List[Any] = 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 lowerCamelCase__ ( ) -> Tuple:
_A: List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __magic_name__ ( self : Union[str, Any] ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
_A: List[str] = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
_A: str = self.default_image_processor
_A: int = prepare_img()
_A: List[Any] = image_processor(images=lowerCAmelCase_ , return_tensors='''np''' )
_A: str = model(**lowerCAmelCase_ )
# verify the logits
_A: str = (1, 1_0_0_0)
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
_A: Tuple = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1e-4 ) )
| 352
|
from __future__ import annotations
UpperCAmelCase__ : List[str] = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ : 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
UpperCAmelCase__ : 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 lowerCamelCase__ ( a , a , a , a ) -> bool:
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 lowerCamelCase__ ( a ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def lowerCamelCase__ ( a ) -> Matrix | None:
if location := find_empty_location(a ):
_A , _A: Optional[Any] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(a , a , a , a ):
_A: str = digit
if sudoku(a ) is not None:
return grid
_A: Tuple = 0
return None
def lowerCamelCase__ ( a ) -> None:
for row in grid:
for cell in row:
print(a , 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:')
UpperCAmelCase__ : int = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('Cannot find a solution.')
| 301
| 0
|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
lowercase : List[Any] = {
"""vocab_file""": {
"""allegro/herbert-base-cased""": """https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"""
},
"""merges_file""": {
"""allegro/herbert-base-cased""": """https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"""
},
}
lowercase : Any = {"""allegro/herbert-base-cased""": 5_1_4}
lowercase : str = {}
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : Any = VOCAB_FILES_NAMES
__A : str = PRETRAINED_VOCAB_FILES_MAP
__A : Optional[int] = PRETRAINED_INIT_CONFIGURATION
__A : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : Tuple = HerbertTokenizer
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , lowercase="</s>" , **lowercase , ) -> List[Any]:
'''simple docstring'''
super().__init__(
lowercase , lowercase , tokenizer_file=lowercase , cls_token=lowercase , unk_token=lowercase , pad_token=lowercase , mask_token=lowercase , sep_token=lowercase , **lowercase , )
def __lowercase ( self , lowercase , lowercase = None) -> List[int]:
'''simple docstring'''
a__ : str = [self.cls_token_id]
a__ : Tuple = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __lowercase ( self , lowercase , lowercase = None , lowercase = False) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase)
if token_ids_a is None:
return [1] + ([0] * len(lowercase)) + [1]
return [1] + ([0] * len(lowercase)) + [1] + ([0] * len(lowercase)) + [1]
def __lowercase ( self , lowercase , lowercase = None) -> List[int]:
'''simple docstring'''
a__ : Optional[int] = [self.sep_token_id]
a__ : Union[str, Any] = [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) * [0] + len(token_ids_a + sep) * [1]
def __lowercase ( self , lowercase , lowercase = None) -> Tuple[str]:
'''simple docstring'''
a__ : Any = self._tokenizer.model.save(lowercase , name=lowercase)
return tuple(lowercase)
| 99
|
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339
| 0
|
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Tuple , lowerCamelCase_ :Tuple , lowerCamelCase_ :Tuple ):
'''simple docstring'''
snake_case_ : Optional[int] = TaConfig.from_json_file(_lowerCAmelCase )
print(F'''Building PyTorch model from configuration: {config}''' )
snake_case_ : Optional[int] = TaForConditionalGeneration(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_ta(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_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 T5 model. \nThis specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__A : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 353
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
lowercase : str = ['input_values', 'padding_mask']
def __init__( self :Optional[int] ,_UpperCamelCase :int = 1 ,_UpperCamelCase :int = 2_4_0_0_0 ,_UpperCamelCase :float = 0.0 ,_UpperCamelCase :float = None ,_UpperCamelCase :float = None ,**_UpperCamelCase :List[Any] ,):
super().__init__(feature_size=_UpperCamelCase ,sampling_rate=_UpperCamelCase ,padding_value=_UpperCamelCase ,**_UpperCamelCase )
snake_case_ : Dict = chunk_length_s
snake_case_ : str = overlap
@property
def a__ ( self :Any ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def a__ ( self :List[str] ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 ,int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self :Optional[Any] ,_UpperCamelCase :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,_UpperCamelCase :Optional[Union[bool, str, PaddingStrategy]] = None ,_UpperCamelCase :Optional[bool] = False ,_UpperCamelCase :Optional[int] = None ,_UpperCamelCase :Optional[Union[str, TensorType]] = None ,_UpperCamelCase :Optional[int] = None ,):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
snake_case_ : Tuple = True
snake_case_ : str = bool(
isinstance(_UpperCamelCase ,(list, tuple) ) and (isinstance(raw_audio[0] ,(np.ndarray, tuple, list) )) )
if is_batched:
snake_case_ : Any = [np.asarray(_UpperCamelCase ,dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_UpperCamelCase ,np.ndarray ):
snake_case_ : Optional[int] = np.asarray(_UpperCamelCase ,dtype=np.floataa )
elif isinstance(_UpperCamelCase ,np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
snake_case_ : List[str] = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
snake_case_ : Optional[Any] = [np.asarray(_UpperCamelCase ).T]
# verify inputs are valid
for idx, example in enumerate(_UpperCamelCase ):
if example.ndim > 2:
raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}''' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels''' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels''' )
snake_case_ : Tuple = None
snake_case_ : Optional[Any] = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
snake_case_ : Union[str, Any] = min(array.shape[0] for array in raw_audio )
snake_case_ : Dict = int(np.floor(max_length / self.chunk_stride ) )
snake_case_ : Union[str, Any] = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
snake_case_ : Any = max(array.shape[0] for array in raw_audio )
snake_case_ : List[Any] = int(np.ceil(max_length / self.chunk_stride ) )
snake_case_ : Any = (nb_step - 1) * self.chunk_stride + self.chunk_length
snake_case_ : Union[str, Any] = """max_length"""
else:
snake_case_ : int = input_values
# normal padding on batch
if padded_inputs is None:
snake_case_ : Optional[int] = self.pad(
_UpperCamelCase ,max_length=_UpperCamelCase ,truncation=_UpperCamelCase ,padding=_UpperCamelCase ,return_attention_mask=_UpperCamelCase ,)
if padding:
snake_case_ : Tuple = padded_inputs.pop("""attention_mask""" )
snake_case_ : Optional[int] = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
snake_case_ : Dict = example[..., None]
input_values.append(example.T )
snake_case_ : List[Any] = input_values
if return_tensors is not None:
snake_case_ : Tuple = padded_inputs.convert_to_tensors(_UpperCamelCase )
return padded_inputs
| 8
| 0
|
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a : Tuple = logging.get_logger(__name__)
__a : Union[str, Any] = {
"nielsr/canine-s": 2_0_4_8,
}
# Unicode defines 1,114,112 total “codepoints”
__a : Optional[int] = 1_1_1_4_1_1_2
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__a : List[str] = 0
__a : int = 0Xe_0_0_0
__a : Any = 0Xe_0_0_1
__a : List[Any] = 0Xe_0_0_2
__a : Union[str, Any] = 0Xe_0_0_3
__a : List[str] = 0Xe_0_0_4
# Maps special codepoints to human-readable names.
__a : Dict[int, str] = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
__a : Dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
__a : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , lowerCAmelCase__=chr(lowerCAmelCase__ ) , lowerCAmelCase__=chr(lowerCAmelCase__ ) , lowerCAmelCase__=chr(lowerCAmelCase__ ) , lowerCAmelCase__=chr(lowerCAmelCase__ ) , lowerCAmelCase__=chr(lowerCAmelCase__ ) , lowerCAmelCase__=chr(lowerCAmelCase__ ) , lowerCAmelCase__=False , lowerCAmelCase__=20_48 , **lowerCAmelCase__ , ) -> Optional[int]:
'''simple docstring'''
__lowercase = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else bos_token
__lowercase = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else eos_token
__lowercase = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else sep_token
__lowercase = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else cls_token
__lowercase = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowercase = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
super().__init__(
bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , model_max_length=lowerCAmelCase__ , **lowerCAmelCase__ , )
# Creates a mapping for looking up the IDs of special symbols.
__lowercase = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
__lowercase = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
__lowercase = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
__lowercase = UNICODE_VOCAB_SIZE
__lowercase = len(self._special_codepoints )
@property
def _SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
return self._unicode_vocab_size
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> str:
'''simple docstring'''
return list(lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> int:
'''simple docstring'''
try:
return ord(lowerCAmelCase__ )
except TypeError:
raise ValueError(F"invalid token: \'{token}\'" )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(lowerCAmelCase__ )
except TypeError:
raise ValueError(F"invalid id: {index}" )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
return "".join(lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Optional[Any]:
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False ) -> Optional[int]:
'''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__ )
__lowercase = [1] + ([0] * len(lowerCAmelCase__ )) + [1]
if token_ids_a is not None:
result += ([0] * len(lowerCAmelCase__ )) + [1]
return result
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> int:
'''simple docstring'''
return ()
| 210
|
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ):
a = pre_numerator
a = 2 * i // 3 if i % 3 == 0 else 1
a = cur_numerator
a = e_cont * pre_numerator + temp
return sum_digits(__lowerCamelCase )
if __name__ == "__main__":
print(F'{solution() = }')
| 228
| 0
|
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
UpperCamelCase__ = Mapping[str, np.ndarray]
UpperCamelCase__ = Mapping[str, Any] # Is a nested dict.
UpperCamelCase__ = 0.0_1
@dataclasses.dataclass(frozen=_a )
class A :
__UpperCAmelCase : List[str] = 42 # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
__UpperCAmelCase : List[Any] = 42 # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
__UpperCAmelCase : List[str] = 42 # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
__UpperCAmelCase : List[Any] = 42 # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
__UpperCAmelCase : Any = 42 # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
__UpperCAmelCase : Optional[Any] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
__UpperCAmelCase : int = None
# Templates used to generate this protein (prediction-only)
__UpperCAmelCase : Optional[Any] = None
# Chain corresponding to each parent
__UpperCAmelCase : Tuple = None
def lowerCAmelCase_ ( __A ) -> Protein:
'''simple docstring'''
UpperCAmelCase__ = r"""(\[[A-Z]+\]\n)"""
UpperCAmelCase__ = [tag.strip() for tag in re.split(_lowerCAmelCase, _lowerCAmelCase ) if len(_lowerCAmelCase ) > 0]
UpperCAmelCase__ = zip(tags[0::2], [l.split("\n" ) for l in tags[1::2]] )
UpperCAmelCase__ = ["N", "CA", "C"]
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
for g in groups:
if "[PRIMARY]" == g[0]:
UpperCAmelCase__ = g[1][0].strip()
for i in range(len(_lowerCAmelCase ) ):
if seq[i] not in residue_constants.restypes:
UpperCAmelCase__ = """X""" # FIXME: strings are immutable
UpperCAmelCase__ = np.array(
[residue_constants.restype_order.get(_lowerCAmelCase, residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
UpperCAmelCase__ = []
for axis in range(3 ):
tertiary.append(list(map(_lowerCAmelCase, g[1][axis].split() ) ) )
UpperCAmelCase__ = np.array(_lowerCAmelCase )
UpperCAmelCase__ = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_lowerCAmelCase ):
UpperCAmelCase__ = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
UpperCAmelCase__ = np.array(list(map({"-": 0, "+": 1}.get, g[1][0].strip() ) ) )
UpperCAmelCase__ = np.zeros(
(
len(_lowerCAmelCase ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_lowerCAmelCase ):
UpperCAmelCase__ = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_lowerCAmelCase, atom_mask=_lowerCAmelCase, aatype=_lowerCAmelCase, residue_index=np.arange(len(_lowerCAmelCase ) ), b_factors=_lowerCAmelCase, )
def lowerCAmelCase_ ( __A, __A = 0 ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ = []
UpperCAmelCase__ = prot.remark
if remark is not None:
pdb_headers.append(f"""REMARK {remark}""" )
UpperCAmelCase__ = prot.parents
UpperCAmelCase__ = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
UpperCAmelCase__ = [p for i, p in zip(_lowerCAmelCase, _lowerCAmelCase ) if i == chain_id]
if parents is None or len(_lowerCAmelCase ) == 0:
UpperCAmelCase__ = ["""N/A"""]
pdb_headers.append(f"""PARENT {" ".join(_lowerCAmelCase )}""" )
return pdb_headers
def lowerCAmelCase_ ( __A, __A ) -> str:
'''simple docstring'''
UpperCAmelCase__ = []
UpperCAmelCase__ = pdb_str.split("\n" )
UpperCAmelCase__ = prot.remark
if remark is not None:
out_pdb_lines.append(f"""REMARK {remark}""" )
UpperCAmelCase__ = 42
if prot.parents is not None and len(prot.parents ) > 0:
UpperCAmelCase__ = []
if prot.parents_chain_index is not None:
UpperCAmelCase__ = {}
for p, i in zip(prot.parents, prot.parents_chain_index ):
parent_dict.setdefault(str(_lowerCAmelCase ), [] )
parent_dict[str(_lowerCAmelCase )].append(_lowerCAmelCase )
UpperCAmelCase__ = max([int(_lowerCAmelCase ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
UpperCAmelCase__ = parent_dict.get(str(_lowerCAmelCase ), ["N/A"] )
parents_per_chain.append(_lowerCAmelCase )
else:
parents_per_chain.append(list(prot.parents ) )
else:
UpperCAmelCase__ = [["""N/A"""]]
def make_parent_line(__A ) -> str:
return f"""PARENT {" ".join(_lowerCAmelCase )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
UpperCAmelCase__ = 0
for i, l in enumerate(_lowerCAmelCase ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_lowerCAmelCase )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_lowerCAmelCase ):
UpperCAmelCase__ = parents_per_chain[chain_counter]
else:
UpperCAmelCase__ = ["""N/A"""]
out_pdb_lines.append(make_parent_line(_lowerCAmelCase ) )
return "\n".join(_lowerCAmelCase )
def lowerCAmelCase_ ( __A ) -> str:
'''simple docstring'''
UpperCAmelCase__ = residue_constants.restypes + ["""X"""]
def res_atoa(__A ) -> str:
return residue_constants.restype_atoa.get(restypes[r], "UNK" )
UpperCAmelCase__ = residue_constants.atom_types
UpperCAmelCase__ = []
UpperCAmelCase__ = prot.atom_mask
UpperCAmelCase__ = prot.aatype
UpperCAmelCase__ = prot.atom_positions
UpperCAmelCase__ = prot.residue_index.astype(np.intaa )
UpperCAmelCase__ = prot.b_factors
UpperCAmelCase__ = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("Invalid aatypes." )
UpperCAmelCase__ = get_pdb_headers(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
pdb_lines.extend(_lowerCAmelCase )
UpperCAmelCase__ = aatype.shape[0]
UpperCAmelCase__ = 1
UpperCAmelCase__ = 0
UpperCAmelCase__ = string.ascii_uppercase
UpperCAmelCase__ = None
# Add all atom sites.
for i in range(_lowerCAmelCase ):
UpperCAmelCase__ = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_lowerCAmelCase, atom_positions[i], atom_mask[i], b_factors[i] ):
if mask < 0.5:
continue
UpperCAmelCase__ = """ATOM"""
UpperCAmelCase__ = atom_name if len(_lowerCAmelCase ) == 4 else f""" {atom_name}"""
UpperCAmelCase__ = """"""
UpperCAmelCase__ = """"""
UpperCAmelCase__ = 1.00
UpperCAmelCase__ = atom_name[0] # Protein supports only C, N, O, S, this works.
UpperCAmelCase__ = """"""
UpperCAmelCase__ = """A"""
if chain_index is not None:
UpperCAmelCase__ = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
UpperCAmelCase__ = (
f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
f"""{res_name_a:>3} {chain_tag:>1}"""
f"""{residue_index[i]:>4}{insertion_code:>1} """
f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
f"""{occupancy:>6.2f}{b_factor:>6.2f} """
f"""{element:>2}{charge:>2}"""
)
pdb_lines.append(_lowerCAmelCase )
atom_index += 1
UpperCAmelCase__ = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
UpperCAmelCase__ = True
UpperCAmelCase__ = chain_index[i + 1]
if should_terminate:
