code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 21 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class A__ ( lowerCAmelCase__ ):
lowerCAmelCase__ : Tuple = "mask2former"
lowerCAmelCase__ : List[Any] = ["swin"]
lowerCAmelCase__ : str = {"hidden_size": "hidden_dim"}
def __init__( self : Optional[int] , _UpperCAmelCase : Optional[Dict] = None , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 10_24 , _UpperCAmelCase : str = "relu" , _UpperCAmelCase : int = 6 , _UpperCAmelCase : int = 10 , _UpperCAmelCase : int = 8 , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : int = 20_48 , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 4 , _UpperCAmelCase : int = 2_55 , _UpperCAmelCase : int = 1_00 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 2.0 , _UpperCAmelCase : float = 5.0 , _UpperCAmelCase : float = 5.0 , _UpperCAmelCase : int = 1_25_44 , _UpperCAmelCase : float = 3.0 , _UpperCAmelCase : float = 0.75 , _UpperCAmelCase : float = 0.02 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : bool = True , _UpperCAmelCase : List[int] = [4, 8, 16, 32] , _UpperCAmelCase : bool = None , **_UpperCAmelCase : List[str] , ) -> int:
"""simple docstring"""
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
__lowercase = CONFIG_MAPPING['swin'](
image_size=2_24 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=_UpperCAmelCase , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__lowercase = backbone_config.pop('model_type' )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(_UpperCAmelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
__lowercase = backbone_config
__lowercase = feature_size
__lowercase = mask_feature_size
__lowercase = hidden_dim
__lowercase = encoder_feedforward_dim
__lowercase = activation_function
__lowercase = encoder_layers
__lowercase = decoder_layers
__lowercase = num_attention_heads
__lowercase = dropout
__lowercase = dim_feedforward
__lowercase = pre_norm
__lowercase = enforce_input_projection
__lowercase = common_stride
__lowercase = ignore_value
__lowercase = num_queries
__lowercase = no_object_weight
__lowercase = class_weight
__lowercase = mask_weight
__lowercase = dice_weight
__lowercase = train_num_points
__lowercase = oversample_ratio
__lowercase = importance_sample_ratio
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = use_auxiliary_loss
__lowercase = feature_strides
__lowercase = output_auxiliary_logits
__lowercase = decoder_layers
super().__init__(**_UpperCAmelCase )
@classmethod
def a__ ( cls : Union[str, Any] , _UpperCAmelCase : PretrainedConfig , **_UpperCAmelCase : Optional[int] ) -> Dict:
"""simple docstring"""
return cls(
backbone_config=_UpperCAmelCase , **_UpperCAmelCase , )
def a__ ( self : str ) -> Dict[str, any]:
"""simple docstring"""
__lowercase = copy.deepcopy(self.__dict__ )
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
| 325 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase_ ( _a , _a , _a , unittest.TestCase ):
"""simple docstring"""
lowercase = StableDiffusionInstructPixaPixPipeline
lowercase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
lowercase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase ( self : Any ):
torch.manual_seed(0 )
snake_case__ : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
snake_case__ : Optional[int] = PNDMScheduler(skip_prk_steps=snake_case_ )
torch.manual_seed(0 )
snake_case__ : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case__ : List[Any] = 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 , )
snake_case__ : Dict = CLIPTextModel(snake_case_ )
snake_case__ : Optional[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
snake_case__ : Optional[int] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase ( self : Any , snake_case_ : Dict , snake_case_ : int=0 ):
snake_case__ : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
snake_case__ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case__ : str = Image.fromarray(np.uinta(snake_case_ ) ).convert("""RGB""" )
if str(snake_case_ ).startswith("""mps""" ):
snake_case__ : Any = torch.manual_seed(snake_case_ )
else:
snake_case__ : List[Any] = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
snake_case__ : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""image_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase ( self : List[Any] ):
snake_case__ : Tuple = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : List[Any] = self.get_dummy_components()
snake_case__ : Any = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
snake_case__ : Tuple = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
snake_case__ : Any = self.get_dummy_inputs(snake_case_ )
snake_case__ : Dict = sd_pipe(**snake_case_ ).images
snake_case__ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case__ : str = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCamelCase ( self : List[str] ):
snake_case__ : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : Union[str, Any] = self.get_dummy_components()
snake_case__ : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
snake_case__ : Tuple = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
snake_case__ : List[str] = self.get_dummy_inputs(snake_case_ )
snake_case__ : Any = """french fries"""
snake_case__ : Optional[Any] = sd_pipe(**snake_case_ , negative_prompt=snake_case_ )
snake_case__ : Optional[int] = output.images
snake_case__ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case__ : Any = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCamelCase ( self : List[Any] ):
snake_case__ : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : Union[str, Any] = self.get_dummy_components()
snake_case__ : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
snake_case__ : List[Any] = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
snake_case__ : Optional[int] = self.get_dummy_inputs(snake_case_ )
snake_case__ : Optional[int] = [inputs["""prompt"""]] * 2
snake_case__ : Dict = np.array(inputs["""image"""] ).astype(np.floataa ) / 255.0
snake_case__ : Dict = torch.from_numpy(snake_case_ ).unsqueeze(0 ).to(snake_case_ )
snake_case__ : Dict = image / 2 + 0.5
snake_case__ : List[str] = image.permute(0 , 3 , 1 , 2 )
snake_case__ : str = image.repeat(2 , 1 , 1 , 1 )
snake_case__ : Dict = sd_pipe(**snake_case_ ).images
snake_case__ : Dict = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