# Close the chain.
UpperCAmelCase__ = """TER"""
UpperCAmelCase__ = (
f"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(_lowerCAmelCase )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_lowerCAmelCase, _lowerCAmelCase ) )
pdb_lines.append("END" )
pdb_lines.append("" )
return "\n".join(_lowerCAmelCase )
def lowerCAmelCase_ ( __A ) -> np.ndarray:
'''simple docstring'''
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def lowerCAmelCase_ ( __A, __A, __A = None, __A = None, __A = None, __A = None, __A = None, ) -> Protein:
'''simple docstring'''
return Protein(
aatype=features["aatype"], atom_positions=result["final_atom_positions"], atom_mask=result["final_atom_mask"], residue_index=features["residue_index"] + 1, b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"] ), chain_index=_lowerCAmelCase, remark=_lowerCAmelCase, parents=_lowerCAmelCase, parents_chain_index=_lowerCAmelCase, )
| 371
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
"compression_format, is_archive", [
("7z", True),
("bz2", False),
("gzip", False),
("lz4", False),
("tar", True),
("xz", False),
("zip", True),
("zstd", False),
], )
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, ) -> Any:
'''simple docstring'''
UpperCAmelCase__ = {
"7z": (seven_zip_file, SevenZipExtractor),
"bz2": (bza_file, BzipaExtractor),
"gzip": (gz_file, GzipExtractor),
"lz4": (lza_file, LzaExtractor),
"tar": (tar_file, TarExtractor),
"xz": (xz_file, XzExtractor),
"zip": (zip_file, ZipExtractor),
"zstd": (zstd_file, ZstdExtractor),
}
UpperCAmelCase__ , UpperCAmelCase__ = input_paths_and_base_extractors[compression_format]
if input_path is None:
UpperCAmelCase__ = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
assert base_extractor.is_extractable(__A )
UpperCAmelCase__ = tmp_path / ("extracted" if is_archive else "extracted.txt")
base_extractor.extract(__A, __A )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
UpperCAmelCase__ = file_path.read_text(encoding="utf-8" )
else:
UpperCAmelCase__ = output_path.read_text(encoding="utf-8" )
UpperCAmelCase__ = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
"compression_format, is_archive", [
("7z", True),
("bz2", False),
("gzip", False),
("lz4", False),
("tar", True),
("xz", False),
("zip", True),
("zstd", False),
], )
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ = {
"7z": seven_zip_file,
"bz2": bza_file,
"gzip": gz_file,
"lz4": lza_file,
"tar": tar_file,
"xz": xz_file,
"zip": zip_file,
"zstd": zstd_file,
}
UpperCAmelCase__ = input_paths[compression_format]
if input_path is None:
UpperCAmelCase__ = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
UpperCAmelCase__ = Extractor.infer_extractor_format(__A )
assert extractor_format is not None
UpperCAmelCase__ = tmp_path / ("extracted" if is_archive else "extracted.txt")
Extractor.extract(__A, __A, __A )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
UpperCAmelCase__ = file_path.read_text(encoding="utf-8" )
else:
UpperCAmelCase__ = output_path.read_text(encoding="utf-8" )
UpperCAmelCase__ = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def lowerCAmelCase_ ( __A, __A ) -> List[str]:
'''simple docstring'''
import tarfile
UpperCAmelCase__ = tmp_path / "data_dot_dot"
directory.mkdir()
UpperCAmelCase__ = directory / "tar_file_with_dot_dot.tar"
with tarfile.TarFile(__A, "w" ) as f:
f.add(__A, arcname=os.path.join("..", text_file.name ) )
return path
@pytest.fixture
def lowerCAmelCase_ ( __A ) -> Dict:
'''simple docstring'''
import tarfile
UpperCAmelCase__ = tmp_path / "data_sym_link"
directory.mkdir()
UpperCAmelCase__ = directory / "tar_file_with_sym_link.tar"
os.symlink("..", directory / "subdir", target_is_directory=__A )
with tarfile.TarFile(__A, "w" ) as f:
f.add(str(directory / "subdir" ), arcname="subdir" ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
"insecure_tar_file, error_log", [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")], )
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ = {
"tar_file_with_dot_dot": tar_file_with_dot_dot,
"tar_file_with_sym_link": tar_file_with_sym_link,
}
UpperCAmelCase__ = insecure_tar_files[insecure_tar_file]
UpperCAmelCase__ = tmp_path / "extracted"
TarExtractor.extract(__A, __A )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def lowerCAmelCase_ ( __A ) -> Any:
'''simple docstring'''
UpperCAmelCase__ = tmpdir / "not_a_zip_file"
# From: https://github.com/python/cpython/pull/5053
UpperCAmelCase__ = (
B"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
B"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
B"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
B"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
)
with not_a_zip_file.open("wb" ) as f:
f.write(__A )
assert zipfile.is_zipfile(str(__A ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(__A ) # but we're right
| 143
| 0
|
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] )
def __UpperCamelCase ( _A : Dict , _A : Optional[int] , _A : Tuple ) ->Union[str, Any]:
"""simple docstring"""
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , """IN_MEMORY_MAX_SIZE""" , _A )
lowerCamelCase_ =datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
lowerCamelCase_ =dataset_size < in_memory_max_size
else:
lowerCamelCase_ =False
lowerCamelCase_ =is_small_dataset(_A )
assert result == expected
| 154
|
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__A : Tuple = logging.get_logger(__name__)
def __UpperCamelCase ( _A : str , _A : str , _A : str ) ->int:
"""simple docstring"""
lowerCamelCase_ =UniSpeechSatForSequenceClassification.from_pretrained(_A , config=_A )
lowerCamelCase_ =downstream_dict["""projector.weight"""]
lowerCamelCase_ =downstream_dict["""projector.bias"""]
lowerCamelCase_ =downstream_dict["""model.post_net.linear.weight"""]
lowerCamelCase_ =downstream_dict["""model.post_net.linear.bias"""]
return model
def __UpperCamelCase ( _A : Optional[int] , _A : str , _A : Any ) ->Optional[int]:
"""simple docstring"""
lowerCamelCase_ =UniSpeechSatForAudioFrameClassification.from_pretrained(_A , config=_A )
lowerCamelCase_ =downstream_dict["""model.linear.weight"""]
lowerCamelCase_ =downstream_dict["""model.linear.bias"""]
return model
def __UpperCamelCase ( _A : Optional[Any] , _A : Optional[Any] , _A : Optional[Any] ) ->List[Any]:
"""simple docstring"""
lowerCamelCase_ =UniSpeechSatForXVector.from_pretrained(_A , config=_A )
lowerCamelCase_ =downstream_dict["""connector.weight"""]
lowerCamelCase_ =downstream_dict["""connector.bias"""]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowerCamelCase_ =downstream_dict[
f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
]
lowerCamelCase_ =downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']
lowerCamelCase_ =downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
lowerCamelCase_ =downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
lowerCamelCase_ =downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
lowerCamelCase_ =downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
lowerCamelCase_ =downstream_dict["""objective.W"""]
return model
@torch.no_grad()
def __UpperCamelCase ( _A : Any , _A : Optional[Any] , _A : Union[str, Any] , _A : str ) ->Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =torch.load(_A , map_location="""cpu""" )
lowerCamelCase_ =checkpoint["""Downstream"""]
lowerCamelCase_ =UniSpeechSatConfig.from_pretrained(_A )
lowerCamelCase_ =WavaVecaFeatureExtractor.from_pretrained(
_A , return_attention_mask=_A , do_normalize=_A )
lowerCamelCase_ =hf_config.architectures[0]
if arch.endswith("""ForSequenceClassification""" ):
lowerCamelCase_ =convert_classification(_A , _A , _A )
elif arch.endswith("""ForAudioFrameClassification""" ):
lowerCamelCase_ =convert_diarization(_A , _A , _A )
elif arch.endswith("""ForXVector""" ):
lowerCamelCase_ =convert_xvector(_A , _A , _A )
else:
raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}' )
if hf_config.use_weighted_layer_sum:
lowerCamelCase_ =checkpoint["""Featurizer"""]["""weights"""]
hf_feature_extractor.save_pretrained(_A )
hf_model.save_pretrained(_A )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
)
parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
__A : int = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 154
| 1
|
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
__snake_case : int =logging.get_logger(__name__) # pylint: disable=invalid-name
__snake_case : Optional[int] ="\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n >>> repo = \"openai/shap-e-img2img\"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n >>> image = load_image(image_url).convert(\"RGB\")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n ```\n"
@dataclass
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ =42
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
def __init__(self ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,) -> List[Any]:
"""simple docstring"""
super().__init__()
self.register_modules(
prior=__A ,image_encoder=__A ,image_processor=__A ,scheduler=__A ,renderer=__A ,)
def lowerCAmelCase__ (self ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ) -> Union[str, Any]:
"""simple docstring"""
if latents is None:
lowerCAmelCase__ : Tuple = randn_tensor(__A ,generator=__A ,device=__A ,dtype=__A )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
lowerCAmelCase__ : List[str] = latents.to(__A )
lowerCAmelCase__ : Union[str, Any] = latents * scheduler.init_noise_sigma
return latents
def lowerCAmelCase__ (self ,__lowerCamelCase=0 ) -> Tuple:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
lowerCAmelCase__ : int = torch.device(f"""cuda:{gpu_id}""" )
lowerCAmelCase__ : int = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__A ,__A )
@property
def lowerCAmelCase__ (self ) -> Optional[int]:
"""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(__A ,'''_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 lowerCAmelCase__ (self ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,) -> Optional[int]:
"""simple docstring"""
if isinstance(__A ,__A ) and isinstance(image[0] ,torch.Tensor ):
lowerCAmelCase__ : Dict = torch.cat(__A ,axis=0 ) if image[0].ndim == 4 else torch.stack(__A ,axis=0 )
if not isinstance(__A ,torch.Tensor ):
lowerCAmelCase__ : int = self.image_processor(__A ,return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )
lowerCAmelCase__ : Dict = image.to(dtype=self.image_encoder.dtype ,device=__A )
lowerCAmelCase__ : int = self.image_encoder(__A )['''last_hidden_state''']
lowerCAmelCase__ : Optional[Any] = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
lowerCAmelCase__ : Union[str, Any] = image_embeds.repeat_interleave(__A ,dim=0 )
if do_classifier_free_guidance:
lowerCAmelCase__ : int = torch.zeros_like(__A )
# 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__ : str = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(__A )
def __call__(self ,__lowerCamelCase ,__lowerCamelCase = 1 ,__lowerCamelCase = 25 ,__lowerCamelCase = None ,__lowerCamelCase = None ,__lowerCamelCase = 4.0 ,__lowerCamelCase = 64 ,__lowerCamelCase = "pil" ,__lowerCamelCase = True ,) -> int:
"""simple docstring"""
if isinstance(__A ,PIL.Image.Image ):
lowerCAmelCase__ : Tuple = 1
elif isinstance(__A ,torch.Tensor ):
lowerCAmelCase__ : Tuple = image.shape[0]
elif isinstance(__A ,__A ) and isinstance(image[0] ,(torch.Tensor, PIL.Image.Image) ):
lowerCAmelCase__ : Dict = len(__A )
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(__A )}""" )
lowerCAmelCase__ : Tuple = self._execution_device
lowerCAmelCase__ : int = batch_size * num_images_per_prompt
lowerCAmelCase__ : str = guidance_scale > 1.0
lowerCAmelCase__ : List[Any] = self._encode_image(__A ,__A ,__A ,__A )
# prior
self.scheduler.set_timesteps(__A ,device=__A )
lowerCAmelCase__ : Dict = self.scheduler.timesteps
lowerCAmelCase__ : Union[str, Any] = self.prior.config.num_embeddings
lowerCAmelCase__ : Optional[int] = self.prior.config.embedding_dim
lowerCAmelCase__ : Any = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) ,image_embeds.dtype ,__A ,__A ,__A ,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__ : List[Any] = latents.reshape(latents.shape[0] ,__A ,__A )
for i, t in enumerate(self.progress_bar(__A ) ):
# expand the latents if we are doing classifier free guidance
lowerCAmelCase__ : List[str] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCAmelCase__ : Optional[Any] = self.scheduler.scale_model_input(__A ,__A )
lowerCAmelCase__ : Any = self.prior(
__A ,timestep=__A ,proj_embedding=__A ,).predicted_image_embedding
# remove the variance
lowerCAmelCase__ : Tuple = 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__ : List[str] = noise_pred.chunk(2 )
lowerCAmelCase__ : int = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
lowerCAmelCase__ : int = self.scheduler.step(
__A ,timestep=__A ,sample=__A ,).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=__A )
lowerCAmelCase__ : Union[str, Any] = []
for i, latent in enumerate(__A ):
print()
lowerCAmelCase__ : Any = self.renderer.decode(
latent[None, :] ,__A ,size=__A ,ray_batch_size=40_96 ,n_coarse_samples=64 ,n_fine_samples=1_28 ,)
images.append(__A )
lowerCAmelCase__ : Optional[int] = torch.stack(__A )
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__ : str = images.cpu().numpy()
if output_type == "pil":
lowerCAmelCase__ : str = [self.numpy_to_pil(__A ) 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=__A )
| 360
|
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase__ ( lowerCamelCase_ : ndarray):
'''simple docstring'''
return np.dot(lowerCamelCase_ ,lowerCamelCase_)
class lowerCamelCase__ :
'''simple docstring'''
def __init__(self ,*,
__lowerCamelCase = np.inf ,__lowerCamelCase = "linear" ,__lowerCamelCase = 0.0 ,) -> None:
"""simple docstring"""
lowerCAmelCase__ : Any = regularization
lowerCAmelCase__ : str = gamma
if kernel == "linear":
lowerCAmelCase__ : Dict = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('''rbf kernel requires gamma''' )
if not isinstance(self.gamma ,(float, int) ):
raise ValueError('''gamma must be float or int''' )
if not self.gamma > 0:
raise ValueError('''gamma must be > 0''' )
lowerCAmelCase__ : Optional[Any] = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
lowerCAmelCase__ : List[str] = f"""Unknown kernel: {kernel}"""
raise ValueError(__lowerCamelCase )
def lowerCAmelCase__ (self ,__lowerCamelCase ,__lowerCamelCase ) -> float:
"""simple docstring"""
return np.dot(__lowerCamelCase ,__lowerCamelCase )
def lowerCAmelCase__ (self ,__lowerCamelCase ,__lowerCamelCase ) -> float:
"""simple docstring"""
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def lowerCAmelCase__ (self ,__lowerCamelCase ,__lowerCamelCase ) -> None:
"""simple docstring"""
lowerCAmelCase__ : str = observations
lowerCAmelCase__ : Optional[int] = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((lowerCAmelCase__) , ) : List[str] = np.shape(__lowerCamelCase )
def to_minimize(__lowerCamelCase ) -> float:
lowerCAmelCase__ : List[str] = 0
((lowerCAmelCase__) , ) : str = np.shape(__lowerCamelCase )
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] ,observations[j] )
)
return 1 / 2 * s - sum(__lowerCamelCase )
lowerCAmelCase__ : List[str] = LinearConstraint(__lowerCamelCase ,0 ,0 )
lowerCAmelCase__ : List[str] = Bounds(0 ,self.regularization )
lowerCAmelCase__ : int = minimize(
__lowerCamelCase ,np.ones(__lowerCamelCase ) ,bounds=__lowerCamelCase ,constraints=[ly_contraint] ).x
lowerCAmelCase__ : List[Any] = l_star
# calculating mean offset of separation plane to points
lowerCAmelCase__ : Optional[Any] = 0
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] ,observations[j] )
lowerCAmelCase__ : Dict = s / n
def lowerCAmelCase__ (self ,__lowerCamelCase ) -> int:
"""simple docstring"""
lowerCAmelCase__ : str = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] ,__lowerCamelCase )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 94
| 0
|
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
_a : List[Any]= '''src/transformers'''
# Matches is_xxx_available()
_a : Union[str, Any]= re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
_a : Optional[Any]= re.compile(R"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_a : int= re.compile(R"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
_a : Optional[int]= re.compile(R"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
_a : str= re.compile(R"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_a : Optional[int]= re.compile(R"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
_a : Tuple= re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only: ["foo", "bar"],
_a : Optional[int]= re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
_a : str= re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
_a : Tuple= re.compile(R"^\s*try:")
# Catches a line with else:
_a : Tuple= re.compile(R"^\s*else:")
def __UpperCAmelCase ( UpperCAmelCase_ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
if _re_test_backend.search(A_ ) is None:
return None
__snake_case : Optional[Any] = [b[0] for b in _re_backend.findall(A_ )]
backends.sort()
return "_and_".join(A_ )
def __UpperCAmelCase ( UpperCAmelCase_ : str ) -> str:
'''simple docstring'''
with open(A_ , 'r' , encoding='utf-8' , newline='\n' ) as f:
__snake_case : Union[str, Any] = f.readlines()
__snake_case : Optional[int] = 0
while line_index < len(A_ ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(A_ ):
return None
# First grab the objects without a specific backend in _import_structure
__snake_case : Optional[int] = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
__snake_case : Any = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(A_ ):
__snake_case : Tuple = _re_one_line_import_struct.search(A_ ).groups()[0]
__snake_case : int = re.findall('\[([^\]]+)\]' , A_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
__snake_case : Dict = _re_import_struct_key_value.search(A_ )
if single_line_import_search is not None:
__snake_case : Union[str, Any] = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(A_ ) > 0]
objects.extend(A_ )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
__snake_case : Dict = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
__snake_case : Optional[Any] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
__snake_case : List[str] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
__snake_case : Optional[Any] = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
__snake_case : List[str] = lines[line_index]
if _re_import_struct_add_one.search(A_ ) is not None:
objects.append(_re_import_struct_add_one.search(A_ ).groups()[0] )
elif _re_import_struct_add_many.search(A_ ) is not None:
__snake_case : Dict = _re_import_struct_add_many.search(A_ ).groups()[0].split(', ' )
__snake_case : List[str] = [obj[1:-1] for obj in imports if len(A_ ) > 0]
objects.extend(A_ )
elif _re_between_brackets.search(A_ ) is not None:
__snake_case : Optional[Any] = _re_between_brackets.search(A_ ).groups()[0].split(', ' )
__snake_case : str = [obj[1:-1] for obj in imports if len(A_ ) > 0]
objects.extend(A_ )
elif _re_quote_object.search(A_ ) is not None:
objects.append(_re_quote_object.search(A_ ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
__snake_case : int = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
__snake_case : Optional[Any] = []
while (
line_index < len(A_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
__snake_case : str = lines[line_index]
__snake_case : List[Any] = _re_import.search(A_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
__snake_case : Tuple = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(A_ ):
# If the line is an if is_backend_available, we grab all objects associated.
__snake_case : List[Any] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
__snake_case : Any = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
__snake_case : Optional[Any] = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
__snake_case : str = lines[line_index]
__snake_case : str = _re_import.search(A_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
__snake_case : List[Any] = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def __UpperCAmelCase ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
def find_duplicates(UpperCAmelCase_ : Union[str, Any] ):
return [k for k, v in collections.Counter(A_ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
__snake_case : str = []
for key in import_dict_objects.keys():
__snake_case : Any = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}" )
__snake_case : List[Any] = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
__snake_case : Tuple = '''base imports''' if key == '''none''' else F"{key} backend"
errors.append(F"Differences for {name}:" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F" {a} in TYPE_HINT but not in _import_structure." )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F" {a} in _import_structure but not in TYPE_HINT." )
return errors
def __UpperCAmelCase ( ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = []
for root, _, files in os.walk(A_ ):
if "__init__.py" in files:
__snake_case : str = os.path.join(A_ , '__init__.py' )
__snake_case : Optional[int] = parse_init(A_ )
if objects is not None:
__snake_case : Optional[int] = analyze_results(*A_ )
if len(A_ ) > 0:
__snake_case : Dict = F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append('\n'.join(A_ ) )
if len(A_ ) > 0:
raise ValueError('\n\n'.join(A_ ) )
def __UpperCAmelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[Any] = []
for path, directories, files in os.walk(A_ ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(A_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(A_ ) / folder).glob('*.py' ) ) ) == 0:
continue
__snake_case : Any = str((Path(A_ ) / folder).relative_to(A_ ) )
__snake_case : Optional[int] = short_path.replace(os.path.sep , '.' )
submodules.append(A_ )
for fname in files:
if fname == "__init__.py":
continue
__snake_case : Any = str((Path(A_ ) / fname).relative_to(A_ ) )
__snake_case : str = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(A_ )
return submodules
_a : str= [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
]
def __UpperCAmelCase ( ) -> str:
'''simple docstring'''
__snake_case : Union[str, Any] = importlib.util.spec_from_file_location(
'transformers' , os.path.join(A_ , '__init__.py' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
__snake_case : Any = spec.loader.load_module()
__snake_case : List[str] = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(A_ ) > 0:
__snake_case : List[str] = '''\n'''.join(F"- {module}" for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registered in the main init of Transformers:\n'
F"{list_of_modules}\n"
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 172
|
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__UpperCamelCase : int = logging.getLogger(__name__)
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
# save results
if os.path.exists(A_ ):
if os.path.exists(os.path.join(A_ , '''config.json''' ) ) and os.path.isfile(
os.path.join(A_ , '''config.json''' ) ):
os.remove(os.path.join(A_ , '''config.json''' ) )
if os.path.exists(os.path.join(A_ , '''pytorch_model.bin''' ) ) and os.path.isfile(
os.path.join(A_ , '''pytorch_model.bin''' ) ):
os.remove(os.path.join(A_ , '''pytorch_model.bin''' ) )
else:
os.makedirs(A_ )
model.save_pretrained(A_ )
def __SCREAMING_SNAKE_CASE ( A_ , A_=False ):
lowerCAmelCase__ : Optional[Any] = 2
if unlogit:
lowerCAmelCase__ : Union[str, Any] = torch.pow(A_ , A_ )
lowerCAmelCase__ : Optional[Any] = p * torch.log(A_ )
lowerCAmelCase__ : List[Any] = 0
return -plogp.sum(dim=-1 )
def __SCREAMING_SNAKE_CASE ( A_ ):
logger.info('''lv, h >\t''' + '''\t'''.join(f'{x + 1}' for x in range(len(A_ ) ) ) )
for row in range(len(A_ ) ):
if tensor.dtype != torch.long:
logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:.5f}' for x in tensor[row].cpu().data ) )
else:
logger.info(f'layer {row + 1}:\t' + '''\t'''.join(f'{x:d}' for x in tensor[row].cpu().data ) )
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_=True , A_=True , A_=None , A_=False ):
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = model.config.num_hidden_layers, model.config.num_attention_heads
lowerCAmelCase__ : Dict = torch.zeros(A_ , A_ ).to(args.device )
lowerCAmelCase__ : int = torch.zeros(A_ , A_ ).to(args.device )
if head_mask is None:
lowerCAmelCase__ : Union[str, Any] = torch.ones(A_ , A_ ).to(args.device )
head_mask.requires_grad_(requires_grad=A_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : Optional[int] = 0.0
lowerCAmelCase__ : Optional[int] = 0.0
for step, inputs in enumerate(tqdm(A_ , desc='''Iteration''' , disable=args.local_rank not in [-1, 0] ) ):
lowerCAmelCase__ : Any = tuple(t.to(args.device ) for t in inputs )
((lowerCAmelCase__) ,) : List[Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
lowerCAmelCase__ : Any = model(A_ , labels=A_ , head_mask=A_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Dict = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A_ ):
lowerCAmelCase__ : Dict = entropy(attn.detach() , A_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
lowerCAmelCase__ : Any = 2
lowerCAmelCase__ : Dict = torch.pow(torch.pow(A_ , A_ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-2_0
if not args.dont_normalize_global_importance:
lowerCAmelCase__ : List[Any] = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''' )
print_ad_tensor(A_ )
if compute_importance:
logger.info('''Head importance scores''' )
print_ad_tensor(A_ )
logger.info('''Head ranked by importance scores''' )
lowerCAmelCase__ : str = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
lowerCAmelCase__ : Optional[int] = torch.arange(
head_importance.numel() , device=args.device )
lowerCAmelCase__ : int = head_ranks.view_as(A_ )
print_ad_tensor(A_ )
return attn_entropy, head_importance, total_loss
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ ):
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : List[str] = compute_heads_importance(A_ , A_ , A_ , compute_entropy=A_ )
lowerCAmelCase__ : Union[str, Any] = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , A_ , original_score * args.masking_threshold )
lowerCAmelCase__ : Union[str, Any] = torch.ones_like(A_ )
lowerCAmelCase__ : List[str] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
lowerCAmelCase__ : int = original_score
while current_score >= original_score * args.masking_threshold:
lowerCAmelCase__ : Union[str, Any] = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
lowerCAmelCase__ : str = float('''Inf''' )
lowerCAmelCase__ : List[Any] = head_importance.view(-1 ).sort()[1]
if len(A_ ) <= num_to_mask:
print('''BREAK BY num_to_mask''' )
break
# mask heads
lowerCAmelCase__ : List[Any] = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist() ) )
lowerCAmelCase__ : int = new_head_mask.view(-1 )
lowerCAmelCase__ : Optional[int] = 0.0
lowerCAmelCase__ : Union[str, Any] = new_head_mask.view_as(A_ )
lowerCAmelCase__ : Tuple = new_head_mask.clone().detach()
print_ad_tensor(A_ )
# Compute metric and head importance again
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , head_mask=A_ )
lowerCAmelCase__ : Tuple = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , A_ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_00 , )
logger.info('''Final head mask''' )
print_ad_tensor(A_ )
np.save(os.path.join(args.output_dir , '''head_mask.npy''' ) , head_mask.detach().cpu().numpy() )
return head_mask
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
lowerCAmelCase__ : Optional[Any] = datetime.now()
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Optional[Any] = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ )
lowerCAmelCase__ : Optional[Any] = 1 / loss
lowerCAmelCase__ : Tuple = datetime.now() - before_time
lowerCAmelCase__ : int = sum(p.numel() for p in model.parameters() )
lowerCAmelCase__ : List[Any] = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(A_ , A_ ):
lowerCAmelCase__ : int = [
v,
]
assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A_ )
lowerCAmelCase__ : List[Any] = sum(p.numel() for p in model.parameters() )
lowerCAmelCase__ : Any = datetime.now()
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : int = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ , actually_pruned=A_ , )
lowerCAmelCase__ : int = 1 / loss
lowerCAmelCase__ : Dict = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , A_ , A_ , pruned_num_params / original_num_params * 1_00 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , A_ , A_ )
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 1_00 )
save_model(A_ , args.output_dir )
def __SCREAMING_SNAKE_CASE ( ):
lowerCAmelCase__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=A_ , type=A_ , required=A_ , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=A_ , type=A_ , required=A_ , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=A_ , type=A_ , required=A_ , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=A_ , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=A_ , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=A_ , type=A_ , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=A_ , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''' )
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''' )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''' )
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''' )
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=A_ , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=A_ , help='''Amount to heads to masking at each masking step.''' )
parser.add_argument('''--metric_name''' , default='''acc''' , type=A_ , help='''Metric to use for head masking.''' )
parser.add_argument(
'''--max_seq_length''' , default=1_28 , type=A_ , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=A_ , help='''Batch size.''' )
parser.add_argument('''--seed''' , type=A_ , default=42 )
parser.add_argument('''--local_rank''' , type=A_ , default=-1 , help='''local_rank for distributed training on gpus''' )
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''' )
parser.add_argument('''--server_ip''' , type=A_ , default='''''' , help='''Can be used for distant debugging.''' )
parser.add_argument('''--server_port''' , type=A_ , default='''''' , help='''Can be used for distant debugging.''' )
lowerCAmelCase__ : Optional[Any] = parser.parse_args()
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=A_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
lowerCAmelCase__ : Union[str, Any] = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
lowerCAmelCase__ : str = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
lowerCAmelCase__ : Dict = torch.device('''cuda''' , args.local_rank )
lowerCAmelCase__ : Union[str, Any] = 1
torch.distributed.init_process_group(backend='''nccl''' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
lowerCAmelCase__ : List[str] = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
lowerCAmelCase__ : Dict = nn.parallel.DistributedDataParallel(
A_ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A_ )
elif args.n_gpu > 1:
lowerCAmelCase__ : List[Any] = nn.DataParallel(A_ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A_ )
torch.save(A_ , os.path.join(args.output_dir , '''run_args.bin''' ) )
logger.info('''Training/evaluation parameters %s''' , A_ )
# Prepare dataset
lowerCAmelCase__ : str = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
lowerCAmelCase__ : Union[str, Any] = (torch.from_numpy(A_ ),)
lowerCAmelCase__ : Tuple = TensorDataset(*A_ )
lowerCAmelCase__ : Optional[int] = RandomSampler(A_ )
lowerCAmelCase__ : Dict = DataLoader(A_ , sampler=A_ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A_ , A_ , A_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
lowerCAmelCase__ : Tuple = mask_heads(A_ , A_ , A_ )
prune_heads(A_ , A_ , A_ , A_ )
if __name__ == "__main__":
main()
| 106
| 0
|
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Tuple = None
__lowerCamelCase : int = BloomTokenizerFast
__lowerCamelCase : Union[str, Any] = BloomTokenizerFast
__lowerCamelCase : Any = True
__lowerCamelCase : List[str] = False
__lowerCamelCase : str = "tokenizer_file"
__lowerCamelCase : Any = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
def _lowerCAmelCase ( self ):
super().setUp()
A : List[Any] = BloomTokenizerFast.from_pretrained("""bigscience/tokenizer""" )
tokenizer.save_pretrained(self.tmpdirname )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : List[Any] = self.get_rust_tokenizer()
A : Tuple = ["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
A : List[Any] = [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]
A : Dict = tokenizer.batch_encode_plus(lowerCamelCase__ )["""input_ids"""]
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
A : Tuple = tokenizer.batch_decode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__=6 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
A : int = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__, **lowerCamelCase__ )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
A : Tuple = """This is a simple input"""
A : Optional[Any] = ["""This is a simple input 1""", """This is a simple input 2"""]
A : Optional[Any] = ("""This is a simple input""", """This is a pair""")
A : Union[str, Any] = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
try:
tokenizer_r.encode(lowerCamelCase__, max_length=lowerCamelCase__ )
tokenizer_r.encode_plus(lowerCamelCase__, max_length=lowerCamelCase__ )
tokenizer_r.batch_encode_plus(lowerCamelCase__, max_length=lowerCamelCase__ )
tokenizer_r.encode(lowerCamelCase__, max_length=lowerCamelCase__ )
tokenizer_r.batch_encode_plus(lowerCamelCase__, max_length=lowerCamelCase__ )
except ValueError:
self.fail("""Bloom Tokenizer should be able to deal with padding""" )
A : int = None # Hotfixing padding = None
self.assertRaises(lowerCamelCase__, tokenizer_r.encode, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""" )
# Simple input
self.assertRaises(lowerCamelCase__, tokenizer_r.encode_plus, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""" )
# Simple input
self.assertRaises(
lowerCamelCase__, tokenizer_r.batch_encode_plus, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""", )
# Pair input
self.assertRaises(lowerCamelCase__, tokenizer_r.encode, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""" )
# Pair input
self.assertRaises(lowerCamelCase__, tokenizer_r.encode_plus, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""" )
# Pair input
self.assertRaises(
lowerCamelCase__, tokenizer_r.batch_encode_plus, lowerCamelCase__, max_length=lowerCamelCase__, padding="""max_length""", )
def _lowerCAmelCase ( self ):
A : List[str] = self.get_rust_tokenizer()
A : int = load_dataset("""xnli""", """all_languages""", split="""test""", streaming=lowerCamelCase__ )
A : Dict = next(iter(lowerCamelCase__ ) )["""premise"""] # pick up one data
A : Any = list(sample_data.values() )
A : Tuple = list(map(tokenizer.encode, lowerCamelCase__ ) )
A : Any = [tokenizer.decode(lowerCamelCase__, clean_up_tokenization_spaces=lowerCamelCase__ ) for x in output_tokens]