snake_case__ : List[Any] = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCamelCase ( self : Optional[int] ):
snake_case__ : List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : Dict = self.get_dummy_components()
snake_case__ : List[Any] = EulerAncestralDiscreteScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
snake_case__ : Any = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
snake_case__ : Optional[Any] = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
snake_case__ : Any = self.get_dummy_inputs(snake_case_ )
snake_case__ : List[str] = sd_pipe(**snake_case_ ).images
snake_case__ : Tuple = image[0, -3:, -3:, -1]
snake_case__ : Dict = [round(snake_case_ , 4 ) for x in image_slice.flatten().tolist()]
print(""",""".join([str(snake_case_ ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
snake_case__ : List[str] = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCamelCase ( self : Any ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def lowerCamelCase ( self : Tuple ):
snake_case__ : Optional[int] = self.get_dummy_components()
snake_case__ : Dict = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
snake_case__ : Any = VaeImageProcessor(do_resize=snake_case_ , do_normalize=snake_case_ )
snake_case__ : Dict = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
snake_case__ : Optional[Any] = pipe(**self.get_dummy_inputs_by_type(snake_case_ , input_image_type="""pt""" ) )[0]
snake_case__ : Any = components["""vae"""]
snake_case__ : Optional[Any] = self.get_dummy_inputs_by_type(snake_case_ , input_image_type="""pt""" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
snake_case__ : Tuple = vae.encode(inputs[image_param] ).latent_dist.mode()
snake_case__ : Any = pipe(**snake_case_ )[0]
snake_case__ : List[Any] = np.abs(out - out_latents_inputs ).max()
self.assertLess(snake_case_ , 1E-4 , """passing latents as image input generate different result from passing image""" )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : Dict ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self : str , snake_case_ : str=0 ):
snake_case__ : int = torch.manual_seed(snake_case_ )
snake_case__ : int = load_image(
"""https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
snake_case__ : Tuple = {
"""prompt""": """turn him into a cyborg""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""image_guidance_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase ( self : List[Any] ):
snake_case__ : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
snake_case__ : Optional[int] = self.get_inputs()
snake_case__ : Dict = pipe(**snake_case_ ).images
snake_case__ : Optional[int] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
snake_case__ : str = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def lowerCamelCase ( self : Tuple ):
snake_case__ : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=snake_case_ )
snake_case__ : Union[str, Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
snake_case__ : Union[str, Any] = self.get_inputs()
snake_case__ : Tuple = pipe(**snake_case_ ).images
snake_case__ : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
snake_case__ : Union[str, Any] = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def lowerCamelCase ( self : str ):
snake_case__ : Dict = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=snake_case_ )
snake_case__ : List[str] = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
snake_case__ : Dict = self.get_inputs()
snake_case__ : Optional[Any] = pipe(**snake_case_ ).images
snake_case__ : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
snake_case__ : Union[str, Any] = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def lowerCamelCase ( self : List[Any] ):
snake_case__ : Optional[Any] = 0
def callback_fn(snake_case_ : int , snake_case_ : int , snake_case_ : torch.FloatTensor ) -> None:
snake_case__ : List[str] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
snake_case__ : List[str] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
snake_case__ : int = latents[0, -3:, -3:, -1]
snake_case__ : Optional[int] = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
snake_case__ : List[str] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
snake_case__ : Union[str, Any] = latents[0, -3:, -3:, -1]
snake_case__ : Tuple = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
snake_case__ : Tuple = False
snake_case__ : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=snake_case_ , torch_dtype=torch.floataa )
snake_case__ : Optional[int] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
snake_case__ : List[Any] = self.get_inputs()
pipe(**snake_case_ , callback=snake_case_ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowerCamelCase ( self : Any ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
snake_case__ : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=snake_case_ , torch_dtype=torch.floataa )
snake_case__ : Optional[int] = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
snake_case__ : Any = self.get_inputs()
snake_case__ : Dict = pipe(**snake_case_ )
snake_case__ : int = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def lowerCamelCase ( self : Tuple ):
snake_case__ : List[str] = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
snake_case__ : List[str] = inputs["""image"""].resize((504, 504) )
snake_case__ : Optional[Any] = """timbrooks/instruct-pix2pix"""
snake_case__ : Union[str, Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
snake_case_ , safety_checker=snake_case_ , )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
snake_case__ : Any = pipe(**snake_case_ )
snake_case__ : str = output.images[0]
snake_case__ : Optional[int] = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
snake_case__ : Tuple = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
| 350 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = "markuplm"
def __init__( self : List[Any] , snake_case_ : List[Any]=30_522 , snake_case_ : Tuple=768 , snake_case_ : Union[str, Any]=12 , snake_case_ : str=12 , snake_case_ : Optional[Any]=3_072 , snake_case_ : Optional[Any]="gelu" , snake_case_ : str=0.1 , snake_case_ : List[Any]=0.1 , snake_case_ : Dict=512 , snake_case_ : Tuple=2 , snake_case_ : List[str]=0.02 , snake_case_ : int=1E-1_2 , snake_case_ : Any=0 , snake_case_ : Any=0 , snake_case_ : str=2 , snake_case_ : Optional[int]=256 , snake_case_ : Optional[int]=1_024 , snake_case_ : str=216 , snake_case_ : List[str]=1_001 , snake_case_ : Optional[Any]=32 , snake_case_ : int=50 , snake_case_ : Tuple="absolute" , snake_case_ : Tuple=True , snake_case_ : int=None , **snake_case_ : str , ):
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ , )
snake_case__ : Tuple = vocab_size
snake_case__ : Optional[int] = hidden_size
snake_case__ : Union[str, Any] = num_hidden_layers
snake_case__ : Union[str, Any] = num_attention_heads
snake_case__ : List[str] = hidden_act
snake_case__ : Dict = intermediate_size
snake_case__ : Optional[Any] = hidden_dropout_prob
snake_case__ : Any = attention_probs_dropout_prob
snake_case__ : int = max_position_embeddings
snake_case__ : Optional[int] = type_vocab_size
snake_case__ : List[str] = initializer_range
snake_case__ : str = layer_norm_eps
snake_case__ : List[Any] = position_embedding_type
snake_case__ : Any = use_cache
snake_case__ : Union[str, Any] = classifier_dropout
# additional properties
snake_case__ : List[str] = max_depth
snake_case__ : int = max_xpath_tag_unit_embeddings
snake_case__ : Tuple = max_xpath_subs_unit_embeddings
snake_case__ : Dict = tag_pad_id
snake_case__ : Union[str, Any] = subs_pad_id
snake_case__ : Tuple = xpath_unit_hidden_size
| 43 | 0 |
_lowercase : Dict ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_lowercase : str =["a", "b", "c", "d", "e"]
def lowerCAmelCase_ ( _lowercase : Optional[Any] , _lowercase : Tuple , _lowercase : int) -> Optional[Any]:
"""simple docstring"""
a__ : int = start
# add current to visited
visited.append(_lowerCAmelCase)
a__ : List[Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
a__ : Tuple = topological_sort(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase)
# if all neighbors visited add current to sort
sort.append(_lowerCAmelCase)
# if all vertices haven't been visited select a new one to visit
if len(_lowerCAmelCase) != len(_lowerCAmelCase):
for vertice in vertices:
if vertice not in visited:
a__ : int = topological_sort(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase)
# return sort
return sort
if __name__ == "__main__":
_lowercase : Optional[int] =topological_sort("a", [], [])
print(sort)
| 170 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
# See all REALM models at https://huggingface.co/models?filter=realm
}
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = """realm"""
def __init__( self :str , lowerCamelCase :List[Any]=3_0522 , lowerCamelCase :Optional[int]=768 , lowerCamelCase :Any=128 , lowerCamelCase :Tuple=12 , lowerCamelCase :str=12 , lowerCamelCase :List[str]=8 , lowerCamelCase :List[str]=3072 , lowerCamelCase :List[str]="gelu_new" , lowerCamelCase :int=0.1 , lowerCamelCase :Optional[Any]=0.1 , lowerCamelCase :int=512 , lowerCamelCase :Union[str, Any]=2 , lowerCamelCase :str=0.02 , lowerCamelCase :Tuple=1e-12 , lowerCamelCase :Dict=256 , lowerCamelCase :int=10 , lowerCamelCase :List[str]=1e-3 , lowerCamelCase :str=5 , lowerCamelCase :Optional[int]=320 , lowerCamelCase :Union[str, Any]=1335_3718 , lowerCamelCase :str=5000 , lowerCamelCase :str=1 , lowerCamelCase :List[Any]=0 , lowerCamelCase :Tuple=2 , **lowerCamelCase :Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase )
# Common config
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = retriever_proj_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = num_candidates
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = type_vocab_size
UpperCAmelCase__ = layer_norm_eps
# Reader config
UpperCAmelCase__ = span_hidden_size
UpperCAmelCase__ = max_span_width
UpperCAmelCase__ = reader_layer_norm_eps
UpperCAmelCase__ = reader_beam_size
UpperCAmelCase__ = reader_seq_len
# Retrieval config
UpperCAmelCase__ = num_block_records
UpperCAmelCase__ = searcher_beam_size
| 169 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = collection[i]
__lowerCAmelCase = 0
__lowerCAmelCase = i - 1
while low <= high:
__lowerCAmelCase = (low + high) // 2
if val < collection[mid]:
__lowerCAmelCase = mid - 1
else:
__lowerCAmelCase = mid + 1
for j in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , -1 ):
__lowerCAmelCase = collection[j - 1]
__lowerCAmelCase = val
return collection
if __name__ == "__main__":
UpperCamelCase__ = input("""Enter numbers separated by a comma:\n""").strip()
UpperCamelCase__ = [int(item) for item in user_input.split(""",""")]
print(binary_insertion_sort(unsorted))
| 359 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
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(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00_01 ):
try:
__lowerCAmelCase = int(SCREAMING_SNAKE_CASE_ )
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int." ) from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one." )
__lowerCAmelCase = []
__lowerCAmelCase = 2
while len(SCREAMING_SNAKE_CASE_ ) < nth:
if is_prime(SCREAMING_SNAKE_CASE_ ):
primes.append(SCREAMING_SNAKE_CASE_ )
num += 1
else:
num += 1
return primes[len(SCREAMING_SNAKE_CASE_ ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 102 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
return [
a * b * (1000 - a - b)
for a in range(1 ,999 )
for b in range(lowercase_ ,999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 44 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCAmelCase ( lowercase_ = "isbn/0140328726" ):
UpperCAmelCase = olid.strip().strip('/' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('/' ) != 1:
UpperCAmelCase = F"""{olid} is not a valid Open Library olid"""
raise ValueError(lowercase_ )
return requests.get(F"""https://openlibrary.org/{new_olid}.json""" ).json()
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = {
'title': 'Title',
'publish_date': 'Publish date',
'authors': 'Authors',
'number_of_pages': 'Number of pages:',
'first_sentence': 'First sentence',
'isbn_10': 'ISBN (10)',
'isbn_13': 'ISBN (13)',
}
UpperCAmelCase = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
UpperCAmelCase = [
get_openlibrary_data(author['key'] )['name'] for author in data['Authors']
]
UpperCAmelCase = data['First sentence']['value']
for key, value in data.items():
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = ', '.join(lowercase_ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
snake_case_ = input("""\nEnter the ISBN code to search (or 'quit' to stop): """).strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(f'''Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.''')