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
# any sequence length constraints. This test of the parent class will fail since it relies on the
# maximum sequence length of the positoonal embeddings.
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ), 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ), 1 )
| 115
|
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Union[str, Any] = """https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE_:Optional[int] = {
"""jukebox-1b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""1b_lyrics/prior_level_2.pth.tar""",
],
"""jukebox-5b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""5b_lyrics/prior_level_2.pth.tar""",
],
}
def __UpperCamelCase ( _lowerCAmelCase ) -> Dict:
"""simple docstring"""
if key.endswith(""".model.1.bias""" ) and len(key.split(""".""" ) ) > 10:
A : Optional[int] = key.replace(""".model.1.bias""" , """.conv1d_1.bias""" )
elif key.endswith(""".model.1.weight""" ) and len(key.split(""".""" ) ) > 10:
A : Any = key.replace(""".model.1.weight""" , """.conv1d_1.weight""" )
elif key.endswith(""".model.3.bias""" ) and len(key.split(""".""" ) ) > 10:
A : str = key.replace(""".model.3.bias""" , """.conv1d_2.bias""" )
elif key.endswith(""".model.3.weight""" ) and len(key.split(""".""" ) ) > 10:
A : Optional[Any] = key.replace(""".model.3.weight""" , """.conv1d_2.weight""" )
if "conditioner_blocks.0." in key:
A : List[str] = key.replace("""conditioner_blocks.0""" , """conditioner_blocks""" )
if "prime_prior" in key:
A : Tuple = key.replace("""prime_prior""" , """encoder""" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
A : List[str] = key.replace(""".emb.""" , """.""" )
if key.endswith("""k""" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(""".k""" , """.codebook""" )
if "y_emb." in key:
return key.replace("""y_emb.""" , """metadata_embedding.""" )
if "x_emb.emb." in key:
A : Optional[int] = key.replace("""0.x_emb.emb""" , """embed_tokens""" )
if "prime_state_ln" in key:
return key.replace("""prime_state_ln""" , """encoder.final_layer_norm""" )
if ".ln" in key:
return key.replace(""".ln""" , """.layer_norm""" )
if "_ln" in key:
return key.replace("""_ln""" , """_layer_norm""" )
if "prime_state_proj" in key:
return key.replace("""prime_state_proj""" , """encoder.proj_in""" )
if "prime_x_out" in key:
return key.replace("""prime_x_out""" , """encoder.lm_head""" )
if "prior.x_out" in key:
return key.replace("""x_out""" , """fc_proj_out""" )
if "x_emb" in key:
return key.replace("""x_emb""" , """embed_tokens""" )
return key
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
"""simple docstring"""
A : List[str] = {}
import re
A : Any = re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
A : str = re.compile(
R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
A : Union[str, Any] = re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )
A : List[Any] = re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
A : Optional[Any] = re.compile(
R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
A : List[str] = re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )
A : Optional[Any] = re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)""" )
A : Tuple = re.compile(
R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
A : List[Any] = re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)""" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_lowerCAmelCase ):
A : Optional[Any] = re_encoder_block_conv_in.match(_lowerCAmelCase )
A : Tuple = regex_match.groups()
A : str = int(groups[2] ) * 2 + int(groups[3] )
A : int = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
A : Any = re_encoder_block_conv_in.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_encoder_block_resnet.fullmatch(_lowerCAmelCase ):
A : Optional[int] = re_encoder_block_resnet.match(_lowerCAmelCase )
A : str = regex_match.groups()
A : Optional[int] = int(groups[2] ) * 2 + int(groups[3] )
A : Any = {"""1""": 1, """3""": 2}[groups[-2]]
A : int = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
A : List[str] = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
A : str = prefix + resnet_block
A : str = re_encoder_block_resnet.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_encoder_block_proj_out.fullmatch(_lowerCAmelCase ):
A : List[str] = re_encoder_block_proj_out.match(_lowerCAmelCase )
A : List[Any] = regex_match.groups()
A : List[Any] = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
A : Optional[int] = re_encoder_block_proj_out.sub(_lowerCAmelCase , _lowerCAmelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_lowerCAmelCase ):
A : Union[str, Any] = re_decoder_block_conv_out.match(_lowerCAmelCase )
A : Dict = regex_match.groups()
A : Union[str, Any] = int(groups[2] ) * 2 + int(groups[3] ) - 2
A : Optional[int] = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
A : int = re_decoder_block_conv_out.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_decoder_block_resnet.fullmatch(_lowerCAmelCase ):
A : Optional[int] = re_decoder_block_resnet.match(_lowerCAmelCase )
A : List[Any] = regex_match.groups()
A : List[str] = int(groups[2] ) * 2 + int(groups[3] ) - 2
A : str = {"""1""": 1, """3""": 2}[groups[-2]]
A : Optional[int] = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
A : Union[str, Any] = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
A : Tuple = prefix + resnet_block
A : Tuple = re_decoder_block_resnet.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_decoder_block_proj_in.fullmatch(_lowerCAmelCase ):
A : Optional[Any] = re_decoder_block_proj_in.match(_lowerCAmelCase )
A : Any = regex_match.groups()
A : Optional[Any] = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
A : Dict = re_decoder_block_proj_in.sub(_lowerCAmelCase , _lowerCAmelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_lowerCAmelCase ):
A : Optional[int] = re_prior_cond_conv_out.match(_lowerCAmelCase )
A : List[Any] = regex_match.groups()
A : List[Any] = int(groups[1] ) * 2 + int(groups[2] ) - 2
A : Tuple = f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
A : List[str] = re_prior_cond_conv_out.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_prior_cond_resnet.fullmatch(_lowerCAmelCase ):
A : Any = re_prior_cond_resnet.match(_lowerCAmelCase )
A : Any = regex_match.groups()
A : Tuple = int(groups[1] ) * 2 + int(groups[2] ) - 2
A : Optional[Any] = {"""1""": 1, """3""": 2}[groups[-2]]
A : Tuple = f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
A : List[str] = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
A : Dict = prefix + resnet_block
A : Union[str, Any] = re_prior_cond_resnet.sub(_lowerCAmelCase , _lowerCAmelCase )
elif re_prior_cond_proj_in.fullmatch(_lowerCAmelCase ):
A : List[Any] = re_prior_cond_proj_in.match(_lowerCAmelCase )
A : Optional[int] = regex_match.groups()
A : Tuple = f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
A : Optional[int] = re_prior_cond_proj_in.sub(_lowerCAmelCase , _lowerCAmelCase )
# keep original key
else:
A : str = original_key
A : List[str] = replace_key(_lowerCAmelCase )
if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
print(f'''failed converting {original_key} to {key}, does not match''' )
# handle missmatched shape
elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
A : str = model_state_dict[f'''{key_prefix}.{key}''']
print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match''' )
A : Union[str, Any] = original_key
A : Union[str, Any] = original_key
A : List[str] = value
return new_dict
@torch.no_grad()
def __UpperCamelCase ( _lowerCAmelCase=None , _lowerCAmelCase=None ) -> Tuple:
"""simple docstring"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/" )[-1]}''' ):
A : Optional[Any] = requests.get(f'''{PREFIX}{file}''' , allow_redirects=_lowerCAmelCase )
os.makedirs(f'''{pytorch_dump_folder_path}/''' , exist_ok=_lowerCAmelCase )
open(f'''{pytorch_dump_folder_path}/{file.split("/" )[-1]}''' , """wb""" ).write(r.content )
A : Optional[int] = MODEL_MAPPING[model_name.split("""/""" )[-1]]
A : List[Any] = JukeboxConfig.from_pretrained(_lowerCAmelCase )
A : Dict = JukeboxModel(_lowerCAmelCase )
A : str = []
A : Optional[int] = {}
for i, dict_name in enumerate(_lowerCAmelCase ):
A : str = torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}''' )["""model"""]
A : Optional[int] = {}
for k in old_dic.keys():
if k.endswith(""".b""" ):
A : Dict = old_dic[k]
elif k.endswith(""".w""" ):
A : Optional[Any] = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
A : Union[str, Any] = old_dic[k]
else:
A : Optional[int] = old_dic[k]
A : List[str] = """vqvae""" if i == 0 else f'''priors.{3 - i}'''
A : List[Any] = fix_jukebox_keys(_lowerCAmelCase , model.state_dict() , _lowerCAmelCase , _lowerCAmelCase )
weight_dict.append(_lowerCAmelCase )
A : List[Any] = weight_dict.pop(0 )
model.vqvae.load_state_dict(_lowerCAmelCase )
for i in range(len(_lowerCAmelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
with open(f'''{pytorch_dump_folder_path}/mapping.json''' , """w""" ) as txtfile:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowerCAmelCase )
return weight_dict
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_:List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""jukebox-5b-lyrics""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""jukebox-5b-lyrics-converted""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
SCREAMING_SNAKE_CASE_:int = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 115
| 1
|
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def snake_case_ ( ):
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 93
|
"""simple docstring"""
import datasets
from .evaluate import evaluate
A: Optional[Any] = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n"
A: Optional[int] = "\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"
A: int = "\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair as given in the references (see below)\n - 'prediction_text': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - 'id': id of the question-answer pair (see above),\n - 'answers': a Dict in the CUAD dataset format\n {\n 'text': list of possible texts for the answer, as a list of strings\n 'answer_start': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n 'exact_match': Exact match (the normalized answer exactly match the gold answer)\n 'f1': The F-score of predicted tokens versus the gold answer\n 'aupr': Area Under the Precision-Recall curve\n 'prec_at_80_recall': Precision at 80% recall\n 'prec_at_90_recall': Precision at 90% recall\nExamples:\n >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n >>> cuad_metric = datasets.load_metric(\"cuad\")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
UpperCAmelCase : int = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCAmelCase : Tuple = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCAmelCase : Optional[Any] = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 109
| 0
|
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase = logging.get_logger(__name__)
def lowerCAmelCase (__UpperCamelCase : Tuple ):
"""simple docstring"""
__UpperCamelCase =torch.load(__a , map_location='''cpu''' )
if "model" in sd.keys():
__UpperCamelCase =torch.load(__a , map_location='''cpu''' )['model']
# pop unnecessary weights
__UpperCamelCase =[
'decoder.version',
'decoder.output_projection.weight',
]
for key in keys_to_delete:
if key in sd:
sd.pop(__a )
__UpperCamelCase ={
'decoder.project_in_dim.weight': 'decoder.project_in.weight',
'decoder.project_out_dim.weight': 'decoder.project_out.weight',
'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__UpperCamelCase =sd.pop(__a )
__UpperCamelCase =list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__UpperCamelCase =sd[key]
# We split QKV in separate Q,K,V
__UpperCamelCase =key.replace('''.qkv_proj.''' , '''.q_proj.''' )
__UpperCamelCase =key.replace('''.qkv_proj.''' , '''.k_proj.''' )
__UpperCamelCase =key.replace('''.qkv_proj.''' , '''.v_proj.''' )
__UpperCamelCase =value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__UpperCamelCase =torch.split(__a , depth // 3 , dim=0 )
__UpperCamelCase =q
__UpperCamelCase =k
__UpperCamelCase =v
del sd[key]
return sd
@torch.no_grad()
def lowerCAmelCase (__UpperCamelCase : List[str] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict=None ):
"""simple docstring"""
__UpperCamelCase =load_checkpoint(__a )
if config is not None:
__UpperCamelCase =OPTConfig.from_pretrained(__a )
else:
__UpperCamelCase =OPTConfig()
__UpperCamelCase =OPTModel(__a ).half().eval()
model.load_state_dict(__a )
# Check results
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--fairseq_path''',
type=str,
help=(
'''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
''' https://huggingface.co/models?other=opt_metasq'''
),
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
__lowercase = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 371
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__lowercase = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ['''GPTNeoXTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
'''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXForCausalLM''',
'''GPTNeoXForQuestionAnswering''',
'''GPTNeoXForSequenceClassification''',
'''GPTNeoXForTokenClassification''',
'''GPTNeoXLayer''',
'''GPTNeoXModel''',
'''GPTNeoXPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 85
| 0
|
import inspect
import unittest
from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase , __lowercase=13 , __lowercase=30 , __lowercase=2 , __lowercase=3 , __lowercase=True , __lowercase=True , __lowercase=32 , __lowercase=5 , __lowercase=4 , __lowercase=37 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=10 , __lowercase=0.02 , __lowercase=None , ) -> List[str]:
__UpperCamelCase :str = parent
__UpperCamelCase :Optional[int] = batch_size
__UpperCamelCase :List[str] = image_size
__UpperCamelCase :str = patch_size
__UpperCamelCase :Union[str, Any] = num_channels
__UpperCamelCase :Dict = is_training
__UpperCamelCase :str = use_labels
__UpperCamelCase :Dict = hidden_size
__UpperCamelCase :List[Any] = num_hidden_layers
__UpperCamelCase :int = num_attention_heads
__UpperCamelCase :Tuple = intermediate_size
__UpperCamelCase :Union[str, Any] = hidden_act
__UpperCamelCase :List[Any] = hidden_dropout_prob
__UpperCamelCase :Optional[Any] = attention_probs_dropout_prob
__UpperCamelCase :Any = type_sequence_label_size
__UpperCamelCase :Optional[Any] = initializer_range
__UpperCamelCase :str = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__UpperCamelCase :Union[str, Any] = (image_size // patch_size) ** 2
__UpperCamelCase :List[Any] = num_patches + 1
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__UpperCamelCase :Optional[Any] = None
if self.use_labels:
__UpperCamelCase :Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__UpperCamelCase :Optional[int] = self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ ( self) -> List[str]:
return ViTMSNConfig(
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 , initializer_range=self.initializer_range , )
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> Union[str, Any]:
__UpperCamelCase :int = ViTMSNModel(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__UpperCamelCase :Union[str, Any] = model(_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> Dict:
__UpperCamelCase :List[Any] = self.type_sequence_label_size
__UpperCamelCase :Any = ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__UpperCamelCase :Union[str, Any] = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''')
print('''Labels: {labels}''')
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
__UpperCamelCase :List[str] = 1
__UpperCamelCase :int = ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__UpperCamelCase :Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
__UpperCamelCase :int = model(_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :Optional[int] = self.prepare_config_and_inputs()
__UpperCamelCase :Any = config_and_inputs
__UpperCamelCase :List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : Optional[int] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
a__ : str = (
{"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
if is_torch_available()
else {}
)
a__ : str = False
a__ : str = False
a__ : int = False
a__ : List[str] = False
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :Optional[int] = ViTMSNModelTester(self)
__UpperCamelCase :Any = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE , hidden_size=37)
def UpperCamelCase__ ( self) -> Optional[int]:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''')
def UpperCamelCase__ ( self) -> List[Any]:
pass
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCamelCase :Tuple = model_class(_SCREAMING_SNAKE_CASE)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
__UpperCamelCase :Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE , nn.Linear))
def UpperCamelCase__ ( self) -> Union[str, Any]:
__UpperCamelCase :str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCamelCase :Optional[Any] = model_class(_SCREAMING_SNAKE_CASE)
__UpperCamelCase :List[str] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCamelCase :Dict = [*signature.parameters.keys()]
__UpperCamelCase :List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE)
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE)
@slow
def UpperCamelCase__ ( self) -> Dict:
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCamelCase :Optional[Any] = ViTMSNModel.from_pretrained(_SCREAMING_SNAKE_CASE)
self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase__ ( self) -> Tuple:
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''') if is_vision_available() else None
@slow
def UpperCamelCase__ ( self) -> Union[str, Any]:
torch.manual_seed(2)
__UpperCamelCase :List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''').to(_SCREAMING_SNAKE_CASE)
__UpperCamelCase :Tuple = self.default_image_processor
__UpperCamelCase :List[Any] = prepare_img()
__UpperCamelCase :Optional[int] = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='''pt''').to(_SCREAMING_SNAKE_CASE)
# forward pass
with torch.no_grad():
__UpperCamelCase :Tuple = model(**_SCREAMING_SNAKE_CASE)
# verify the logits
__UpperCamelCase :int = torch.Size((1, 1_000))
self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE)
__UpperCamelCase :Optional[int] = torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(_SCREAMING_SNAKE_CASE)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4))
| 43
|
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _SCREAMING_SNAKE_CASE ( *SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=2 ):
from .. import __version__
A_ : Union[str, Any] = take_from
A_ : Optional[Any] = ()
if not isinstance(args[0] , SCREAMING_SNAKE_CASE ):
A_ : List[str] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(SCREAMING_SNAKE_CASE ).base_version ) >= version.parse(SCREAMING_SNAKE_CASE ):
raise ValueError(
f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
f''' version {__version__} is >= {version_name}''' )
A_ : List[Any] = None
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE ),)
A_ : Optional[Any] = f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
elif hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
values += (getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ),)
A_ : int = f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
elif deprecated_kwargs is None:
A_ : List[Any] = f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''
if warning is not None:
A_ : Union[str, Any] = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , SCREAMING_SNAKE_CASE , stacklevel=SCREAMING_SNAKE_CASE )
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
A_ : Dict = inspect.getouterframes(inspect.currentframe() )[1]
A_ : Optional[int] = call_frame.filename
A_ : Optional[int] = call_frame.lineno
A_ : str = call_frame.function
A_ , A_ : List[str] = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`''' )