continue
print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')
try:
snake_case_ = summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
print("""\n""".join(f'''{key}: {value}''' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f'''Sorry, there are no results for ISBN: {isbn}.''')
| 78 | 0 |
from math import ceil
def snake_case_ ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] ):
__lowercase : List[Any] = list(range(0 , lowerCAmelCase_ ) )
__lowercase : Dict = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
__lowercase : Any = []
for i in device_map_blocks:
if device_map_blocks.count(lowerCAmelCase_ ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(lowerCAmelCase_ )
# Missing blocks
__lowercase : Union[str, Any] = [i for i in blocks if i not in device_map_blocks]
__lowercase : Tuple = [i for i in device_map_blocks if i not in blocks]
if len(lowerCAmelCase_ ) != 0:
raise ValueError(
"""Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
""" These attention blocks were specified more than once: """ + str(lowerCAmelCase_ ) )
if len(lowerCAmelCase_ ) != 0:
raise ValueError(
"""There are attention blocks for this model that are not specified in the device_map. Add these attention """
"""blocks to a device on the device_map: """ + str(lowerCAmelCase_ ) )
if len(lowerCAmelCase_ ) != 0:
raise ValueError(
"""The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
+ str(lowerCAmelCase_ ) )
def snake_case_ ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict ):
__lowercase : str = list(range(lowerCAmelCase_ ) )
__lowercase : List[str] = int(ceil(n_layers / len(lowerCAmelCase_ ) ) )
__lowercase : List[str] = [layers[i : i + n_blocks] for i in range(0 , lowerCAmelCase_ , lowerCAmelCase_ )]
return dict(zip(lowerCAmelCase_ , lowerCAmelCase_ ) ) | 306 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : Optional[Any] = (DPMSolverSDEScheduler,)
_A : Dict = 10
def lowerCAmelCase ( self : Optional[int] , **__a : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase : Any = {
"""num_train_timesteps""": 1100,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**__a )
return config
def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=__a )
def lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__a , beta_end=__a )
def lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a )
def lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def lowerCAmelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : Optional[int] = self.scheduler_classes[0]
__lowercase : List[str] = self.get_scheduler_config()
__lowercase : Any = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
__lowercase : Optional[Any] = self.dummy_model()
__lowercase : str = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowercase : Optional[Any] = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
__lowercase : Union[str, Any] = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[Any] = model(__a , __a )
__lowercase : Optional[Any] = scheduler.step(__a , __a , __a )
__lowercase : str = output.prev_sample
__lowercase : Optional[Any] = torch.sum(torch.abs(__a ) )
__lowercase : Union[str, Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1E-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1E-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase : Tuple = self.scheduler_classes[0]
__lowercase : Dict = self.get_scheduler_config(prediction_type="""v_prediction""" )
__lowercase : int = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
__lowercase : Optional[int] = self.dummy_model()
__lowercase : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
__lowercase : Dict = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
__lowercase : Dict = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[int] = model(__a , __a )
__lowercase : Optional[int] = scheduler.step(__a , __a , __a )
__lowercase : int = output.prev_sample
__lowercase : Optional[Any] = torch.sum(torch.abs(__a ) )
__lowercase : List[str] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1E-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1E-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1E-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1E-3
def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase : Tuple = self.scheduler_classes[0]
__lowercase : Dict = self.get_scheduler_config()
__lowercase : Optional[int] = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
__lowercase : int = self.dummy_model()
__lowercase : Optional[Any] = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__lowercase : int = scheduler.scale_model_input(__a , __a )
__lowercase : List[str] = model(__a , __a )
__lowercase : List[str] = scheduler.step(__a , __a , __a )
__lowercase : int = output.prev_sample
__lowercase : List[Any] = torch.sum(torch.abs(__a ) )
__lowercase : Optional[Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1E-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1E-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase : str = self.scheduler_classes[0]
__lowercase : List[Any] = self.get_scheduler_config()
__lowercase : Tuple = scheduler_class(**__a , use_karras_sigmas=__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
__lowercase : List[str] = self.dummy_model()
__lowercase : Optional[int] = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma
__lowercase : str = sample.to(__a )
for t in scheduler.timesteps:
__lowercase : List[Any] = scheduler.scale_model_input(__a , __a )
__lowercase : Optional[Any] = model(__a , __a )
__lowercase : Any = scheduler.step(__a , __a , __a )
__lowercase : Optional[Any] = output.prev_sample
__lowercase : Any = torch.sum(torch.abs(__a ) )
__lowercase : Optional[Any] = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2 | 306 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase : Dict = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class A__ ( A__ , unittest.TestCase ):
A__ = AlbertTokenizer
A__ = AlbertTokenizerFast
A__ = True
A__ = True
A__ = True
def A ( self : str ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_SCREAMING_SNAKE_CASE =AlbertTokenizer(_a )
tokenizer.save_pretrained(self.tmpdirname )
def A ( self : Optional[int] , _a : Dict ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE ='this is a test'
_SCREAMING_SNAKE_CASE ='this is a test'
return input_text, output_text
def A ( self : int ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE ='<pad>'
_SCREAMING_SNAKE_CASE =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def A ( self : str ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '▁eloquent' )
self.assertEqual(len(_a ) , 3_0000 )
def A ( self : Tuple ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def A ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE ='I was born in 92000, and this is falsé.'
_SCREAMING_SNAKE_CASE =tokenizer.tokenize(_a )
_SCREAMING_SNAKE_CASE =rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
_SCREAMING_SNAKE_CASE =tokenizer.encode(_a , add_special_tokens=_a )
_SCREAMING_SNAKE_CASE =rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_SCREAMING_SNAKE_CASE =self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE =tokenizer.encode(_a )
_SCREAMING_SNAKE_CASE =rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def A ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =AlbertTokenizer(_a , keep_accents=_a )
_SCREAMING_SNAKE_CASE =tokenizer.tokenize('This is a test' )
self.assertListEqual(_a , ['▁this', '▁is', '▁a', '▁test'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [48, 25, 21, 1289] )
_SCREAMING_SNAKE_CASE =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
_a , ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.'] )
_SCREAMING_SNAKE_CASE =tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(_a , [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )
_SCREAMING_SNAKE_CASE =tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.'] , )
def A ( self : Optional[int] ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =AlbertTokenizer(_a )
_SCREAMING_SNAKE_CASE =tokenizer.encode('sequence builders' )
_SCREAMING_SNAKE_CASE =tokenizer.encode('multi-sequence build' )
_SCREAMING_SNAKE_CASE =tokenizer.build_inputs_with_special_tokens(_a )
_SCREAMING_SNAKE_CASE =tokenizer.build_inputs_with_special_tokens(_a , _a )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def A ( self : Optional[int] ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE ={'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name='albert-base-v2' , revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e' , )
| 47 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =ArgumentParser(
description=(
'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
) )
# Optional arguments for the launch helper
parser.add_argument('--num_cores' , type=_UpperCamelCase , default=1 , help='Number of TPU cores to use (1 or 8).' )
# positional
parser.add_argument(
'training_script' , type=_UpperCamelCase , help=(
'The full path to the single TPU training '
'program/script to be launched in parallel, '
'followed by all the arguments for the '
'training script'
) , )
# rest from the training program
parser.add_argument('training_script_args' , nargs=_UpperCamelCase )
return parser.parse_args()
def _lowerCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =parse_args()
# Import training_script as a module.
_SCREAMING_SNAKE_CASE =Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_SCREAMING_SNAKE_CASE =script_fpath.stem
_SCREAMING_SNAKE_CASE =importlib.import_module(_UpperCamelCase )
# Patch sys.argv
_SCREAMING_SNAKE_CASE =[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 47 | 1 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = JukeboxTokenizer
UpperCamelCase__ = {
'''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 SCREAMING_SNAKE_CASE_ ( self : int ):
import torch
lowercase_ : Any = JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics""" )
lowercase_ : Any = tokenizer(**self.metas )["""input_ids"""]
# fmt: off
lowercase_ : str = [
torch.tensor([[
0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 1069, 11]] ),
torch.tensor([[0, 0, 0, 1069, 11]] ),
]
# 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 SCREAMING_SNAKE_CASE_ ( self : List[str] ):
import torch
lowercase_ : int = JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics""" )
lowercase_ : List[str] = tokenizer(**self.metas )["""input_ids"""]
# fmt: off
lowercase_ : Dict = [
torch.tensor([[
0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1069, 11, -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] ) )
| 21 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = DistilBertTokenizer
UpperCamelCase__ = DistilBertTokenizerFast
UpperCamelCase__ = True
@slow
def SCREAMING_SNAKE_CASE_ ( self : int ):
lowercase_ : int = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" )
lowercase_ : str = tokenizer.encode("""sequence builders""" , add_special_tokens=lowercase_ )
lowercase_ : Optional[int] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowercase_ )
lowercase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowercase_ )
lowercase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 21 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = tempfile.mkdtemp()
# fmt: off
_lowerCAmelCase : int = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
_lowerCAmelCase : Tuple = dict(zip(__lowercase, range(len(__lowercase))))
_lowerCAmelCase : List[Any] = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
_lowerCAmelCase : Optional[Any] = {'''unk_token''': '''<unk>'''}
_lowerCAmelCase : List[str] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
_lowerCAmelCase : List[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(__lowercase) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(__lowercase))
_lowerCAmelCase : List[str] = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
'''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
_lowerCAmelCase : Any = os.path.join(self.tmpdirname, __lowercase)
with open(self.image_processor_file, "w", encoding="utf-8") as fp:
json.dump(__lowercase, __lowercase)
def snake_case__ ( self, **__a):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname, **__lowercase)
def snake_case__ ( self, **__a):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **__lowercase)
def snake_case__ ( self, **__a):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname, **__lowercase)