if len(SCREAMING_SNAKE_CASE ) == 0:
return
elif len(SCREAMING_SNAKE_CASE ) == 1:
return values[0]
return values
| 186
| 0
|
from math import factorial
__A ={str(d): factorial(d) for d in range(1_0)}
def lowerCamelCase_ ( lowerCamelCase__ ):
return sum(DIGIT_FACTORIAL[d] for d in str(a__ ) )
def lowerCamelCase_ ( ):
lowerCamelCase_ = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , a__ ) if sum_of_digit_factorial(a__ ) == i )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 368
|
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__A =True
except ImportError:
__A =False
__A =logging.get_logger(__name__) # pylint: disable=invalid-name
def lowerCamelCase_ ( lowerCamelCase__ ):
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
@staticmethod
def SCREAMING_SNAKE_CASE_( lowercase ) -> int:
lowerCamelCase_ = parser.add_parser("add-new-model" )
add_new_model_parser.add_argument("--testing" , action="store_true" , help="If in testing mode." )
add_new_model_parser.add_argument("--testing_file" , type=lowercase , help="Configuration file on which to run." )
add_new_model_parser.add_argument(
"--path" , type=lowercase , help="Path to cookiecutter. Should only be used for testing purposes." )
add_new_model_parser.set_defaults(func=lowercase )
def __init__( self , lowercase , lowercase , lowercase=None , *lowercase ) -> List[str]:
lowerCamelCase_ = testing
lowerCamelCase_ = testing_file
lowerCamelCase_ = path
def SCREAMING_SNAKE_CASE_( self ) -> str:
warnings.warn(
"The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
"It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
"checks, you should use `transformers-cli add-new-model-like` instead." )
if not _has_cookiecutter:
raise ImportError(
"Model creation dependencies are required to use the `add_new_model` command. Install them by running "
"the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
lowerCamelCase_ = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
if len(lowercase ) > 0:
raise ValueError(
"Several directories starting with `cookiecutter-template-` in current working directory. "
"Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
"change your working directory." )
lowerCamelCase_ = (
Path(lowercase ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
lowerCamelCase_ = path_to_transformer_root / "templates" / "adding_a_new_model"
# Execute cookiecutter
if not self._testing:
cookiecutter(str(lowercase ) )
else:
with open(self._testing_file , "r" ) as configuration_file:
lowerCamelCase_ = json.load(lowercase )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=lowercase , extra_context=lowercase , )
lowerCamelCase_ = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]
# Retrieve configuration
with open(directory + "/configuration.json" , "r" ) as configuration_file:
lowerCamelCase_ = json.load(lowercase )
lowerCamelCase_ = configuration["lowercase_modelname"]
lowerCamelCase_ = configuration["generate_tensorflow_pytorch_and_flax"]
os.remove(f'{directory}/configuration.json' )
lowerCamelCase_ = "PyTorch" in generate_tensorflow_pytorch_and_flax
lowerCamelCase_ = "TensorFlow" in generate_tensorflow_pytorch_and_flax
lowerCamelCase_ = "Flax" in generate_tensorflow_pytorch_and_flax
lowerCamelCase_ = f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
os.makedirs(lowercase , exist_ok=lowercase )
os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}' , exist_ok=lowercase )
# Tests require submodules as they have parent imports
with open(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' , "w" ):
pass
shutil.move(
f'{directory}/__init__.py' , f'{model_dir}/__init__.py' , )
shutil.move(
f'{directory}/configuration_{lowercase_model_name}.py' , f'{model_dir}/configuration_{lowercase_model_name}.py' , )
def remove_copy_lines(lowercase ):
with open(lowercase , "r" ) as f:
lowerCamelCase_ = f.readlines()
with open(lowercase , "w" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(lowercase )
if output_pytorch:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_{lowercase_model_name}.py' , f'{model_dir}/modeling_{lowercase_model_name}.py' , )
shutil.move(
f'{directory}/test_modeling_{lowercase_model_name}.py' , f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' , )
else:
os.remove(f'{directory}/modeling_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_{lowercase_model_name}.py' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_tf_{lowercase_model_name}.py' , f'{model_dir}/modeling_tf_{lowercase_model_name}.py' , )
shutil.move(
f'{directory}/test_modeling_tf_{lowercase_model_name}.py' , f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' , )
else:
os.remove(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_tf_{lowercase_model_name}.py' )
if output_flax:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_flax_{lowercase_model_name}.py' , f'{model_dir}/modeling_flax_{lowercase_model_name}.py' , )
shutil.move(
f'{directory}/test_modeling_flax_{lowercase_model_name}.py' , f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' , )
else:
os.remove(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/{lowercase_model_name}.md' , f'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' , )
shutil.move(
f'{directory}/tokenization_{lowercase_model_name}.py' , f'{model_dir}/tokenization_{lowercase_model_name}.py' , )
shutil.move(
f'{directory}/tokenization_fast_{lowercase_model_name}.py' , f'{model_dir}/tokenization_{lowercase_model_name}_fast.py' , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(lowercase , lowercase , lowercase ):
# Create temp file
lowerCamelCase_ , lowerCamelCase_ = mkstemp()
lowerCamelCase_ = False
with fdopen(lowercase , "w" ) as new_file:
with open(lowercase ) as old_file:
for line in old_file:
new_file.write(lowercase )
if line_to_copy_below in line:
lowerCamelCase_ = True
for line_to_copy in lines_to_copy:
new_file.write(lowercase )
if not line_found:
raise ValueError(f'Line {line_to_copy_below} was not found in file.' )
# Copy the file permissions from the old file to the new file
copymode(lowercase , lowercase )
# Remove original file
remove(lowercase )
# Move new file
move(lowercase , lowercase )
def skip_units(lowercase ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(lowercase ):
with open(lowercase ) as datafile:
lowerCamelCase_ = []
lowerCamelCase_ = False
lowerCamelCase_ = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
lowerCamelCase_ = line.split("\"" )[1]
lowerCamelCase_ = skip_units(lowercase )
elif "# Below: " in line and "##" not in line:
lowerCamelCase_ = line.split("\"" )[1]
lowerCamelCase_ = skip_units(lowercase )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(lowercase , lowercase , lowercase )
lowerCamelCase_ = []
elif "# Replace with" in line and "##" not in line:
lowerCamelCase_ = []
elif "##" not in line:
lines_to_copy.append(lowercase )
remove(lowercase )
replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py' )
os.rmdir(lowercase )
| 47
| 0
|
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_A )
class _UpperCAmelCase ( _A ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
SCREAMING_SNAKE_CASE_ : str = field(default="question-answering-extractive" , metadata={"include_in_asdict_even_if_is_default": True} )
SCREAMING_SNAKE_CASE_ : ClassVar[Features] = Features({"question": Value("string" ), "context": Value("string" )} )
SCREAMING_SNAKE_CASE_ : ClassVar[Features] = Features(
{
"answers": Sequence(
{
"text": Value("string" ),
"answer_start": Value("int32" ),
} )
} )
SCREAMING_SNAKE_CASE_ : str = "question"
SCREAMING_SNAKE_CASE_ : str = "context"
SCREAMING_SNAKE_CASE_ : str = "answers"
@property
def A ( self : Any ) -> Dict[str, str]:
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 33
|
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = SwinConfig()
SCREAMING_SNAKE_CASE = swin_name.split("""_""" )
SCREAMING_SNAKE_CASE = name_split[1]
SCREAMING_SNAKE_CASE = int(name_split[4] )
SCREAMING_SNAKE_CASE = int(name_split[3][-1] )
if model_size == "tiny":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 6, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "small":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "base":
SCREAMING_SNAKE_CASE = 1_28
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
else:
SCREAMING_SNAKE_CASE = 1_92
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
if "in22k" in swin_name:
SCREAMING_SNAKE_CASE = 2_18_41
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = img_size
SCREAMING_SNAKE_CASE = num_classes
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = window_size
return config
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE = """encoder.""" + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE = """layernorm.weight"""
if name == "norm.bias":
SCREAMING_SNAKE_CASE = """layernorm.bias"""
if "head" in name:
SCREAMING_SNAKE_CASE = name.replace("""head""" , """classifier""" )
else:
SCREAMING_SNAKE_CASE = """swin.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[1] )
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[
:dim
]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
SCREAMING_SNAKE_CASE = get_swin_config(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = SwinForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
SCREAMING_SNAKE_CASE = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = timm_model(inputs["""pixel_values"""] )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swin_name""",
default="""swin_tiny_patch4_window7_224""",
type=str,
help="""Name of the Swin timm 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."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 296
| 0
|
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __A( __UpperCamelCase ):
snake_case_ = 42
snake_case_ = None
def __lowerCAmelCase ( a__ , a__=0.999 , a__="cosine" , ) -> Optional[Any]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(a__ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(a__ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
__a = []
for i in range(lowercase__ ):
__a = i / num_diffusion_timesteps
__a = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowercase__ ) / alpha_bar_fn(lowercase__ ) , lowercase__ ) )
return torch.tensor(lowercase__ , dtype=torch.floataa )
class __A( __UpperCamelCase , __UpperCamelCase ):
@register_to_config
def __init__( self , _snake_case = 1_000 , _snake_case = "fixed_small_log" , _snake_case = True , _snake_case = 1.0 , _snake_case = "epsilon" , _snake_case = "squaredcos_cap_v2" , ) -> List[Any]:
'''simple docstring'''
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
__a = betas_for_alpha_bar(_snake_case )
__a = 1.0 - self.betas
__a = torch.cumprod(self.alphas , dim=0 )
__a = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
__a = 1.0
# setable values
__a = None
__a = torch.from_numpy(np.arange(0 , _snake_case )[::-1].copy() )
__a = variance_type
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> Union[str, Any]:
'''simple docstring'''
return sample
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> Any:
'''simple docstring'''
__a = num_inference_steps
__a = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
__a = (np.arange(0 , _snake_case ) * step_ratio).round()[::-1].copy().astype(np.intaa )
__a = torch.from_numpy(_snake_case ).to(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=None , _snake_case=None , _snake_case=None ) -> str:
'''simple docstring'''
if prev_timestep is None:
__a = t - 1
__a = self.alphas_cumprod[t]
__a = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__a = 1 - alpha_prod_t
__a = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__a = self.betas[t]
else:
__a = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
__a = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
__a = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
__a = torch.log(torch.clamp(_snake_case , min=1E-20 ) )
__a = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
__a = variance.log()
__a = beta.log()
__a = (predicted_variance + 1) / 2
__a = frac * max_log + (1 - frac) * min_log
return variance
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case = None , _snake_case=None , _snake_case = True , ) -> List[Any]:
'''simple docstring'''
__a = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
__a = torch.split(_snake_case , sample.shape[1] , dim=1 )
else:
__a = None
# 1. compute alphas, betas
if prev_timestep is None:
__a = t - 1
__a = self.alphas_cumprod[t]
__a = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__a = 1 - alpha_prod_t
__a = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__a = self.betas[t]
__a = self.alphas[t]
else:
__a = 1 - alpha_prod_t / alpha_prod_t_prev
__a = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
__a = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
__a = model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
''' for the UnCLIPScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
__a = torch.clamp(
_snake_case , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__a = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
__a = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__a = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
__a = 0
if t > 0:
__a = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=_snake_case , device=model_output.device )
__a = self._get_variance(
_snake_case , predicted_variance=_snake_case , prev_timestep=_snake_case , )
if self.variance_type == "fixed_small_log":
__a = variance
elif self.variance_type == "learned_range":
__a = (0.5 * variance).exp()
else:
raise ValueError(
F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
''' for the UnCLIPScheduler.''' )
__a = variance * variance_noise
__a = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=_snake_case , pred_original_sample=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , ) -> Optional[Any]:
'''simple docstring'''
__a = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
__a = timesteps.to(original_samples.device )
__a = alphas_cumprod[timesteps] ** 0.5
__a = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
__a = sqrt_alpha_prod.unsqueeze(-1 )
__a = (1 - alphas_cumprod[timesteps]) ** 0.5
__a = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
__a = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
__a = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 356
|
import os
# Precomputes a list of the 100 first triangular numbers
A : List[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def __lowerCAmelCase ( ) -> Tuple:
__a = os.path.dirname(os.path.realpath(a__ ) )
__a = os.path.join(a__ , '''words.txt''' )
__a = ''''''
with open(a__ ) as f:
__a = f.readline()
__a = [word.strip('''"''' ) for word in words.strip('''\r\n''' ).split(''',''' )]
__a = [
word
for word in [sum(ord(a__ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(a__ )
if __name__ == "__main__":
print(solution())
| 33
| 0
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( unittest.TestCase ):
"""simple docstring"""
@property
def UpperCAmelCase__ ( self :List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase__ : Any =UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def UpperCAmelCase__ ( self :List[Any] ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =self.dummy_uncond_unet
lowerCamelCase__ : int =ScoreSdeVeScheduler()
lowerCamelCase__ : Union[str, Any] =ScoreSdeVePipeline(unet=__lowercase , scheduler=__lowercase )
sde_ve.to(__lowercase )
sde_ve.set_progress_bar_config(disable=__lowercase )
lowerCamelCase__ : str =torch.manual_seed(0 )
lowerCamelCase__ : List[str] =sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__lowercase ).images
lowerCamelCase__ : Optional[int] =torch.manual_seed(0 )
lowerCamelCase__ : List[Any] =sde_ve(num_inference_steps=2 , output_type='numpy' , generator=__lowercase , return_dict=__lowercase )[
0
]
lowerCamelCase__ : List[str] =image[0, -3:, -3:, -1]
lowerCamelCase__ : Union[str, Any] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase__ : List[Any] =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class A_ ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self :Dict ):
"""simple docstring"""
lowerCamelCase__ : List[str] ='google/ncsnpp-church-256'
lowerCamelCase__ : str =UNetaDModel.from_pretrained(__lowercase )
lowerCamelCase__ : Union[str, Any] =ScoreSdeVeScheduler.from_pretrained(__lowercase )
lowerCamelCase__ : Tuple =ScoreSdeVePipeline(unet=__lowercase , scheduler=__lowercase )
sde_ve.to(__lowercase )
sde_ve.set_progress_bar_config(disable=__lowercase )
lowerCamelCase__ : List[Any] =torch.manual_seed(0 )
lowerCamelCase__ : Any =sde_ve(num_inference_steps=10 , output_type='numpy' , generator=__lowercase ).images
lowerCamelCase__ : Optional[int] =image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowerCamelCase__ : Tuple =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 126
|
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __magic_name__( lowerCamelCase=None, lowerCamelCase=None):
return field(default_factory=lambda: default, metadata=lowerCamelCase)
@dataclass
class a__ :
"""simple docstring"""
__UpperCamelCase : str = field(
metadata={'help': 'The csv file to plot.'} , )
__UpperCamelCase : bool = field(
default=__A , metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'} , )
__UpperCamelCase : bool = field(
default=__A , metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'} , )
__UpperCamelCase : bool = field(
default=__A , metadata={'help': 'Disable logarithmic scale when plotting'} , )
__UpperCamelCase : bool = field(
default=__A , metadata={
'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
} , )
__UpperCamelCase : Optional[str] = field(
default=__A , metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'} , )
__UpperCamelCase : Optional[List[str]] = list_field(
default=__A , metadata={'help': 'List of model names that are used instead of the ones in the csv file.'} )
def __magic_name__( lowerCamelCase):
try:
int(lowerCamelCase)
return True
except ValueError:
return False
def __magic_name__( lowerCamelCase):
try:
float(lowerCamelCase)
return True
except ValueError:
return False
class a__ :
"""simple docstring"""
def __init__(self , __lowercase ):
__lowerCAmelCase = args
__lowerCAmelCase = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline='''''' ) as csv_file:
__lowerCAmelCase = csv.DictReader(__lowercase )
for row in reader:
__lowerCAmelCase = row['''model''']
self.result_dict[model_name]["bsz"].append(int(row['''batch_size'''] ) )
self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length'''] ) )
if can_convert_to_int(row['''result'''] ):
# value is not None
__lowerCAmelCase = int(row['''result'''] )
elif can_convert_to_float(row['''result'''] ):
# value is not None
__lowerCAmelCase = float(row['''result'''] )
def _snake_case (self ):
__lowerCAmelCase , __lowerCAmelCase = plt.subplots()
__lowerCAmelCase = '''Time usage''' if self.args.is_time else '''Memory usage'''
__lowerCAmelCase = title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale('''log''' )
ax.set_yscale('''log''' )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
__lowerCAmelCase = sorted(set(self.result_dict[model_name]['''bsz'''] ) )
__lowerCAmelCase = sorted(set(self.result_dict[model_name]['''seq_len'''] ) )
__lowerCAmelCase = self.result_dict[model_name]['''result''']
((__lowerCAmelCase) , (__lowerCAmelCase)) = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
__lowerCAmelCase = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
__lowerCAmelCase = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=__lowercase , )
else:
__lowerCAmelCase = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((__lowerCAmelCase) , (__lowerCAmelCase)) = (
('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
)
__lowerCAmelCase = np.asarray(__lowercase , __lowercase )[: len(__lowercase )]
plt.scatter(
__lowercase , __lowercase , label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}""" )
plt.plot(__lowercase , __lowercase , '''--''' )
title_str += F""" {label_model_name} vs."""