def snake_case__ ( self):
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta)]
_lowerCAmelCase : int = [Image.fromarray(np.moveaxis(__lowercase, 0, -1)) for x in image_inputs]
return image_inputs
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.get_tokenizer()
_lowerCAmelCase : str = self.get_rust_tokenizer()
_lowerCAmelCase : Optional[Any] = self.get_image_processor()
_lowerCAmelCase : List[Any] = CLIPSegProcessor(tokenizer=__lowercase, image_processor=__lowercase)
processor_slow.save_pretrained(self.tmpdirname)
_lowerCAmelCase : Optional[int] = CLIPSegProcessor.from_pretrained(self.tmpdirname, use_fast=__lowercase)
_lowerCAmelCase : Union[str, Any] = CLIPSegProcessor(tokenizer=__lowercase, image_processor=__lowercase)
processor_fast.save_pretrained(self.tmpdirname)
_lowerCAmelCase : Dict = CLIPSegProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
self.assertIsInstance(processor_slow.tokenizer, __lowercase)
self.assertIsInstance(processor_fast.tokenizer, __lowercase)
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string())
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string())
self.assertIsInstance(processor_slow.image_processor, __lowercase)
self.assertIsInstance(processor_fast.image_processor, __lowercase)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = CLIPSegProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
_lowerCAmelCase : int = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
_lowerCAmelCase : List[Any] = self.get_image_processor(do_normalize=__lowercase, padding_value=1.0)
_lowerCAmelCase : Optional[Any] = CLIPSegProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=__lowercase, padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, __lowercase)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, __lowercase)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.get_image_processor()
_lowerCAmelCase : Optional[Any] = self.get_tokenizer()
_lowerCAmelCase : Dict = CLIPSegProcessor(tokenizer=__lowercase, image_processor=__lowercase)
_lowerCAmelCase : Dict = self.prepare_image_inputs()
_lowerCAmelCase : Union[str, Any] = image_processor(__lowercase, return_tensors="np")
_lowerCAmelCase : str = processor(images=__lowercase, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1E-2)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.get_image_processor()
_lowerCAmelCase : Dict = self.get_tokenizer()
_lowerCAmelCase : str = CLIPSegProcessor(tokenizer=__lowercase, image_processor=__lowercase)
_lowerCAmelCase : Tuple = '''lower newer'''
_lowerCAmelCase : str = processor(text=__lowercase)
_lowerCAmelCase : Optional[Any] = tokenizer(__lowercase)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = self.get_image_processor()
_lowerCAmelCase : Tuple = self.get_tokenizer()
_lowerCAmelCase : Optional[Any] = CLIPSegProcessor(tokenizer=__lowercase, image_processor=__lowercase)
_lowerCAmelCase : Optional[int] = '''lower newer'''
_lowerCAmelCase : str = self.prepare_image_inputs()
_lowerCAmelCase : Any = processor(text=__lowercase, images=__lowercase)
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
# test if it raises when no input is passed
with pytest.raises(__lowercase):
processor()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = self.get_image_processor()
_lowerCAmelCase : Tuple = self.get_tokenizer()
_lowerCAmelCase : List[Any] = CLIPSegProcessor(tokenizer=__lowercase, image_processor=__lowercase)
_lowerCAmelCase : Optional[Any] = self.prepare_image_inputs()
_lowerCAmelCase : Optional[Any] = self.prepare_image_inputs()
_lowerCAmelCase : List[Any] = processor(images=__lowercase, visual_prompt=__lowercase)
self.assertListEqual(list(inputs.keys()), ["pixel_values", "conditional_pixel_values"])
# test if it raises when no input is passed
with pytest.raises(__lowercase):
processor()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.get_image_processor()
_lowerCAmelCase : List[str] = self.get_tokenizer()
_lowerCAmelCase : Optional[Any] = CLIPSegProcessor(tokenizer=__lowercase, image_processor=__lowercase)
_lowerCAmelCase : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowerCAmelCase : Optional[Any] = processor.batch_decode(__lowercase)
_lowerCAmelCase : Any = tokenizer.batch_decode(__lowercase)
self.assertListEqual(__lowercase, __lowercase)
| 36 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : str = """ctrl"""
a__ : Dict = ["""past_key_values"""]
a__ : Tuple = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __lowercase=246_534 , __lowercase=256 , __lowercase=1_280 , __lowercase=8_192 , __lowercase=48 , __lowercase=16 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=1E-6 , __lowercase=0.02 , __lowercase=True , **__lowercase , ) -> List[Any]:
__UpperCamelCase :List[str] = vocab_size
__UpperCamelCase :Optional[Any] = n_positions
__UpperCamelCase :Dict = n_embd
__UpperCamelCase :Dict = n_layer
__UpperCamelCase :List[Any] = n_head
__UpperCamelCase :int = dff
__UpperCamelCase :Union[str, Any] = resid_pdrop
__UpperCamelCase :Optional[int] = embd_pdrop
__UpperCamelCase :List[Any] = layer_norm_epsilon
__UpperCamelCase :Dict = initializer_range
__UpperCamelCase :Any = use_cache
super().__init__(**__lowercase)
| 43 | 0 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
__snake_case : int = b * b - 4 * a * c
__snake_case : Optional[int] = (-b + sqrt(_UpperCAmelCase )) / (2 * a)
__snake_case : List[Any] = (-b - sqrt(_UpperCAmelCase )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def a_ ( ) -> Optional[int]:
__snake_case : Union[str, Any] = quadratic_roots(a=5 ,b=6 ,c=1 )
print(f'''The solutions are: {solutiona} and {solutiona}''' )