__lowerCAmelCase = title_str[:-4]
__lowerCAmelCase = '''Time in s''' if self.args.is_time else '''Memory in MB'''
# plot
plt.title(__lowercase )
plt.xlabel(__lowercase )
plt.ylabel(__lowercase )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def __magic_name__( ):
__lowerCAmelCase = HfArgumentParser(lowerCamelCase)
__lowerCAmelCase = parser.parse_args_into_dataclasses()[0]
__lowerCAmelCase = Plot(args=lowerCamelCase)
plot.plot()
if __name__ == "__main__":
main()
| 174
| 0
|
"""simple docstring"""
def lowerCAmelCase ():
"""simple docstring"""
return 1
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(__UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(__UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(__UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(__UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(__UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(__UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : int = 2_0_0 ):
"""simple docstring"""
return two_pound(__UpperCamelCase )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 85
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Any ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =data
__UpperCamelCase =None
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase =None
__UpperCamelCase =None
def __iter__( self : int ) -> Iterator[Any]:
'''simple docstring'''
__UpperCamelCase =self.head
while self.head:
yield node.data
__UpperCamelCase =node.next
if node == self.head:
break
def __len__( self : Union[str, Any] ) -> int:
'''simple docstring'''
return sum(1 for _ in self )
def __repr__( self : str ) -> Union[str, Any]:
'''simple docstring'''
return "->".join(str(UpperCamelCase__ ) for item in iter(self ) )
def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
self.insert_nth(len(self ) , UpperCamelCase__ )
def UpperCAmelCase_ ( self : Optional[int] , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
self.insert_nth(0 , UpperCamelCase__ )
def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Any ) -> None:
'''simple docstring'''
if index < 0 or index > len(self ):
raise IndexError('''list index out of range.''' )
__UpperCamelCase =Node(UpperCamelCase__ )
if self.head is None:
__UpperCamelCase =new_node # first node points itself
__UpperCamelCase =__UpperCamelCase =new_node
elif index == 0: # insert at head
__UpperCamelCase =self.head
__UpperCamelCase =__UpperCamelCase =new_node
else:
__UpperCamelCase =self.head
for _ in range(index - 1 ):
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next
__UpperCamelCase =new_node
if index == len(self ) - 1: # insert at tail
__UpperCamelCase =new_node
def UpperCAmelCase_ ( self : Any ) -> Any:
'''simple docstring'''
return self.delete_nth(0 )
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return self.delete_nth(len(self ) - 1 )
def UpperCAmelCase_ ( self : int , UpperCamelCase__ : int = 0 ) -> Any:
'''simple docstring'''
if not 0 <= index < len(self ):
raise IndexError('''list index out of range.''' )
__UpperCamelCase =self.head
if self.head == self.tail: # just one node
__UpperCamelCase =__UpperCamelCase =None
elif index == 0: # delete head node
__UpperCamelCase =self.tail.next.next
__UpperCamelCase =self.head.next
else:
__UpperCamelCase =self.head
for _ in range(index - 1 ):
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next
__UpperCamelCase =temp.next.next
if index == len(self ) - 1: # delete at tail
__UpperCamelCase =temp
return delete_node.data
def UpperCAmelCase_ ( self : str ) -> bool:
'''simple docstring'''
return len(self ) == 0
def lowerCAmelCase ():
"""simple docstring"""
__UpperCamelCase =CircularLinkedList()
assert len(__UpperCamelCase ) == 0
assert circular_linked_list.is_empty() is True
assert str(__UpperCamelCase ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(__UpperCamelCase ) == i
circular_linked_list.insert_nth(__UpperCamelCase , i + 1 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85
| 1
|
"""simple docstring"""
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''T''')
class __snake_case ( Generic[T]):
def __init__( self : Tuple , __lowerCAmelCase : bool = True ):
"""simple docstring"""
_lowerCamelCase : dict[T, list[T]] = {} # dictionary of lists
_lowerCamelCase : Any = directed
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : T , __lowerCAmelCase : T ):
"""simple docstring"""
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(__lowerCAmelCase )
self.adj_list[destination_vertex].append(__lowerCAmelCase )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(__lowerCAmelCase )
_lowerCamelCase : List[Any] = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(__lowerCAmelCase )
_lowerCamelCase : Optional[int] = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
_lowerCamelCase : Any = [destination_vertex]
_lowerCamelCase : Optional[int] = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(__lowerCAmelCase )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
_lowerCamelCase : List[Any] = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
_lowerCamelCase : Any = [destination_vertex]
_lowerCamelCase : List[str] = []
return self
def __repr__( self : Dict ):
"""simple docstring"""
return pformat(self.adj_list )
| 72
|
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def lowerCamelCase_ (UpperCamelCase__ : float ):
if num <= 0:
raise ValueError('''math domain error''' )
return quad(UpperCamelCase__ , 0 , UpperCamelCase__ , args=(UpperCamelCase__) )[0]
def lowerCamelCase_ (UpperCamelCase__ : float , UpperCamelCase__ : float ):
return math.pow(UpperCamelCase__ , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 263
| 0
|
import os
import unicodedata
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
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = {
'vocab_file': {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
}
}
_UpperCAmelCase = {
'albert-base-v1': 5_1_2,
'albert-large-v1': 5_1_2,
'albert-xlarge-v1': 5_1_2,
'albert-xxlarge-v1': 5_1_2,
'albert-base-v2': 5_1_2,
'albert-large-v2': 5_1_2,
'albert-xlarge-v2': 5_1_2,
'albert-xxlarge-v2': 5_1_2,
}
_UpperCAmelCase = '▁'
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Optional[int] = VOCAB_FILES_NAMES
_UpperCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self: str , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , _SCREAMING_SNAKE_CASE: Any=False , _SCREAMING_SNAKE_CASE: List[Any]="[CLS]" , _SCREAMING_SNAKE_CASE: Tuple="[SEP]" , _SCREAMING_SNAKE_CASE: List[Any]="<unk>" , _SCREAMING_SNAKE_CASE: str="[SEP]" , _SCREAMING_SNAKE_CASE: Any="<pad>" , _SCREAMING_SNAKE_CASE: Dict="[CLS]" , _SCREAMING_SNAKE_CASE: Any="[MASK]" , _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None , **_SCREAMING_SNAKE_CASE: List[str] , ) -> None:
"""simple docstring"""
UpperCamelCase_ = (
AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE , normalized=_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else mask_token
)
UpperCamelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_SCREAMING_SNAKE_CASE , remove_space=_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = do_lower_case
UpperCamelCase_ = remove_space
UpperCamelCase_ = keep_accents
UpperCamelCase_ = vocab_file
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
@property
def lowercase ( self: int ) -> List[str]:
"""simple docstring"""
return len(self.sp_model )
def lowercase ( self: Dict ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self: Any ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.__dict__.copy()
UpperCamelCase_ = None
return state
def __setstate__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCamelCase_ = {}
UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: Union[str, Any] ) -> str:
"""simple docstring"""
if self.remove_space:
UpperCamelCase_ = " ".join(inputs.strip().split() )
else:
UpperCamelCase_ = inputs
UpperCamelCase_ = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
UpperCamelCase_ = unicodedata.normalize("NFKD" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = "".join([c for c in outputs if not unicodedata.combining(_SCREAMING_SNAKE_CASE )] )
if self.do_lower_case:
UpperCamelCase_ = outputs.lower()
return outputs
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: str ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = self.preprocess_text(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = []
for piece in pieces:
if len(_SCREAMING_SNAKE_CASE ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
UpperCamelCase_ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_SCREAMING_SNAKE_CASE , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
UpperCamelCase_ = cur_pieces[1:]
else:
UpperCamelCase_ = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_SCREAMING_SNAKE_CASE )
else:
new_pieces.append(_SCREAMING_SNAKE_CASE )
return new_pieces
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any] ) -> List[Any]:
"""simple docstring"""
return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE )
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Dict ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = []
UpperCamelCase_ = ""
UpperCamelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token
UpperCamelCase_ = True
UpperCamelCase_ = []
else:
current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = False
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE )
return out_string.strip()
def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase_ = [self.sep_token_id]
UpperCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None , _SCREAMING_SNAKE_CASE: bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is not None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase_ = [self.sep_token_id]
UpperCamelCase_ = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_ = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , "wb" ) as fi:
UpperCamelCase_ = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 356
|
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
_UpperCAmelCase = False
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = 'ybelkada/fonts'
def lowerCAmelCase_ ( ) -> Dict:
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
"Pix2StructImageProcessor. Please upgrade torch." )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
requires_backends(UpperCamelCase_ , ["torch"] )
_check_torch_version()
UpperCamelCase_ = image_tensor.unsqueeze(0 )
UpperCamelCase_ = torch.nn.functional.unfold(UpperCamelCase_ , (patch_height, patch_width) , stride=(patch_height, patch_width) )
UpperCamelCase_ = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , UpperCamelCase_ , UpperCamelCase_ , -1 )
UpperCamelCase_ = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ = 36 , UpperCamelCase_ = "black" , UpperCamelCase_ = "white" , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = 5 , UpperCamelCase_ = None , UpperCamelCase_ = None , ) -> Image.Image:
requires_backends(UpperCamelCase_ , "vision" )
# Add new lines so that each line is no more than 80 characters.
UpperCamelCase_ = textwrap.TextWrapper(width=80 )
UpperCamelCase_ = wrapper.wrap(text=UpperCamelCase_ )
UpperCamelCase_ = "\n".join(UpperCamelCase_ )
if font_bytes is not None and font_path is None:
UpperCamelCase_ = io.BytesIO(UpperCamelCase_ )
elif font_path is not None:
UpperCamelCase_ = font_path
else:
UpperCamelCase_ = hf_hub_download(UpperCamelCase_ , "Arial.TTF" )
UpperCamelCase_ = ImageFont.truetype(UpperCamelCase_ , encoding="UTF-8" , size=UpperCamelCase_ )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
UpperCamelCase_ = ImageDraw.Draw(Image.new("RGB" , (1, 1) , UpperCamelCase_ ) )
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = temp_draw.textbbox((0, 0) , UpperCamelCase_ , UpperCamelCase_ )
# Create the actual image with a bit of padding around the text.
UpperCamelCase_ = text_width + left_padding + right_padding
UpperCamelCase_ = text_height + top_padding + bottom_padding
UpperCamelCase_ = Image.new("RGB" , (image_width, image_height) , UpperCamelCase_ )
UpperCamelCase_ = ImageDraw.Draw(UpperCamelCase_ )
draw.text(xy=(left_padding, top_padding) , text=UpperCamelCase_ , fill=UpperCamelCase_ , font=UpperCamelCase_ )
return image
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_ ) -> Union[str, Any]:
requires_backends(UpperCamelCase_ , "vision" )
# Convert to PIL image if necessary
UpperCamelCase_ = to_pil_image(UpperCamelCase_ )
UpperCamelCase_ = render_text(UpperCamelCase_ , **UpperCamelCase_ )
UpperCamelCase_ = max(header_image.width , image.width )
UpperCamelCase_ = int(image.height * (new_width / image.width) )
UpperCamelCase_ = int(header_image.height * (new_width / header_image.width) )
UpperCamelCase_ = Image.new("RGB" , (new_width, new_height + new_header_height) , "white" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
UpperCamelCase_ = to_numpy_array(UpperCamelCase_ )
if infer_channel_dimension_format(UpperCamelCase_ ) == ChannelDimension.LAST:
UpperCamelCase_ = to_channel_dimension_format(UpperCamelCase_ , ChannelDimension.LAST )
return new_image
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : str = ['''flattened_patches''']
def __init__( self: List[Any] , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Dict[str, int] = None , _SCREAMING_SNAKE_CASE: int = 2048 , _SCREAMING_SNAKE_CASE: bool = False , **_SCREAMING_SNAKE_CASE: Optional[Any] , ) -> None:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = patch_size if patch_size is not None else {"height": 16, "width": 16}
UpperCamelCase_ = do_normalize
UpperCamelCase_ = do_convert_rgb
UpperCamelCase_ = max_patches
UpperCamelCase_ = is_vqa
def lowercase ( self: Dict , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: dict , **_SCREAMING_SNAKE_CASE: Union[str, Any] ) -> np.ndarray:
"""simple docstring"""
requires_backends(self.extract_flattened_patches , "torch" )
_check_torch_version()
# convert to torch
UpperCamelCase_ = to_channel_dimension_format(_SCREAMING_SNAKE_CASE , ChannelDimension.FIRST )
UpperCamelCase_ = torch.from_numpy(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ , UpperCamelCase_ = patch_size["height"], patch_size["width"]
UpperCamelCase_ , UpperCamelCase_ = get_image_size(_SCREAMING_SNAKE_CASE )
# maximize scale s.t.
UpperCamelCase_ = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
UpperCamelCase_ = max(min(math.floor(scale * image_height / patch_height ) , _SCREAMING_SNAKE_CASE ) , 1 )
UpperCamelCase_ = max(min(math.floor(scale * image_width / patch_width ) , _SCREAMING_SNAKE_CASE ) , 1 )
UpperCamelCase_ = max(num_feasible_rows * patch_height , 1 )
UpperCamelCase_ = max(num_feasible_cols * patch_width , 1 )
UpperCamelCase_ = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="bilinear" , align_corners=_SCREAMING_SNAKE_CASE , antialias=_SCREAMING_SNAKE_CASE , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
UpperCamelCase_ = torch_extract_patches(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = patches.shape
UpperCamelCase_ = patches_shape[1]
UpperCamelCase_ = patches_shape[2]
UpperCamelCase_ = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
UpperCamelCase_ = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
UpperCamelCase_ = torch.arange(_SCREAMING_SNAKE_CASE ).reshape([rows, 1] ).repeat(1 , _SCREAMING_SNAKE_CASE ).reshape([rows * columns, 1] )
UpperCamelCase_ = torch.arange(_SCREAMING_SNAKE_CASE ).reshape([1, columns] ).repeat(_SCREAMING_SNAKE_CASE , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
UpperCamelCase_ = row_ids.to(torch.floataa )
UpperCamelCase_ = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
UpperCamelCase_ = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
UpperCamelCase_ = torch.nn.functional.pad(_SCREAMING_SNAKE_CASE , [0, 0, 0, max_patches - (rows * columns)] ).float()
UpperCamelCase_ = to_numpy_array(_SCREAMING_SNAKE_CASE )
return result
def lowercase ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: np.ndarray , _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None , **_SCREAMING_SNAKE_CASE: List[str] ) -> np.ndarray:
"""simple docstring"""
if image.dtype == np.uinta:
UpperCamelCase_ = image.astype(np.floataa )
# take mean across the whole `image`
UpperCamelCase_ = np.mean(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = np.std(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = max(_SCREAMING_SNAKE_CASE , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: ImageInput , _SCREAMING_SNAKE_CASE: Optional[str] = None , _SCREAMING_SNAKE_CASE: bool = None , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[int] = None , _SCREAMING_SNAKE_CASE: Optional[Dict[str, int]] = None , _SCREAMING_SNAKE_CASE: Optional[Union[str, TensorType]] = None , _SCREAMING_SNAKE_CASE: ChannelDimension = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE: List[Any] , ) -> ImageInput:
"""simple docstring"""
UpperCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase_ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCamelCase_ = patch_size if patch_size is not None else self.patch_size
UpperCamelCase_ = max_patches if max_patches is not None else self.max_patches
UpperCamelCase_ = self.is_vqa
if kwargs.get("data_format" , _SCREAMING_SNAKE_CASE ) is not None:
raise ValueError("data_format is not an accepted input as the outputs are " )
UpperCamelCase_ = make_list_of_images(_SCREAMING_SNAKE_CASE )
if not valid_images(_SCREAMING_SNAKE_CASE ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCamelCase_ = [convert_to_rgb(_SCREAMING_SNAKE_CASE ) for image in images]
# All transformations expect numpy arrays.
UpperCamelCase_ = [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("A header text must be provided for VQA models." )
UpperCamelCase_ = kwargs.pop("font_bytes" , _SCREAMING_SNAKE_CASE )
UpperCamelCase_ = kwargs.pop("font_path" , _SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase_ = [header_text] * len(_SCREAMING_SNAKE_CASE )
UpperCamelCase_ = [
render_header(_SCREAMING_SNAKE_CASE , header_text[i] , font_bytes=_SCREAMING_SNAKE_CASE , font_path=_SCREAMING_SNAKE_CASE )
for i, image in enumerate(_SCREAMING_SNAKE_CASE )
]
if do_normalize:
UpperCamelCase_ = [self.normalize(image=_SCREAMING_SNAKE_CASE ) for image in images]
# convert to torch tensor and permute
UpperCamelCase_ = [
self.extract_flattened_patches(image=_SCREAMING_SNAKE_CASE , max_patches=_SCREAMING_SNAKE_CASE , patch_size=_SCREAMING_SNAKE_CASE )
for image in images
]
# create attention mask in numpy
UpperCamelCase_ = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
UpperCamelCase_ = BatchFeature(
data={"flattened_patches": images, "attention_mask": attention_masks} , tensor_type=_SCREAMING_SNAKE_CASE )
return encoded_outputs
| 328
| 0
|
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowercase : Optional[Any] = "src/diffusers"
# Matches is_xxx_available()
_lowercase : Tuple = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
_lowercase : Optional[int] = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
_lowercase : str = "\n{0} = None\n"
_lowercase : List[str] = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, {1})\n"
_lowercase : Union[str, Any] = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
def snake_case__ ( __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ : List[str] =_re_backend.findall(__lowerCamelCase )
if len(__lowerCamelCase ) == 0:
return None
return "_and_".join(__lowerCamelCase )
def snake_case__ ( ):
"""simple docstring"""
with open(os.path.join(__lowerCamelCase , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCamelCase__ : List[str] =f.readlines()
# Get to the point we do the actual imports for type checking
lowerCamelCase__ : Union[str, Any] =0
lowerCamelCase__ : List[str] ={}
# Go through the end of the file
while line_index < len(__lowerCamelCase ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
lowerCamelCase__ : int =find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
lowerCamelCase__ : Optional[Any] =[]
# Until we unindent, add backend objects to the list
while line_index < len(__lowerCamelCase ) and len(lines[line_index] ) > 1:
lowerCamelCase__ : Any =lines[line_index]
lowerCamelCase__ : Any =_re_single_line_import.search(__lowerCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(__lowerCamelCase ) > 0:
lowerCamelCase__ : Tuple =objects
else:
line_index += 1
return backend_specific_objects
def snake_case__ ( __lowerCamelCase : List[str] , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
if name.isupper():
return DUMMY_CONSTANT.format(__lowerCamelCase )
elif name.islower():
return DUMMY_FUNCTION.format(__lowerCamelCase , __lowerCamelCase )
else:
return DUMMY_CLASS.format(__lowerCamelCase , __lowerCamelCase )
def snake_case__ ( __lowerCamelCase : Union[str, Any]=None ):
"""simple docstring"""
if backend_specific_objects is None:
lowerCamelCase__ : Optional[Any] =read_init()
# For special correspondence backend to module name as used in the function requires_modulename
lowerCamelCase__ : int ={}
for backend, objects in backend_specific_objects.items():
lowerCamelCase__ : Optional[int] ='''[''' + ''', '''.join(f'''"{b}"''' for b in backend.split('''_and_''' ) ) + ''']'''
lowerCamelCase__ : Optional[Any] ='''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(__lowerCamelCase , __lowerCamelCase ) for o in objects] )
lowerCamelCase__ : Dict =dummy_file
return dummy_files
def snake_case__ ( __lowerCamelCase : List[Any]=False ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] =create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
lowerCamelCase__ : Optional[Any] ={'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
lowerCamelCase__ : int =os.path.join(__lowerCamelCase , '''utils''' )
lowerCamelCase__ : Union[str, Any] ={
backend: os.path.join(__lowerCamelCase , f'''dummy_{short_names.get(__lowerCamelCase , __lowerCamelCase )}_objects.py''' )
for backend in dummy_files.keys()
}
lowerCamelCase__ : int ={}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(__lowerCamelCase ):
with open(__lowerCamelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowerCamelCase__ : List[str] =f.read()
else:
lowerCamelCase__ : Dict =''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f'''Updating diffusers.utils.dummy_{short_names.get(__lowerCamelCase , __lowerCamelCase )}_objects.py as the main '''
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
f'''diffusers.utils.dummy_{short_names.get(__lowerCamelCase , __lowerCamelCase )}_objects.py. Run `make fix-copies` '''
'''to fix this.''' )
if __name__ == "__main__":
_lowercase : List[Any] = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_lowercase : Tuple = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 238
|
"""simple docstring"""
import argparse
import os
import re
_lowercase : str = "src/diffusers"
# Pattern that looks at the indentation in a line.
_lowercase : List[Any] = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase : int = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_lowercase : Optional[int] = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
_lowercase : List[Any] = re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_lowercase : str = re.compile(r"\[([^\]]+)\]")
def snake_case__ ( __lowerCamelCase : Optional[int] ):
"""simple docstring"""
lowerCamelCase__ : List[str] =_re_indent.search(__lowerCamelCase )
return "" if search is None else search.groups()[0]
def snake_case__ ( __lowerCamelCase : Any , __lowerCamelCase : List[Any]="" , __lowerCamelCase : int=None , __lowerCamelCase : Optional[int]=None ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : Any =code.split('''\n''' )
if start_prompt is not None:
while not lines[index].startswith(__lowerCamelCase ):
index += 1
lowerCamelCase__ : Dict =['''\n'''.join(lines[:index] )]
else:
lowerCamelCase__ : Tuple =[]
# We split into blocks until we get to the `end_prompt` (or the end of the block).
lowerCamelCase__ : int =[lines[index]]
index += 1
while index < len(__lowerCamelCase ) and (end_prompt is None or not lines[index].startswith(__lowerCamelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__lowerCamelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ''' ''' ):
current_block.append(lines[index] )
blocks.append('''\n'''.join(__lowerCamelCase ) )
if index < len(__lowerCamelCase ) - 1:
lowerCamelCase__ : str =[lines[index + 1]]
index += 1
else:
lowerCamelCase__ : str =[]
else:
blocks.append('''\n'''.join(__lowerCamelCase ) )
lowerCamelCase__ : str =[lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__lowerCamelCase ) > 0:
blocks.append('''\n'''.join(__lowerCamelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__lowerCamelCase ):
blocks.append('''\n'''.join(lines[index:] ) )
return blocks
def snake_case__ ( __lowerCamelCase : Any ):
"""simple docstring"""
def _inner(__lowerCamelCase : Any ):
return key(__lowerCamelCase ).lower().replace('''_''' , '''''' )
return _inner
def snake_case__ ( __lowerCamelCase : int , __lowerCamelCase : Any=None ):
"""simple docstring"""
# If no key is provided, we use a noop.
def noop(__lowerCamelCase : List[str] ):
return x
if key is None:
lowerCamelCase__ : Tuple =noop
# Constants are all uppercase, they go first.
lowerCamelCase__ : Union[str, Any] =[obj for obj in objects if key(__lowerCamelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
lowerCamelCase__ : Optional[int] =[obj for obj in objects if key(__lowerCamelCase )[0].isupper() and not key(__lowerCamelCase ).isupper()]
# Functions begin with a lowercase, they go last.
lowerCamelCase__ : Optional[Any] =[obj for obj in objects if not key(__lowerCamelCase )[0].isupper()]
lowerCamelCase__ : int =ignore_underscore(__lowerCamelCase )
return sorted(__lowerCamelCase , key=__lowerCamelCase ) + sorted(__lowerCamelCase , key=__lowerCamelCase ) + sorted(__lowerCamelCase , key=__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
# This inner function sort imports between [ ].
def _replace(__lowerCamelCase : Optional[Any] ):
lowerCamelCase__ : Dict =match.groups()[0]
if "," not in imports:
return f'''[{imports}]'''
lowerCamelCase__ : List[Any] =[part.strip().replace('''"''' , '''''' ) for part in imports.split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCamelCase__ : Optional[int] =keys[:-1]
return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(__lowerCamelCase )] ) + "]"
lowerCamelCase__ : List[Any] =import_statement.split('''\n''' )
if len(__lowerCamelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
lowerCamelCase__ : Tuple =2 if lines[1].strip() == '''[''' else 1
lowerCamelCase__ : Any =[(i, _re_strip_line.search(__lowerCamelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
lowerCamelCase__ : List[Any] =sort_objects(__lowerCamelCase , key=lambda __lowerCamelCase : x[1] )
lowerCamelCase__ : Union[str, Any] =[lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__lowerCamelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
lowerCamelCase__ : List[str] =_re_bracket_content.sub(_replace , lines[1] )
else:
lowerCamelCase__ : str =[part.strip().replace('''"''' , '''''' ) for part in lines[1].split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCamelCase__ : Any =keys[:-1]
lowerCamelCase__ : List[Any] =get_indent(lines[1] ) + ''', '''.join([f'''"{k}"''' for k in sort_objects(__lowerCamelCase )] )
return "\n".join(__lowerCamelCase )
else:
# Finally we have to deal with imports fitting on one line
lowerCamelCase__ : Union[str, Any] =_re_bracket_content.sub(_replace , __lowerCamelCase )
return import_statement
def snake_case__ ( __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any]=True ):
"""simple docstring"""
with open(__lowerCamelCase , '''r''' ) as f:
lowerCamelCase__ : Optional[int] =f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
lowerCamelCase__ : int =split_code_in_indented_blocks(
__lowerCamelCase , start_prompt='''_import_structure = {''' , end_prompt='''if TYPE_CHECKING:''' )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__lowerCamelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
lowerCamelCase__ : Optional[Any] =main_blocks[block_idx]
lowerCamelCase__ : List[str] =block.split('''\n''' )
# Get to the start of the imports.
lowerCamelCase__ : Any =0
while line_idx < len(__lowerCamelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
lowerCamelCase__ : Tuple =len(__lowerCamelCase )
else:
line_idx += 1
if line_idx >= len(__lowerCamelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
lowerCamelCase__ : Any ='''\n'''.join(block_lines[line_idx:-1] )
lowerCamelCase__ : Dict =get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
lowerCamelCase__ : List[Any] =split_code_in_indented_blocks(__lowerCamelCase , indent_level=__lowerCamelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
lowerCamelCase__ : List[Any] =_re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
lowerCamelCase__ : Union[str, Any] =[(pattern.search(__lowerCamelCase ).groups()[0] if pattern.search(__lowerCamelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
lowerCamelCase__ : Optional[Any] =[(i, key) for i, key in enumerate(__lowerCamelCase ) if key is not None]
lowerCamelCase__ : List[Any] =[x[0] for x in sorted(__lowerCamelCase , key=lambda __lowerCamelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
lowerCamelCase__ : Optional[Any] =0
lowerCamelCase__ : Tuple =[]
for i in range(len(__lowerCamelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
lowerCamelCase__ : List[Any] =sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__lowerCamelCase )
count += 1
# And we put our main block back together with its first and last line.
lowerCamelCase__ : str ='''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__lowerCamelCase ):
if check_only:
return True
else:
print(f'''Overwriting {file}.''' )
with open(__lowerCamelCase , '''w''' ) as f:
f.write('''\n'''.join(__lowerCamelCase ) )
def snake_case__ ( __lowerCamelCase : Optional[Any]=True ):
"""simple docstring"""
lowerCamelCase__ : Any =[]
for root, _, files in os.walk(__lowerCamelCase ):
if "__init__.py" in files:
lowerCamelCase__ : Tuple =sort_imports(os.path.join(__lowerCamelCase , '''__init__.py''' ) , check_only=__lowerCamelCase )
if result:
lowerCamelCase__ : List[str] =[os.path.join(__lowerCamelCase , '''__init__.py''' )]
if len(__lowerCamelCase ) > 0:
raise ValueError(f'''Would overwrite {len(__lowerCamelCase )} files, run `make style`.''' )
if __name__ == "__main__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
_lowercase : List[Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 238
| 1
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE (self : Any ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[Any] = torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
snake_case : Union[str, Any] = get_activation("gelu" )
self.assertTrue(torch.allclose(gelu_python(snake_case__ ) , torch_builtin(snake_case__ ) ) )
self.assertFalse(torch.allclose(gelu_python(snake_case__ ) , gelu_new(snake_case__ ) ) )
def _SCREAMING_SNAKE_CASE (self : Tuple ) -> int:
'''simple docstring'''
snake_case : str = torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
snake_case : str = get_activation("gelu" )
snake_case : Union[str, Any] = get_activation("gelu_10" )
snake_case : str = torch_builtin(snake_case__ )
snake_case : Optional[int] = geluaa(snake_case__ )
snake_case : Optional[int] = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(snake_case__ ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def _SCREAMING_SNAKE_CASE (self : Optional[Any] ) -> Dict:
'''simple docstring'''
get_activation("gelu" )
get_activation("gelu_10" )
get_activation("gelu_fast" )
get_activation("gelu_new" )
get_activation("gelu_python" )
get_activation("gelu_pytorch_tanh" )
get_activation("linear" )
get_activation("mish" )
get_activation("quick_gelu" )
get_activation("relu" )
get_activation("sigmoid" )
get_activation("silu" )
get_activation("swish" )
get_activation("tanh" )
with self.assertRaises(snake_case__ ):
get_activation("bogus" )
with self.assertRaises(snake_case__ ):
get_activation(snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Any ) -> str:
'''simple docstring'''
snake_case : int = get_activation("gelu" )
snake_case : str = 1
snake_case : Tuple = get_activation("gelu" )
self.assertEqual(acta.a , 1 )
with self.assertRaises(snake_case__ ):
snake_case : Tuple = acta.a
| 355
|
def UpperCamelCase ( __lowerCamelCase : str , __lowerCamelCase : str ):
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError("String lengths must match!" )
snake_case : Optional[Any] = 0
for chara, chara in zip(__lowerCamelCase , __lowerCamelCase ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10
| 0
|
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class a_ ( snake_case_ ):
'''simple docstring'''
def snake_case_( self ) -> Tuple:
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def snake_case_( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = {"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
return Dataset.from_dict(A )
def snake_case_( self ) -> str:
_SCREAMING_SNAKE_CASE = self._create_example_records()
_SCREAMING_SNAKE_CASE = Dataset.from_list(A )
self.assertListEqual(dset.column_names , ["""col_1""", """col_2"""] )
for i, r in enumerate(A ):
self.assertDictEqual(A , example_records[i] )
def snake_case_( self ) -> str:
_SCREAMING_SNAKE_CASE = self._create_example_records()
_SCREAMING_SNAKE_CASE = Dataset.from_list(A )
_SCREAMING_SNAKE_CASE = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def snake_case_( self ) -> Union[str, Any]: # checks what happens with missing columns
_SCREAMING_SNAKE_CASE = [{"""col_1""": 1}, {"""col_2""": """x"""}]
_SCREAMING_SNAKE_CASE = Dataset.from_list(A )
self.assertDictEqual(dset[0] , {"""col_1""": 1} )
self.assertDictEqual(dset[1] , {"""col_1""": None} ) # NB: first record is used for columns
def snake_case_( self ) -> Optional[Any]: # checks if the type can be inferred from the second record
_SCREAMING_SNAKE_CASE = [{"""col_1""": []}, {"""col_1""": [1, 2]}]
_SCREAMING_SNAKE_CASE = Dataset.from_list(A )
self.assertEqual(dset.info.features["""col_1"""] , Sequence(Value("""int64""" ) ) )
def snake_case_( self ) -> str:
_SCREAMING_SNAKE_CASE = Dataset.from_list([] )
self.assertEqual(len(A ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 58
|
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __SCREAMING_SNAKE_CASE (*SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = list(SCREAMING_SNAKE_CASE__ )
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
snake_case_ = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = [
'''CUDA out of memory.''', # CUDA OOM
'''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''', # CUDNN SNAFU
'''DefaultCPUAllocator: can\'t allocate memory''', # CPU OOM
]
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 128 ):
if function is None:
return functools.partial(SCREAMING_SNAKE_CASE__ , starting_batch_size=SCREAMING_SNAKE_CASE__ )
snake_case_ = starting_batch_size
def decorator(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
snake_case_ = list(inspect.signature(SCREAMING_SNAKE_CASE__ ).parameters.keys() )
# Guard against user error
if len(SCREAMING_SNAKE_CASE__ ) < (len(SCREAMING_SNAKE_CASE__ ) + 1):
snake_case_ = ''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''' )
while True:
if batch_size == 0:
raise RuntimeError('''No executable batch size found, reached zero.''' )
try:
return function(SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
except Exception as e:
if should_reduce_batch_size(SCREAMING_SNAKE_CASE__ ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 8
| 0
|
'''simple docstring'''
from __future__ import annotations
_SCREAMING_SNAKE_CASE = list[tuple[int, int]]
_SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_SCREAMING_SNAKE_CASE = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int] , __snake_case : int , __snake_case : int , __snake_case : int , __snake_case : int , __snake_case : float , __snake_case : Node | None , )-> Union[str, Any]:
snake_case = pos_x
snake_case = pos_y
snake_case = (pos_y, pos_x)
snake_case = goal_x
snake_case = goal_y
snake_case = g_cost
snake_case = parent
snake_case = self.calculate_heuristic()
def lowerCAmelCase ( self : List[Any] )-> float:
snake_case = abs(self.pos_x - self.goal_x )
snake_case = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self : Any , __snake_case : Union[str, Any] )-> bool:
return self.f_cost < other.f_cost
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[str] , __snake_case : tuple[int, int] , __snake_case : tuple[int, int] )-> Optional[Any]:
snake_case = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , __snake_case )
snake_case = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_99_99 , __snake_case )
snake_case = [self.start]
snake_case = []
snake_case = False
def lowerCAmelCase ( self : Optional[int] )-> Path | None:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
snake_case = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
snake_case = True
return self.retrace_path(__snake_case )
self.closed_nodes.append(__snake_case )
snake_case = self.get_successors(__snake_case )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(__snake_case )
else:
# retrieve the best current path
snake_case = self.open_nodes.pop(self.open_nodes.index(__snake_case ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(__snake_case )
else:
self.open_nodes.append(__snake_case )
if not self.reached:
return [self.start.pos]
return None
def lowerCAmelCase ( self : str , __snake_case : Node )-> list[Node]:
snake_case = []
for action in delta:
snake_case = parent.pos_x + action[1]
snake_case = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__snake_case ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
__snake_case , __snake_case , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , __snake_case , ) )
return successors
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : Node | None )-> Path:
snake_case = node
snake_case = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
snake_case = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = (0, 0)
_SCREAMING_SNAKE_CASE = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
_SCREAMING_SNAKE_CASE = GreedyBestFirst(init, goal)
_SCREAMING_SNAKE_CASE = greedy_bf.search()
if path:
for pos_x, pos_y in path:
_SCREAMING_SNAKE_CASE = 2
for elem in grid:
print(elem)
| 3
|
'''simple docstring'''
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , __snake_case : int , __snake_case : Optional[Any]=None , __snake_case : int=None )-> str:
snake_case = data
snake_case = previous
snake_case = next_node
def __str__( self : Union[str, Any] )-> str:
return f'''{self.data}'''
def lowerCAmelCase ( self : Tuple )-> int:
return self.data
def lowerCAmelCase ( self : str )-> str:
return self.next
def lowerCAmelCase ( self : Dict )-> Optional[int]:
return self.previous
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : int , __snake_case : List[Any] )-> List[str]:
snake_case = head
def __iter__( self : Optional[int] )-> Dict:
return self
def lowerCAmelCase ( self : Optional[Any] )-> List[str]:
if not self.current:
raise StopIteration
else:
snake_case = self.current.get_data()
snake_case = self.current.get_next()
return value
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] )-> str:
snake_case = None # First node in list
snake_case = None # Last node in list
def __str__( self : List[str] )-> Any:
snake_case = self.head
snake_case = []
while current is not None:
nodes.append(current.get_data() )
snake_case = current.get_next()
return " ".join(str(__snake_case ) for node in nodes )
def __contains__( self : Optional[Any] , __snake_case : int )-> Optional[Any]:
snake_case = self.head
while current:
if current.get_data() == value:
return True
snake_case = current.get_next()
return False
def __iter__( self : Dict )-> List[Any]:
return LinkedListIterator(self.head )
def lowerCAmelCase ( self : Tuple )-> int:
if self.head:
return self.head.get_data()
return None
def lowerCAmelCase ( self : Dict )-> Optional[Any]:
if self.tail:
return self.tail.get_data()
return None
def lowerCAmelCase ( self : List[Any] , __snake_case : Node )-> None:
if self.head is None:
snake_case = node
snake_case = node
else:
self.insert_before_node(self.head , __snake_case )
def lowerCAmelCase ( self : int , __snake_case : Node )-> None:
if self.head is None:
self.set_head(__snake_case )
else:
self.insert_after_node(self.tail , __snake_case )
def lowerCAmelCase ( self : str , __snake_case : int )-> None:
snake_case = Node(__snake_case )
if self.head is None:
self.set_head(__snake_case )
else:
self.set_tail(__snake_case )
def lowerCAmelCase ( self : List[Any] , __snake_case : Node , __snake_case : Node )-> None:
snake_case = node
snake_case = node.previous
if node.get_previous() is None:
snake_case = node_to_insert
else:
snake_case = node_to_insert
snake_case = node_to_insert
def lowerCAmelCase ( self : Optional[int] , __snake_case : Node , __snake_case : Node )-> None:
snake_case = node
snake_case = node.next
if node.get_next() is None:
snake_case = node_to_insert
else:
snake_case = node_to_insert
snake_case = node_to_insert
def lowerCAmelCase ( self : int , __snake_case : int , __snake_case : int )-> None:
snake_case = 1
snake_case = Node(__snake_case )
snake_case = self.head
while node:
if current_position == position:
self.insert_before_node(__snake_case , __snake_case )
return
current_position += 1
snake_case = node.next
self.insert_after_node(self.tail , __snake_case )
def lowerCAmelCase ( self : str , __snake_case : int )-> Node:
snake_case = self.head
while node:
if node.get_data() == item:
return node
snake_case = node.get_next()
raise Exception("""Node not found""" )
def lowerCAmelCase ( self : Any , __snake_case : Dict )-> Tuple:
if (node := self.get_node(__snake_case )) is not None:
if node == self.head:
snake_case = self.head.get_next()
if node == self.tail:
snake_case = self.tail.get_previous()
self.remove_node_pointers(__snake_case )
@staticmethod
def lowerCAmelCase ( __snake_case : Node )-> None:
if node.get_next():
snake_case = node.previous
if node.get_previous():
snake_case = node.next
snake_case = None
snake_case = None
def lowerCAmelCase ( self : List[Any] )-> Optional[Any]:
return self.head is None
def __lowerCamelCase ( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 3
| 1
|
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowercase__ :int = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportWarning(
"To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
"To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
"If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
lowercase__ :Tuple = concatenate_datasets
lowercase__ :List[str] = DownloadConfig
lowercase__ :Optional[int] = DownloadManager
lowercase__ :Optional[int] = DownloadMode
lowercase__ :Any = DownloadConfig
lowercase__ :str = DownloadMode
lowercase__ :List[str] = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 101
|
'''simple docstring'''
from __future__ import annotations
from random import choice
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
return choice(__a )
def UpperCAmelCase_ (__a : list[int] , __a : int ):
"""simple docstring"""
_a : Dict = random_pivot(__a )
# partition based on pivot
# linear time
_a : Optional[int] = [e for e in lst if e < pivot]
_a : List[str] = [e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(__a ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(__a ) < k - 1:
return kth_number(__a , k - len(__a ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(__a , __a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 271
| 0
|
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ):
"""simple docstring"""
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
A = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(lowercase__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354
|
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
"""simple docstring"""
# Load configuration defined in the metadata file
with open(lowercase__ ) as metadata_file:
A = json.load(lowercase__ )
A = LukeConfig(use_entity_aware_attention=lowercase__ , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
A = torch.load(lowercase__ , map_location="cpu" )
# Load the entity vocab file
A = load_entity_vocab(lowercase__ )
A = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
A = AddedToken("<ent>" , lstrip=lowercase__ , rstrip=lowercase__ )
A = AddedToken("<ent2>" , lstrip=lowercase__ , rstrip=lowercase__ )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(lowercase__ )
with open(os.path.join(lowercase__ , LukeTokenizer.vocab_files_names["entity_vocab_file"] ) , "w" ) as f:
json.dump(lowercase__ , lowercase__ )
A = LukeTokenizer.from_pretrained(lowercase__ )
# Initialize the embeddings of the special tokens
A = state_dict["embeddings.word_embeddings.weight"]
A = word_emb[tokenizer.convert_tokens_to_ids(["@"] )[0]].unsqueeze(0 )
A = word_emb[tokenizer.convert_tokens_to_ids(["#"] )[0]].unsqueeze(0 )
A = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
A = F"""encoder.layer.{layer_index}.attention.self."""
A = state_dict[prefix + matrix_name]
A = state_dict[prefix + matrix_name]
A = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
A = state_dict["entity_embeddings.entity_embeddings.weight"]
A = entity_emb[entity_vocab["[MASK]"]]
A = LukeModel(config=lowercase__ ).eval()
A , A = model.load_state_dict(lowercase__ , strict=lowercase__ )
if not (len(lowercase__ ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(F"""Missing keys {", ".join(lowercase__ )}. Expected only missing embeddings.position_ids""" )
if not (all(key.startswith("entity_predictions" ) or key.startswith("lm_head" ) for key in unexpected_keys )):
raise ValueError(
"Unexpected keys"
F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}""" )
# Check outputs
A = LukeTokenizer.from_pretrained(lowercase__ , task="entity_classification" )
A = (
"Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
" new world number one avoid a humiliating second- round exit at Wimbledon ."