if __name__ == "__main__":
main()
| 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
lowercase_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _snake_case( ) -> int:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__ ) )
A__ = os.path.join(SCREAMING_SNAKE_CASE__ , 'words.txt' )
A__ = ''
with open(SCREAMING_SNAKE_CASE__ ) as f:
A__ = f.readline()
A__ = [word.strip('"' ) for word in words.strip('\r\n' ).split(',' )]
A__ = [
word
for word in [sum(ord(SCREAMING_SNAKE_CASE__ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
print(solution())
| 7 |
"""simple docstring"""
def lowercase ( _snake_case : int , _snake_case : int ) ->str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__snake_case : Tuple = str(bin(_snake_case ) )[2:] # remove the leading "0b"
__snake_case : List[Any] = str(bin(_snake_case ) )[2:]
__snake_case : Any = max(len(_snake_case ) , len(_snake_case ) )
return "0b" + "".join(
str(int('''1''' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(_snake_case ) , b_binary.zfill(_snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE :List[Any] = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormerConfig''',
'''Blip2VisionConfig''',
],
'''processing_blip_2''': ['''Blip2Processor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Optional[int] = [
'''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Blip2Model''',
'''Blip2QFormerModel''',
'''Blip2PreTrainedModel''',
'''Blip2ForConditionalGeneration''',
'''Blip2VisionModel''',
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE :List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 359 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : int ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = [False] * len(__lowercase )
_UpperCAmelCase = []
queue.append(__lowercase )
_UpperCAmelCase = True
while queue:
_UpperCAmelCase = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(__lowercase )
_UpperCAmelCase = True
_UpperCAmelCase = u
return visited[t]
def UpperCAmelCase_ ( __lowercase : int , __lowercase : List[Any] , __lowercase : List[str] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = [-1] * (len(__lowercase ))
_UpperCAmelCase = 0
while bfs(__lowercase , __lowercase , __lowercase , __lowercase ):
_UpperCAmelCase = float("Inf" )
_UpperCAmelCase = sink
while s != source:
# Find the minimum value in select path
_UpperCAmelCase = min(__lowercase , graph[parent[s]][s] )
_UpperCAmelCase = parent[s]
max_flow += path_flow
_UpperCAmelCase = sink
while v != source:
_UpperCAmelCase = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_UpperCAmelCase = parent[v]
return max_flow
__SCREAMING_SNAKE_CASE :Union[str, Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Optional[Any] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 156 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
UpperCamelCase = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class __UpperCAmelCase (_UpperCAmelCase ):
def __init__( self: Optional[Any] , *UpperCAmelCase_: Optional[Any] , **UpperCAmelCase_: str ):
'''simple docstring'''
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_ )
requires_backends(self , """vision""" )
self.check_model_type(UpperCAmelCase_ )
def __call__( self: List[str] , UpperCAmelCase_: Union[str, List[str], "Image.Image", List["Image.Image"]] , **UpperCAmelCase_: List[Any] ):
'''simple docstring'''
return super().__call__(UpperCAmelCase_ , **UpperCAmelCase_ )
def UpperCamelCase ( self: Dict , **UpperCAmelCase_: int ):
'''simple docstring'''
return {}, {}, {}
def UpperCamelCase ( self: Union[str, Any] , UpperCAmelCase_: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = load_image(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = image.size
_SCREAMING_SNAKE_CASE = self.image_processor(images=UpperCAmelCase_ , return_tensors=self.framework )
return model_inputs
def UpperCamelCase ( self: List[str] , UpperCAmelCase_: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.model(**UpperCAmelCase_ )
return model_outputs
def UpperCamelCase ( self: Union[str, Any] , UpperCAmelCase_: List[str] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = model_outputs.predicted_depth
_SCREAMING_SNAKE_CASE = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode="""bicubic""" , align_corners=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = prediction.squeeze().cpu().numpy()
_SCREAMING_SNAKE_CASE = (output * 255 / np.max(UpperCAmelCase_ )).astype("""uint8""" )
_SCREAMING_SNAKE_CASE = Image.fromarray(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = predicted_depth
_SCREAMING_SNAKE_CASE = depth
return output_dict
| 306 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(snake_case__ )
_SCREAMING_SNAKE_CASE = [[0] * n for i in range(snake_case__ )]
for i in range(snake_case__ ):
_SCREAMING_SNAKE_CASE = y_points[i]
for i in range(2 ,snake_case__ ):
for j in range(snake_case__ ,snake_case__ ):
_SCREAMING_SNAKE_CASE = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 306 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase__ ( lowercase_, unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = DiTPipeline
_SCREAMING_SNAKE_CASE = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_SCREAMING_SNAKE_CASE = PipelineTesterMixin.required_optional_params - {
"""latents""",
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
_SCREAMING_SNAKE_CASE = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_SCREAMING_SNAKE_CASE = False
def SCREAMING_SNAKE_CASE__ ( self : Any ):
torch.manual_seed(0 )