)
A = (39, 42)
A = tokenizer(lowercase__ , entity_spans=[span] , add_prefix_space=lowercase__ , return_tensors="pt" )
A = model(**lowercase__ )
# Verify word hidden states
if model_size == "large":
A = torch.Size((1, 42, 1_024) )
A = torch.tensor(
[[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]] )
else: # base
A = torch.Size((1, 42, 768) )
A = torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowercase__ , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
A = torch.Size((1, 1, 1_024) )
A = torch.tensor([[0.04_66, -0.01_06, -0.01_79]] )
else: # base
A = torch.Size((1, 1, 768) )
A = torch.tensor([[0.14_57, 0.10_44, 0.01_74]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
F""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , lowercase__ , atol=1e-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(lowercase__ ) )
model.save_pretrained(lowercase__ )
def __SCREAMING_SNAKE_CASE ( lowercase__ ):
"""simple docstring"""
A = {}
with open(lowercase__ , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(lowercase__ ):
A , A = line.rstrip().split("\t" )
A = index
return entity_vocab
if __name__ == "__main__":
__A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
__A : int = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 57
| 0
|
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
__snake_case =logging.get_logger(__name__)
# General docstring
__snake_case ="""RegNetConfig"""
# Base docstring
__snake_case ="""facebook/regnet-y-040"""
__snake_case =[1, 1_088, 7, 7]
# Image classification docstring
__snake_case ="""facebook/regnet-y-040"""
__snake_case ="""tabby, tabby cat"""
__snake_case =[
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 3 , UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : Optional[str] = "relu" , ) -> Any:
super().__init__()
lowerCAmelCase = nn.Convad(
UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=UpperCAmelCase__ , stride=UpperCAmelCase__ , padding=kernel_size // 2 , groups=UpperCAmelCase__ , bias=UpperCAmelCase__ , )
lowerCAmelCase = nn.BatchNormad(UpperCAmelCase__ )
lowerCAmelCase = ACTaFN[activation] if activation is not None else nn.Identity()
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Tuple ) -> Tuple:
lowerCAmelCase = self.convolution(UpperCAmelCase__ )
lowerCAmelCase = self.normalization(UpperCAmelCase__ )
lowerCAmelCase = self.activation(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Optional[int] , UpperCAmelCase__ : RegNetConfig ) -> Tuple:
super().__init__()
lowerCAmelCase = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
lowerCAmelCase = config.num_channels
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any] ) -> Any:
lowerCAmelCase = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
lowerCAmelCase = self.embedder(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 2 ) -> Optional[int]:
super().__init__()
lowerCAmelCase = nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , stride=UpperCAmelCase__ , bias=UpperCAmelCase__ )
lowerCAmelCase = nn.BatchNormad(UpperCAmelCase__ )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Tensor ) -> Tensor:
lowerCAmelCase = self.convolution(UpperCAmelCase__ )
lowerCAmelCase = self.normalization(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> Union[str, Any]:
super().__init__()
lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
lowerCAmelCase = nn.Sequential(
nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 ) , nn.ReLU() , nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 ) , nn.Sigmoid() , )
def __UpperCAmelCase ( self : Tuple , UpperCAmelCase__ : str ) -> Optional[Any]:
# b c h w -> b c 1 1
lowerCAmelCase = self.pooler(UpperCAmelCase__ )
lowerCAmelCase = self.attention(UpperCAmelCase__ )
lowerCAmelCase = hidden_state * attention
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Optional[int] , UpperCAmelCase__ : RegNetConfig , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 1 ) -> Optional[int]:
super().__init__()
lowerCAmelCase = in_channels != out_channels or stride != 1
lowerCAmelCase = max(1 , out_channels // config.groups_width )
lowerCAmelCase = (
RegNetShortCut(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
lowerCAmelCase = nn.Sequential(
RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , groups=UpperCAmelCase__ , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=UpperCAmelCase__ ) , )
lowerCAmelCase = ACTaFN[config.hidden_act]
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Any ) -> Union[str, Any]:
lowerCAmelCase = hidden_state
lowerCAmelCase = self.layer(UpperCAmelCase__ )
lowerCAmelCase = self.shortcut(UpperCAmelCase__ )
hidden_state += residual
lowerCAmelCase = self.activation(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : str , UpperCAmelCase__ : RegNetConfig , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 1 ) -> Optional[Any]:
super().__init__()
lowerCAmelCase = in_channels != out_channels or stride != 1
lowerCAmelCase = max(1 , out_channels // config.groups_width )
lowerCAmelCase = (
RegNetShortCut(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
lowerCAmelCase = nn.Sequential(
RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , groups=UpperCAmelCase__ , activation=config.hidden_act ) , RegNetSELayer(UpperCAmelCase__ , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=UpperCAmelCase__ ) , )
lowerCAmelCase = ACTaFN[config.hidden_act]
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Union[str, Any] ) -> Tuple:
lowerCAmelCase = hidden_state
lowerCAmelCase = self.layer(UpperCAmelCase__ )
lowerCAmelCase = self.shortcut(UpperCAmelCase__ )
hidden_state += residual
lowerCAmelCase = self.activation(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : RegNetConfig , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 2 , ) -> Optional[Any]:
super().__init__()
lowerCAmelCase = RegNetXLayer if config.layer_type == 'x' else RegNetYLayer
lowerCAmelCase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , ) , *[layer(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) for _ in range(depth - 1 )] , )
def __UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase__ : List[str] ) -> Tuple:
lowerCAmelCase = self.layers(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Any , UpperCAmelCase__ : RegNetConfig ) -> Dict:
super().__init__()
lowerCAmelCase = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
UpperCAmelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowerCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(UpperCAmelCase__ , config.depths[1:] ):
self.stages.append(RegNetStage(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , depth=UpperCAmelCase__ ) )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : bool = False , UpperCAmelCase__ : bool = True ) -> BaseModelOutputWithNoAttention:
lowerCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowerCAmelCase = hidden_states + (hidden_state,)
lowerCAmelCase = stage_module(UpperCAmelCase__ )
if output_hidden_states:
lowerCAmelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=UpperCAmelCase__ , hidden_states=UpperCAmelCase__ )
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : List[Any] = RegNetConfig
lowerCamelCase : Any = '''regnet'''
lowerCamelCase : Any = '''pixel_values'''
lowerCamelCase : Union[str, Any] = True
def __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : int ) -> Optional[int]:
if isinstance(UpperCAmelCase__ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode='fan_out' , nonlinearity='relu' )
elif isinstance(UpperCAmelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def __UpperCAmelCase ( self : str , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Tuple=False ) -> Any:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowerCAmelCase = value
__snake_case =R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
__snake_case =R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''The bare RegNet model outputting raw features without any specific head on top.''' , __lowercase , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : List[str] , UpperCAmelCase__ : Optional[int] ) -> List[Any]:
super().__init__(UpperCAmelCase__ )
lowerCAmelCase = config
lowerCAmelCase = RegNetEmbeddings(UpperCAmelCase__ )
lowerCAmelCase = RegNetEncoder(UpperCAmelCase__ )
lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCAmelCase__ , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
lowerCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase = self.embedder(UpperCAmelCase__ )
lowerCAmelCase = self.encoder(
UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , return_dict=UpperCAmelCase__ )
lowerCAmelCase = encoder_outputs[0]
lowerCAmelCase = self.pooler(UpperCAmelCase__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCAmelCase__ , pooler_output=UpperCAmelCase__ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'''
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , __lowercase , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : Optional[int] , UpperCAmelCase__ : Union[str, Any] ) -> str:
super().__init__(UpperCAmelCase__ )
lowerCAmelCase = config.num_labels
lowerCAmelCase = RegNetModel(UpperCAmelCase__ )
# classification head
lowerCAmelCase = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCAmelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __UpperCAmelCase ( self : int , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[torch.LongTensor] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[bool] = None , ) -> ImageClassifierOutputWithNoAttention:
lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase = self.regnet(UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , return_dict=UpperCAmelCase__ )
lowerCAmelCase = outputs.pooler_output if return_dict else outputs[1]
lowerCAmelCase = self.classifier(UpperCAmelCase__ )
lowerCAmelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowerCAmelCase = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowerCAmelCase = 'single_label_classification'
else:
lowerCAmelCase = 'multi_label_classification'
if self.config.problem_type == "regression":
lowerCAmelCase = MSELoss()
if self.num_labels == 1:
lowerCAmelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowerCAmelCase = loss_fct(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.config.problem_type == "single_label_classification":
lowerCAmelCase = CrossEntropyLoss()
lowerCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowerCAmelCase = BCEWithLogitsLoss()
lowerCAmelCase = loss_fct(UpperCAmelCase__ , UpperCAmelCase__ )
if not return_dict:
lowerCAmelCase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=UpperCAmelCase__ , logits=UpperCAmelCase__ , hidden_states=outputs.hidden_states )
| 4
|
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowercase = datasets.utils.logging.get_logger(__name__)
@dataclass
class _A ( datasets.BuilderConfig ):
"""simple docstring"""
UpperCAmelCase : int = 1_0_0_0_0
UpperCAmelCase : Optional[List[str]] = None
UpperCAmelCase : Optional[datasets.Features] = None
class _A ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
UpperCAmelCase : str = ParquetConfig
def __snake_case ( self : Tuple):
return datasets.DatasetInfo(features=self.config.features)
def __snake_case ( self : List[Any] , __UpperCAmelCase : str):
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''')
a : str = dl_manager.download_and_extract(self.config.data_files)
if isinstance(__UpperCAmelCase , (str, list, tuple)):
a : Dict = data_files
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
a : str = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
a : List[Any] = [dl_manager.iter_files(__UpperCAmelCase) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files})]
a : Dict = []
for split_name, files in data_files.items():
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
a : Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
a : Tuple = [dl_manager.iter_files(__UpperCAmelCase) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(__UpperCAmelCase):
with open(__UpperCAmelCase , "rb") as f:
a : Tuple = datasets.Features.from_arrow_schema(pq.read_schema(__UpperCAmelCase))
break
splits.append(datasets.SplitGenerator(name=__UpperCAmelCase , gen_kwargs={"files": files}))
return splits
def __snake_case ( self : List[str] , __UpperCAmelCase : pa.Table):
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
a : Optional[int] = table_cast(__UpperCAmelCase , self.info.features.arrow_schema)
return pa_table
def __snake_case ( self : Union[str, Any] , __UpperCAmelCase : int):
a : Tuple = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema) != sorted(self.config.columns):
raise ValueError(
f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'''')
for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase)):
with open(__UpperCAmelCase , "rb") as f:
a : Tuple = pq.ParquetFile(__UpperCAmelCase)
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns)):
a : Optional[Any] = pa.Table.from_batches([record_batch])
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f'''{file_idx}_{batch_idx}''', self._cast_table(__UpperCAmelCase)
except ValueError as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(__UpperCAmelCase)}: {e}''')
raise
| 40
| 0
|
def UpperCamelCase ( __lowercase : float ):
'''simple docstring'''
if edge <= 0 or not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def UpperCamelCase ( __lowercase : float ):
'''simple docstring'''
if edge <= 0 or not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
raise ValueError('Length must be a positive.' )
return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366
|
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase ( __A ):
'''simple docstring'''
def __init__( self , *lowercase , **lowercase ):
"""simple docstring"""
warnings.warn(
'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use DonutImageProcessor instead.' , lowercase , )
super().__init__(*lowercase , **lowercase )
| 192
| 0
|
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
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
"""google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
"""google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
"""google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE : str = '''mobilenet_v2'''
def __init__( self , snake_case=3 , snake_case=224 , snake_case=1.0 , snake_case=8 , snake_case=8 , snake_case=6 , snake_case=32 , snake_case=True , snake_case=True , snake_case="relu6" , snake_case=True , snake_case=0.8 , snake_case=0.02 , snake_case=0.0_01 , snake_case=255 , **snake_case , ):
super().__init__(**snake_case )
if depth_multiplier <= 0:
raise ValueError('depth_multiplier must be greater than zero.' )
snake_case_ = num_channels
snake_case_ = image_size
snake_case_ = depth_multiplier
snake_case_ = depth_divisible_by
snake_case_ = min_depth
snake_case_ = expand_ratio
snake_case_ = output_stride
snake_case_ = first_layer_is_expansion
snake_case_ = finegrained_output
snake_case_ = hidden_act
snake_case_ = tf_padding
snake_case_ = classifier_dropout_prob
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = semantic_loss_ignore_index
class lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE : str = version.parse('''1.11''' )
@property
def a ( self ):
return OrderedDict([('pixel_values', {0: 'batch'})] )
@property
def a ( self ):
if self.task == "image-classification":
return OrderedDict([('logits', {0: 'batch'})] )
else:
return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})] )
@property
def a ( self ):
return 1e-4
| 285
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
# General docstring
_UpperCAmelCase : Dict = """ResNetConfig"""
# Base docstring
_UpperCAmelCase : Optional[int] = """microsoft/resnet-50"""
_UpperCAmelCase : Optional[Any] = [1, 2048, 7, 7]
# Image classification docstring
_UpperCAmelCase : Tuple = """microsoft/resnet-50"""
_UpperCAmelCase : int = """tiger cat"""
_UpperCAmelCase : Optional[Any] = [
"""microsoft/resnet-50""",
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class lowercase ( nn.Module ):
def __init__( self , snake_case , snake_case , snake_case = 3 , snake_case = 1 , snake_case = "relu" ):
super().__init__()
snake_case_ = nn.Convad(
snake_case , snake_case , kernel_size=snake_case , stride=snake_case , padding=kernel_size // 2 , bias=snake_case )
snake_case_ = nn.BatchNormad(snake_case )
snake_case_ = ACTaFN[activation] if activation is not None else nn.Identity()
def a ( self , snake_case ):
snake_case_ = self.convolution(snake_case )
snake_case_ = self.normalization(snake_case )
snake_case_ = self.activation(snake_case )
return hidden_state
class lowercase ( nn.Module ):
def __init__( self , snake_case ):
super().__init__()
snake_case_ = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
snake_case_ = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
snake_case_ = config.num_channels
def a ( self , snake_case ):
snake_case_ = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
snake_case_ = self.embedder(snake_case )
snake_case_ = self.pooler(snake_case )
return embedding
class lowercase ( nn.Module ):
def __init__( self , snake_case , snake_case , snake_case = 2 ):
super().__init__()
snake_case_ = nn.Convad(snake_case , snake_case , kernel_size=1 , stride=snake_case , bias=snake_case )
snake_case_ = nn.BatchNormad(snake_case )
def a ( self , snake_case ):
snake_case_ = self.convolution(snake_case )
snake_case_ = self.normalization(snake_case )
return hidden_state
class lowercase ( nn.Module ):
def __init__( self , snake_case , snake_case , snake_case = 1 , snake_case = "relu" ):
super().__init__()
snake_case_ = in_channels != out_channels or stride != 1
snake_case_ = (
ResNetShortCut(snake_case , snake_case , stride=snake_case ) if should_apply_shortcut else nn.Identity()
)
snake_case_ = nn.Sequential(
ResNetConvLayer(snake_case , snake_case , stride=snake_case ) , ResNetConvLayer(snake_case , snake_case , activation=snake_case ) , )
snake_case_ = ACTaFN[activation]
def a ( self , snake_case ):
snake_case_ = hidden_state
snake_case_ = self.layer(snake_case )
snake_case_ = self.shortcut(snake_case )
hidden_state += residual
snake_case_ = self.activation(snake_case )
return hidden_state
class lowercase ( nn.Module ):
def __init__( self , snake_case , snake_case , snake_case = 1 , snake_case = "relu" , snake_case = 4 ):
super().__init__()
snake_case_ = in_channels != out_channels or stride != 1
snake_case_ = out_channels // reduction
snake_case_ = (
ResNetShortCut(snake_case , snake_case , stride=snake_case ) if should_apply_shortcut else nn.Identity()
)
snake_case_ = nn.Sequential(
ResNetConvLayer(snake_case , snake_case , kernel_size=1 ) , ResNetConvLayer(snake_case , snake_case , stride=snake_case ) , ResNetConvLayer(snake_case , snake_case , kernel_size=1 , activation=snake_case ) , )
snake_case_ = ACTaFN[activation]
def a ( self , snake_case ):
snake_case_ = hidden_state
snake_case_ = self.layer(snake_case )
snake_case_ = self.shortcut(snake_case )
hidden_state += residual
snake_case_ = self.activation(snake_case )
return hidden_state
class lowercase ( nn.Module ):
def __init__( self , snake_case , snake_case , snake_case , snake_case = 2 , snake_case = 2 , ):
super().__init__()
snake_case_ = ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer
snake_case_ = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(snake_case , snake_case , stride=snake_case , activation=config.hidden_act ) , *[layer(snake_case , snake_case , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def a ( self , snake_case ):
snake_case_ = input
for layer in self.layers:
snake_case_ = layer(snake_case )
return hidden_state
class lowercase ( nn.Module ):
def __init__( self , snake_case ):
super().__init__()
snake_case_ = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
snake_case , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
snake_case_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(snake_case , config.depths[1:] ):
self.stages.append(ResNetStage(snake_case , snake_case , snake_case , depth=snake_case ) )
def a ( self , snake_case , snake_case = False , snake_case = True ):
snake_case_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
snake_case_ = hidden_states + (hidden_state,)
snake_case_ = stage_module(snake_case )
if output_hidden_states:
snake_case_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=snake_case , hidden_states=snake_case , )
class lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE : List[str] = ResNetConfig
__SCREAMING_SNAKE_CASE : Any = '''resnet'''
__SCREAMING_SNAKE_CASE : int = '''pixel_values'''
__SCREAMING_SNAKE_CASE : Tuple = True
def a ( self , snake_case ):
if isinstance(snake_case , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode='fan_out' , nonlinearity='relu' )
elif isinstance(snake_case , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def a ( self , snake_case , snake_case=False ):
if isinstance(snake_case , snake_case ):
snake_case_ = value
_UpperCAmelCase : Tuple = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
_UpperCAmelCase : Optional[int] = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''The bare ResNet model outputting raw features without any specific head on top.''' , lowercase_ , )
class lowercase ( lowercase_ ):
def __init__( self , snake_case ):
super().__init__(snake_case )
snake_case_ = config
snake_case_ = ResNetEmbeddings(snake_case )
snake_case_ = ResNetEncoder(snake_case )
snake_case_ = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=snake_case , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def a ( self , snake_case , snake_case = None , snake_case = None ):
snake_case_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ = self.embedder(snake_case )
snake_case_ = self.encoder(
snake_case , output_hidden_states=snake_case , return_dict=snake_case )
snake_case_ = encoder_outputs[0]
snake_case_ = self.pooler(snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=snake_case , pooler_output=snake_case , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'''
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , lowercase_ , )
class lowercase ( lowercase_ ):
def __init__( self , snake_case ):
super().__init__(snake_case )
snake_case_ = config.num_labels
snake_case_ = ResNetModel(snake_case )
# classification head
snake_case_ = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=snake_case , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def a ( self , snake_case = None , snake_case = None , snake_case = None , snake_case = None , ):
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ = self.resnet(snake_case , output_hidden_states=snake_case , return_dict=snake_case )
snake_case_ = outputs.pooler_output if return_dict else outputs[1]
snake_case_ = self.classifier(snake_case )
snake_case_ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
snake_case_ = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
snake_case_ = 'single_label_classification'
else:
snake_case_ = 'multi_label_classification'
if self.config.problem_type == "regression":
snake_case_ = MSELoss()
if self.num_labels == 1:
snake_case_ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
snake_case_ = loss_fct(snake_case , snake_case )
elif self.config.problem_type == "single_label_classification":
snake_case_ = CrossEntropyLoss()
snake_case_ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
snake_case_ = BCEWithLogitsLoss()
snake_case_ = loss_fct(snake_case , snake_case )
if not return_dict:
snake_case_ = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=snake_case , logits=snake_case , hidden_states=outputs.hidden_states )
@add_start_docstrings(
'''
ResNet backbone, to be used with frameworks like DETR and MaskFormer.
''' , lowercase_ , )
class lowercase ( lowercase_ , lowercase_ ):
def __init__( self , snake_case ):
super().__init__(snake_case )
super()._init_backbone(snake_case )
snake_case_ = [config.embedding_size] + config.hidden_sizes
snake_case_ = ResNetEmbeddings(snake_case )
snake_case_ = ResNetEncoder(snake_case )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case )
@replace_return_docstrings(output_type=snake_case , config_class=_CONFIG_FOR_DOC )
def a ( self , snake_case , snake_case = None , snake_case = None ):
snake_case_ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case_ = self.embedder(snake_case )
snake_case_ = self.encoder(snake_case , output_hidden_states=snake_case , return_dict=snake_case )
snake_case_ = outputs.hidden_states
snake_case_ = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
snake_case_ = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=snake_case , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=snake_case , )
| 285
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|
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__lowerCamelCase , n - 1 , __lowerCamelCase ) * a) % mod
else:
__a = binary_exponentiation(__lowerCamelCase , n / 2 , __lowerCamelCase )
return (b * b) % mod
# a prime number
lowerCamelCase_ : str = 701
lowerCamelCase_ : Optional[int] = 1_000_000_000
lowerCamelCase_ : Optional[int] = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 197
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class a__ ( __snake_case ):
A__ : torch.FloatTensor
A__ : torch.FloatTensor
A__ : Optional[torch.FloatTensor] = None
class a__ ( __snake_case , __snake_case ):
A__ : Optional[Any] = 2
@register_to_config
def __init__( self , UpperCAmelCase = 0.02 , UpperCAmelCase = 1_0_0 , UpperCAmelCase = 1.007 , UpperCAmelCase = 8_0 , UpperCAmelCase = 0.05 , UpperCAmelCase = 5_0 , ) -> Optional[Any]:
# standard deviation of the initial noise distribution
__a = sigma_max
# setable values
__a = None
__a = None
__a = None # sigma(t_i)
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase = None ) -> torch.FloatTensor:
return sample
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase = None ) -> int:
__a = num_inference_steps
__a = np.arange(0 , self.num_inference_steps )[::-1].copy()
__a = torch.from_numpy(UpperCAmelCase ).to(UpperCAmelCase )
__a = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
__a = torch.tensor(UpperCAmelCase , dtype=torch.floataa , device=UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = None ) -> Tuple[torch.FloatTensor, float]:
if self.config.s_min <= sigma <= self.config.s_max:
__a = min(self.config.s_churn / self.num_inference_steps , 2**0.5 - 1 )
else:
__a = 0
# sample eps ~ N(0, S_noise^2 * I)
__a = self.config.s_noise * randn_tensor(sample.shape , generator=UpperCAmelCase ).to(sample.device )
__a = sigma + gamma * sigma
__a = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = True , ) -> Union[KarrasVeOutput, Tuple]:
__a = sample_hat + sigma_hat * model_output
__a = (sample_hat - pred_original_sample) / sigma_hat
__a = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=UpperCAmelCase , derivative=UpperCAmelCase , pred_original_sample=UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = True , ) -> Union[KarrasVeOutput, Tuple]:
__a = sample_prev + sigma_prev * model_output
__a = (sample_prev - pred_original_sample) / sigma_prev
__a = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=UpperCAmelCase , derivative=UpperCAmelCase , pred_original_sample=UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[Any]:
raise NotImplementedError()
| 197
| 1
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
'''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
'''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
'''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
'''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
'''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
'''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
'''self_attn.rotary_emb''': '''encoder.embed_positions''',
'''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
'''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
'''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
'''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
'''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
'''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
'''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
'''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
'''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
'''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
'''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
'''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__UpperCamelCase : Tuple = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
for attribute in key.split('''.''' ):
SCREAMING_SNAKE_CASE : List[Any] = getattr(__UpperCamelCase , __UpperCamelCase )
if weight_type is not None:
SCREAMING_SNAKE_CASE : Any = getattr(__UpperCamelCase , __UpperCamelCase ).shape
else:
SCREAMING_SNAKE_CASE : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
SCREAMING_SNAKE_CASE : Tuple = value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE : List[str] = value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE : Tuple = value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE : Tuple = value
elif weight_type == "running_mean":
SCREAMING_SNAKE_CASE : List[str] = value
elif weight_type == "running_var":
SCREAMING_SNAKE_CASE : Tuple = value
elif weight_type == "num_batches_tracked":
SCREAMING_SNAKE_CASE : Dict = value
elif weight_type == "inv_freq":
SCREAMING_SNAKE_CASE : int = value
else:
SCREAMING_SNAKE_CASE : Dict = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : List[str] = fairseq_model.state_dict()
SCREAMING_SNAKE_CASE : Dict = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
SCREAMING_SNAKE_CASE : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , hf_model.config.feat_extract_norm == '''group''' , )
SCREAMING_SNAKE_CASE : List[Any] = True
else:
for key, mapped_key in MAPPING.items():
SCREAMING_SNAKE_CASE : Union[str, Any] = '''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
SCREAMING_SNAKE_CASE : Tuple = True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE : Dict = name.split(__UpperCamelCase )[0].split('''.''' )[-2]
SCREAMING_SNAKE_CASE : Optional[Any] = mapped_key.replace('''*''' , __UpperCamelCase )
if "pos_bias_u" in name:
SCREAMING_SNAKE_CASE : List[Any] = None
elif "pos_bias_v" in name:
SCREAMING_SNAKE_CASE : Optional[Any] = None
elif "weight_g" in name:
SCREAMING_SNAKE_CASE : List[Any] = '''weight_g'''
elif "weight_v" in name:
SCREAMING_SNAKE_CASE : List[str] = '''weight_v'''
elif "bias" in name:
SCREAMING_SNAKE_CASE : List[Any] = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
SCREAMING_SNAKE_CASE : List[str] = '''weight'''
elif "running_mean" in name:
SCREAMING_SNAKE_CASE : Any = '''running_mean'''
elif "inv_freq" in name:
SCREAMING_SNAKE_CASE : Optional[Any] = '''inv_freq'''
elif "running_var" in name:
SCREAMING_SNAKE_CASE : Any = '''running_var'''
elif "num_batches_tracked" in name:
SCREAMING_SNAKE_CASE : Any = '''num_batches_tracked'''
else:
SCREAMING_SNAKE_CASE : Optional[Any] = None
set_recursively(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
continue
if not is_used:
unused_weights.append(__UpperCamelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Tuple = full_name.split('''conv_layers.''' )[-1]
SCREAMING_SNAKE_CASE : Optional[int] = name.split('''.''' )
SCREAMING_SNAKE_CASE : List[Any] = int(items[0] )
SCREAMING_SNAKE_CASE : Tuple = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
SCREAMING_SNAKE_CASE : List[str] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
SCREAMING_SNAKE_CASE : int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
SCREAMING_SNAKE_CASE : List[str] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
SCREAMING_SNAKE_CASE : int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__UpperCamelCase )
@torch.no_grad()
def A ( _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=True ):
if config_path is not None:
SCREAMING_SNAKE_CASE : Optional[int] = WavaVecaConformerConfig.from_pretrained(__UpperCamelCase , hidden_act='''swish''' )
else:
SCREAMING_SNAKE_CASE : int = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
SCREAMING_SNAKE_CASE : List[str] = '''rotary'''
if is_finetuned:
if dict_path:
SCREAMING_SNAKE_CASE : str = Dictionary.load(__UpperCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
SCREAMING_SNAKE_CASE : Tuple = target_dict.pad_index
SCREAMING_SNAKE_CASE : List[str] = target_dict.bos_index
SCREAMING_SNAKE_CASE : Dict = target_dict.eos_index
SCREAMING_SNAKE_CASE : str = len(target_dict.symbols )
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(__UpperCamelCase , '''vocab.json''' )
if not os.path.isdir(__UpperCamelCase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__UpperCamelCase ) )
return
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
SCREAMING_SNAKE_CASE : str = target_dict.indices
# fairseq has the <pad> and <s> switched
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Optional[Any] = 1
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = WavaVecaCTCTokenizer(
__UpperCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__UpperCamelCase , )
SCREAMING_SNAKE_CASE : Optional[int] = True if config.feat_extract_norm == '''layer''' else False
SCREAMING_SNAKE_CASE : Union[str, Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=__UpperCamelCase , return_attention_mask=__UpperCamelCase , )
SCREAMING_SNAKE_CASE : Optional[int] = WavaVecaProcessor(feature_extractor=__UpperCamelCase , tokenizer=__UpperCamelCase )
processor.save_pretrained(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = WavaVecaConformerForCTC(__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE : int = WavaVecaConformerForPreTraining(__UpperCamelCase )
if is_finetuned:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
SCREAMING_SNAKE_CASE : Optional[int] = argparse.Namespace(task='''audio_pretraining''' )
SCREAMING_SNAKE_CASE : Dict = fairseq.tasks.setup_task(__UpperCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__UpperCamelCase )
SCREAMING_SNAKE_CASE : str = model[0].eval()
recursively_load_weights(__UpperCamelCase , __UpperCamelCase , not is_finetuned )
hf_wavavec.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__UpperCamelCase : str = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
__UpperCamelCase : Any = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 182
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __snake_case ( lowerCamelCase_ , unittest.TestCase ):
lowerCAmelCase_ = KandinskyVaaImgaImgPipeline
lowerCAmelCase_ = ["image_embeds", "negative_image_embeds", "image"]
lowerCAmelCase_ = [
"image_embeds",
"negative_image_embeds",
"image",
]
lowerCAmelCase_ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
lowerCAmelCase_ = False
@property
def __a ( self : Union[str, Any] ):
"""simple docstring"""
return 32
@property
def __a ( self : Union[str, Any] ):
"""simple docstring"""
return 32
@property
def __a ( self : Optional[Any] ):
"""simple docstring"""
return self.time_input_dim
@property
def __a ( self : Optional[int] ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def __a ( self : List[str] ):
"""simple docstring"""
return 1_00
@property
def __a ( self : Union[str, Any] ):
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image""",
"""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,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
SCREAMING_SNAKE_CASE__ = UNetaDConditionModel(**_lowercase )
return model
@property
def __a ( self : str ):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __a ( self : Union[str, Any] ):
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ = VQModel(**self.dummy_movq_kwargs )
return model
def __a ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.dummy_unet
SCREAMING_SNAKE_CASE__ = self.dummy_movq
SCREAMING_SNAKE_CASE__ = {
"""num_train_timesteps""": 10_00,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_00_85,
"""beta_end""": 0.0_12,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
SCREAMING_SNAKE_CASE__ = DDIMScheduler(**_lowercase )
SCREAMING_SNAKE_CASE__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def __a ( self : Optional[Any] , _lowercase : Any , _lowercase : Tuple=0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_lowercase ) ).to(_lowercase )
SCREAMING_SNAKE_CASE__ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_lowercase )
# create init_image
SCREAMING_SNAKE_CASE__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_lowercase ) ).to(_lowercase )
SCREAMING_SNAKE_CASE__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ = Image.fromarray(np.uinta(_lowercase ) ).convert("""RGB""" ).resize((2_56, 2_56) )
if str(_lowercase ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ = torch.manual_seed(_lowercase )
else:
SCREAMING_SNAKE_CASE__ = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
SCREAMING_SNAKE_CASE__ = {
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def __a ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """cpu"""
SCREAMING_SNAKE_CASE__ = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ = self.pipeline_class(**_lowercase )
SCREAMING_SNAKE_CASE__ = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
SCREAMING_SNAKE_CASE__ = pipe(**self.get_dummy_inputs(_lowercase ) )
SCREAMING_SNAKE_CASE__ = output.images
SCREAMING_SNAKE_CASE__ = pipe(
**self.get_dummy_inputs(_lowercase ) , return_dict=_lowercase , )[0]
SCREAMING_SNAKE_CASE__ = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ = np.array(
[0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def __a ( self : Optional[int] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_img2img_frog.npy""" )
SCREAMING_SNAKE_CASE__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
SCREAMING_SNAKE_CASE__ = """A red cartoon frog, 4k"""
SCREAMING_SNAKE_CASE__ = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(_lowercase )
SCREAMING_SNAKE_CASE__ = KandinskyVaaImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-decoder""" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
SCREAMING_SNAKE_CASE__ = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = pipe_prior(
_lowercase , generator=_lowercase , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
SCREAMING_SNAKE_CASE__ = pipeline(
image=_lowercase , image_embeds=_lowercase , negative_image_embeds=_lowercase , generator=_lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(_lowercase , _lowercase )
| 219
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A_ : List[str] ={"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[str] =["""BeitFeatureExtractor"""]
A_ : Dict =["""BeitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple =[
"""BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BeitForImageClassification""",
"""BeitForMaskedImageModeling""",
"""BeitForSemanticSegmentation""",
"""BeitModel""",
"""BeitPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[str] =[
"""FlaxBeitForImageClassification""",
"""FlaxBeitForMaskedImageModeling""",
"""FlaxBeitModel""",
"""FlaxBeitPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
A_ : Optional[Any] =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
A_ : Union[str, Any] =logging.get_logger(__name__)
class __a ( lowerCAmelCase__ ):
def __init__( self , *a__ , **a__ ):
warnings.warn(
'The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use DPTImageProcessor instead.' , a__ , )
super().__init__(*a__ , **a__ )
| 80
| 1
|
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
A : int = 1.5
A : Dict = int(factor * num_class_images )
A : Tuple = ClipClient(
url='''https://knn.laion.ai/knn-service''' , indice_name='''laion_400m''' , num_images=snake_case__ , aesthetic_weight=0.1 )
os.makedirs(F'{class_data_dir}/images' , exist_ok=snake_case__ )
if len(list(Path(F'{class_data_dir}/images' ).iterdir() ) ) >= num_class_images:
return
while True:
A : Tuple = client.query(text=snake_case__ )
if len(snake_case__ ) >= factor * num_class_images or num_images > 1E4:
break
else:
A : Dict = int(factor * num_images )
A : Optional[int] = ClipClient(
url='''https://knn.laion.ai/knn-service''' , indice_name='''laion_400m''' , num_images=snake_case__ , aesthetic_weight=0.1 , )
A : Dict = 0
A : Optional[int] = 0
A : Tuple = tqdm(desc='''downloading real regularization images''' , total=snake_case__ )
with open(F'{class_data_dir}/caption.txt' , '''w''' ) as fa, open(F'{class_data_dir}/urls.txt' , '''w''' ) as fa, open(
F'{class_data_dir}/images.txt' , '''w''' ) as fa:
while total < num_class_images:
A : Optional[Any] = class_images[count]
count += 1
try:
A : Dict = requests.get(images['''url'''] )
if img.status_code == 200:
A : Tuple = Image.open(BytesIO(img.content ) )
with open(F'{class_data_dir}/images/{total}.jpg' , '''wb''' ) as f:
f.write(img.content )
fa.write(images['''caption'''] + '''\n''' )
fa.write(images['''url'''] + '''\n''' )
fa.write(F'{class_data_dir}/images/{total}.jpg' + '''\n''' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : int = argparse.ArgumentParser('''''' , add_help=snake_case__ )
parser.add_argument('''--class_prompt''' , help='''text prompt to retrieve images''' , required=snake_case__ , type=snake_case__ )
parser.add_argument('''--class_data_dir''' , help='''path to save images''' , required=snake_case__ , type=snake_case__ )
parser.add_argument('''--num_class_images''' , help='''number of images to download''' , default=200 , type=snake_case__ )
return parser.parse_args()
if __name__ == "__main__":
lowercase : Optional[int] = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 3
|
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
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class __lowercase (_UpperCAmelCase ):
_UpperCamelCase = """table-transformer"""
_UpperCamelCase = ["""past_key_values"""]
_UpperCamelCase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , A_=True , A_=None , A_=3 , A_=100 , A_=6 , A_=2048 , A_=8 , A_=6 , A_=2048 , A_=8 , A_=0.0 , A_=0.0 , A_=True , A_="relu" , A_=256 , A_=0.1 , A_=0.0 , A_=0.0 , A_=0.02 , A_=1.0 , A_=False , A_="sine" , A_="resnet50" , A_=True , A_=False , A_=1 , A_=5 , A_=2 , A_=1 , A_=1 , A_=5 , A_=2 , A_=0.1 , **A_ , ) ->Any:
'''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 : Optional[Any] = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(A_ , A_ ):
__lowerCAmelCase : int = backbone_config.get('''model_type''' )
__lowerCAmelCase : List[str] = CONFIG_MAPPING[backbone_model_type]
__lowerCAmelCase : Any = config_class.from_dict(A_ )
# set timm attributes to None
__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase : List[str] = None, None, None
__lowerCAmelCase : Tuple = use_timm_backbone
__lowerCAmelCase : Optional[Any] = backbone_config
__lowerCAmelCase : List[str] = num_channels
__lowerCAmelCase : Tuple = num_queries
__lowerCAmelCase : int = d_model
__lowerCAmelCase : List[Any] = encoder_ffn_dim
__lowerCAmelCase : Optional[int] = encoder_layers
__lowerCAmelCase : List[str] = encoder_attention_heads
__lowerCAmelCase : str = decoder_ffn_dim
__lowerCAmelCase : Union[str, Any] = decoder_layers
__lowerCAmelCase : Any = decoder_attention_heads
__lowerCAmelCase : Optional[int] = dropout
__lowerCAmelCase : Any = attention_dropout
__lowerCAmelCase : Tuple = activation_dropout
__lowerCAmelCase : Optional[Any] = activation_function
__lowerCAmelCase : List[str] = init_std
__lowerCAmelCase : Tuple = init_xavier_std
__lowerCAmelCase : Any = encoder_layerdrop
__lowerCAmelCase : List[Any] = decoder_layerdrop
__lowerCAmelCase : Optional[Any] = encoder_layers
__lowerCAmelCase : Optional[Any] = auxiliary_loss
__lowerCAmelCase : Optional[Any] = position_embedding_type
__lowerCAmelCase : Tuple = backbone
__lowerCAmelCase : Any = use_pretrained_backbone
__lowerCAmelCase : int = dilation
# Hungarian matcher
__lowerCAmelCase : Dict = class_cost
__lowerCAmelCase : List[str] = bbox_cost
__lowerCAmelCase : int = giou_cost
# Loss coefficients
__lowerCAmelCase : Optional[Any] = mask_loss_coefficient
__lowerCAmelCase : Tuple = dice_loss_coefficient
__lowerCAmelCase : int = bbox_loss_coefficient
__lowerCAmelCase : List[Any] = giou_loss_coefficient
__lowerCAmelCase : int = eos_coefficient
super().__init__(is_encoder_decoder=A_ , **A_ )
@property
def UpperCamelCase__ ( self ) ->int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCamelCase__ ( self ) ->int:
'''simple docstring'''
return self.d_model
class __lowercase (_UpperCAmelCase ):
_UpperCamelCase = version.parse("""1.11""" )
@property
def UpperCamelCase__ ( self ) ->Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def UpperCamelCase__ ( self ) ->float:
'''simple docstring'''
return 1e-5
@property
def UpperCamelCase__ ( self ) ->int:
'''simple docstring'''
return 12
| 275
| 0
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class snake_case :
def __init__( self : Tuple , A : Union[str, Any] , A : str=1_3 , A : Dict=7 , A : Tuple=True , A : int=True , A : Dict=True , A : List[str]=True , A : str=9_9 , A : Optional[int]=3_2 , A : Optional[Any]=2 , A : Optional[Any]=4 , A : List[str]=3_7 , A : int="gelu" , A : str=0.1 , A : Union[str, Any]=0.1 , A : str=5_1_2 , A : str=1_6 , A : List[str]=2 , A : Tuple=0.02 , A : Any=False , A : List[Any]=True , A : int="None" , A : str=3 , A : Tuple=4 , A : str=None , ):
'''simple docstring'''
a : List[Any] = parent
a : Any = batch_size
a : List[str] = seq_length
a : List[Any] = is_training
a : Any = use_input_mask
a : Union[str, Any] = use_token_type_ids
a : Tuple = use_labels
a : str = vocab_size
a : str = hidden_size
a : Union[str, Any] = num_hidden_layers
a : Optional[Any] = num_attention_heads
a : Tuple = intermediate_size
a : Tuple = hidden_act
a : Optional[int] = hidden_dropout_prob
a : Dict = attention_probs_dropout_prob
a : Optional[int] = max_position_embeddings
a : Optional[int] = type_vocab_size
a : int = type_sequence_label_size
a : Any = initializer_range
a : Dict = num_labels
a : Optional[Any] = num_choices
a : List[Any] = relative_attention
a : Dict = position_biased_input
a : Optional[Any] = pos_att_type
a : Dict = scope
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
a : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a : Tuple = None
if self.use_input_mask:
a : str = random_attention_mask([self.batch_size, self.seq_length] )
a : str = None
if self.use_token_type_ids:
a : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a : List[str] = None
a : Union[str, Any] = None
a : Union[str, Any] = None
if self.use_labels:
a : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a : str = DebertaVaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=A , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self : Optional[Any] , A : Any , A : str , A : Union[str, Any] , A : Dict , A : Optional[Any] , A : str , A : int ):
'''simple docstring'''
a : Dict = TFDebertaVaModel(config=A )
a : Optional[int] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a : int = [input_ids, input_mask]
a : Union[str, Any] = model(A )
a : Any = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self : List[Any] , A : Optional[int] , A : Optional[Any] , A : str , A : List[str] , A : str , A : str , A : Tuple ):
'''simple docstring'''
a : Any = TFDebertaVaForMaskedLM(config=A )
a : Any = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
a : str = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ ( self : Any , A : int , A : Optional[Any] , A : Tuple , A : Optional[int] , A : Union[str, Any] , A : Tuple , A : int ):
'''simple docstring'''
a : str = self.num_labels
a : List[Any] = TFDebertaVaForSequenceClassification(config=A )
a : List[Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
a : str = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self : Dict , A : List[Any] , A : Optional[int] , A : List[Any] , A : Any , A : Tuple , A : Any , A : Union[str, Any] ):
'''simple docstring'''
a : List[Any] = self.num_labels
a : List[str] = TFDebertaVaForTokenClassification(config=A )
a : Optional[Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
a : Optional[Any] = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ ( self : Any , A : Dict , A : str , A : Tuple , A : int , A : Optional[int] , A : Any , A : List[str] ):
'''simple docstring'''
a : str = TFDebertaVaForQuestionAnswering(config=A )
a : str = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
a : str = model(A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
a : int = self.prepare_config_and_inputs()
(
(
a
), (
a
), (
a
), (
a
), (
a
), (
a
), (
a
),
) : List[Any] = config_and_inputs
a : str = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class snake_case ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
__magic_name__ = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
__magic_name__ = (
{
'''feature-extraction''': TFDebertaVaModel,
'''fill-mask''': TFDebertaVaForMaskedLM,
'''question-answering''': TFDebertaVaForQuestionAnswering,
'''text-classification''': TFDebertaVaForSequenceClassification,
'''token-classification''': TFDebertaVaForTokenClassification,
'''zero-shot''': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
__magic_name__ = False
__magic_name__ = False
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
a : Tuple = TFDebertaVaModelTester(self )
a : Union[str, Any] = ConfigTester(self , config_class=A , hidden_size=3_7 )
def lowerCamelCase__ ( self : int ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def lowerCamelCase__ ( self : str ):
'''simple docstring'''
a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
@slow
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
a : Any = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
self.assertIsNotNone(A )
@require_tf
class snake_case ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def lowerCamelCase__ ( self : str ):
'''simple docstring'''
pass
@slow
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
a : Any = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
a : Union[str, Any] = tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] )
a : List[Any] = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
a : Union[str, Any] = model(A , attention_mask=A )[0]
a : List[Any] = tf.constant(
[[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , A , atol=1E-4 )
| 186
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
_UpperCamelCase : int = logging.get_logger(__name__)
class snake_case ( UpperCAmelCase ):
__magic_name__ = ['''input_features''', '''attention_mask''']
def __init__( self : Optional[int] , A : Optional[Any]=8_0 , A : str=1_6_0_0_0 , A : List[str]=0.0 , A : Any=1_0 , A : Union[str, Any]=2_5 , A : str="hamming_window" , A : str=3_27_68.0 , A : Union[str, Any]=0.97 , A : Dict=1.0 , A : Any=True , A : Union[str, Any]=True , A : List[Any]=False , **A : Tuple , ):
'''simple docstring'''
super().__init__(feature_size=A , sampling_rate=A , padding_value=A , **A )
a : Any = feature_size
a : List[Any] = sampling_rate
a : Any = padding_value
a : str = hop_length
a : Any = win_length
a : List[Any] = frame_signal_scale
a : Tuple = preemphasis_coeff
a : Dict = mel_floor
a : Optional[int] = normalize_means
a : List[str] = normalize_vars
a : Dict = win_function
a : Union[str, Any] = return_attention_mask
a : List[Any] = win_length * sampling_rate // 1_0_0_0
a : Tuple = hop_length * sampling_rate // 1_0_0_0
a : List[Any] = optimal_fft_length(self.sample_size )
a : Any = (self.n_fft // 2) + 1
def lowerCamelCase__ ( self : List[Any] , A : np.array ):
'''simple docstring'''
if self.win_function == "hamming_window":
a : List[str] = window_function(window_length=self.sample_size , name=self.win_function , periodic=A )
else:
a : Dict = window_function(window_length=self.sample_size , name=self.win_function )
a : str = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
a : List[Any] = spectrogram(
one_waveform * self.frame_signal_scale , window=A , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=A , preemphasis=self.preemphasis_coeff , mel_filters=A , mel_floor=self.mel_floor , log_mel='log' , )
return msfc_features.T
def lowerCamelCase__ ( self : int , A : Tuple , A : int , A : Optional[int] ):
'''simple docstring'''
if self.normalize_means:
a : Any = x[:input_length].mean(axis=0 )
a : Dict = np.subtract(A , A )
if self.normalize_vars:
a : Dict = x[:input_length].std(axis=0 )
a : Dict = np.divide(A , A )
if input_length < x.shape[0]:
a : Dict = padding_value
# make sure array is in float32
a : Optional[int] = x.astype(np.floataa )
return x
def lowerCamelCase__ ( self : str , A : List[np.ndarray] , A : Optional[np.ndarray] = None ):
'''simple docstring'''
a : str = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(A , A , self.padding_value ) for x, n in zip(A , A )]
def __call__( self : Dict , A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , A : Union[bool, str, PaddingStrategy] = False , A : Optional[int] = None , A : bool = False , A : Optional[int] = None , A : Optional[bool] = None , A : Optional[Union[str, TensorType]] = None , A : Optional[int] = None , **A : Union[str, Any] , ):
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
a : Optional[int] = isinstance(A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
a : Dict = is_batched_numpy or (
isinstance(A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a : str = [np.asarray(A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A , np.ndarray ):
a : List[str] = np.asarray(A , dtype=np.floataa )
elif isinstance(A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a : Optional[int] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a : Any = [raw_speech]
# extract fbank features
a : str = [self._extract_mfsc_features(A ) for one_waveform in raw_speech]
# convert into correct format for padding
a : int = BatchFeature({'input_features': features} )
a : Union[str, Any] = self.pad(
A , padding=A , max_length=A , truncation=A , pad_to_multiple_of=A , return_attention_mask=A , **A , )
# make sure list is in array format
a : Optional[Any] = padded_inputs.get('input_features' )
if isinstance(input_features[0] , A ):
a : List[str] = [np.asarray(A , dtype=np.floataa ) for feature in input_features]
a : List[Any] = padded_inputs.get('attention_mask' )
if attention_mask is not None:
a : int = [np.asarray(A , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
a : Any = (
np.array(A , dtype=np.intaa )
if self._get_padding_strategies(A , max_length=A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
a : List[str] = self.normalize(
padded_inputs['input_features'] , attention_mask=A )
if return_tensors is not None:
a : Optional[int] = padded_inputs.convert_to_tensors(A )
return padded_inputs
| 186
| 1
|
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