lowerCAmelCase_ : Union[str, Any] = TransformeraDModel(
sample_size=1_6 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=SCREAMING_SNAKE_CASE_ , activation_fn='gelu-approximate' , num_embeds_ada_norm=1_0_0_0 , norm_type='ada_norm_zero' , norm_elementwise_affine=SCREAMING_SNAKE_CASE_ , )
lowerCAmelCase_ : List[Any] = AutoencoderKL()
lowerCAmelCase_ : List[Any] = DDIMScheduler()
lowerCAmelCase_ : List[str] = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def SCREAMING_SNAKE_CASE__ ( self : Tuple , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[int]=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith('mps' ):
lowerCAmelCase_ : List[str] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase_ : List[str] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Tuple = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Any ):
lowerCAmelCase_ : Any = 'cpu'
lowerCAmelCase_ : List[str] = self.get_dummy_components()
lowerCAmelCase_ : Optional[Any] = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = pipe(**SCREAMING_SNAKE_CASE_ ).images
lowerCAmelCase_ : List[str] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 1_6, 1_6, 3) )
lowerCAmelCase_ : int = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
lowerCAmelCase_ : int = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1E-3 )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
self._test_inference_batch_single_identical(relax_max_difference=SCREAMING_SNAKE_CASE_ , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : List[Any] = torch.manual_seed(0 )
lowerCAmelCase_ : Any = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
lowerCAmelCase_ : Union[str, Any] = ['vase', 'umbrella', 'white shark', 'white wolf']
lowerCAmelCase_ : Tuple = pipe.get_label_ids(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[Any] = pipe(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=4_0 , output_type='np' ).images
for word, image in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase_ : Optional[Any] = load_numpy(
F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy" )
assert np.abs((expected_image - image).max() ) < 1E-2
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
lowerCAmelCase_ : List[Any] = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
lowerCAmelCase_ : Any = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
lowerCAmelCase_ : Optional[Any] = ['vase', 'umbrella']
lowerCAmelCase_ : Dict = pipe.get_label_ids(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = torch.manual_seed(0 )
lowerCAmelCase_ : int = pipe(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=2_5 , output_type='np' ).images
for word, image in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase_ : Optional[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
F"/dit/{word}_512.npy" )
assert np.abs((expected_image - image).max() ) < 1E-1
| 289 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : Dict = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : str = [
"""FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FalconForCausalLM""",
"""FalconModel""",
"""FalconPreTrainedModel""",
"""FalconForSequenceClassification""",
"""FalconForTokenClassification""",
"""FalconForQuestionAnswering""",
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
lowercase__ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 289 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : int = ["MLukeTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
SCREAMING_SNAKE_CASE : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
@add_end_docstrings(_a )
class _lowerCamelCase( _a ):
def __init__( self, *lowerCamelCase, **lowerCamelCase) -> int:
"""simple docstring"""
super().__init__(*lowerCamelCase, **lowerCamelCase)
requires_backends(self, 'vision')
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)
def UpperCamelCase ( self, lowerCamelCase=None) -> int:
"""simple docstring"""
_lowercase : Dict = {}
if top_k is not None:
_lowercase : List[str] = top_k
return {}, {}, postprocess_params
def __call__( self, lowerCamelCase, **lowerCamelCase) -> Tuple:
"""simple docstring"""
return super().__call__(lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase) -> str:
"""simple docstring"""
_lowercase : Optional[Any] = load_image(lowerCamelCase)
_lowercase : List[str] = self.image_processor(images=lowerCamelCase, return_tensors=self.framework)
return model_inputs
def UpperCamelCase ( self, lowerCamelCase) -> List[str]:
"""simple docstring"""
_lowercase : Optional[int] = self.model(**lowerCamelCase)
return model_outputs
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase=5) -> Dict:
"""simple docstring"""
if top_k > self.model.config.num_labels:
_lowercase : List[Any] = self.model.config.num_labels
if self.framework == "pt":
_lowercase : int = model_outputs.logits.softmax(-1)[0]
_lowercase , _lowercase : Union[str, Any] = probs.topk(lowerCamelCase)
elif self.framework == "tf":
_lowercase : int = stable_softmax(model_outputs.logits, axis=-1)[0]
_lowercase : List[Any] = tf.math.top_k(lowerCamelCase, k=lowerCamelCase)
_lowercase , _lowercase : Any = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(F'''Unsupported framework: {self.framework}''')
_lowercase : str = scores.tolist()
_lowercase : str = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase, lowerCamelCase)]
| 21 | 1 |
from math import factorial, pi
def __a ( lowerCAmelCase_ : float ,lowerCAmelCase_ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ ,(int, float) ):
raise ValueError("""maclaurin_sin() requires either an int or float for theta""" )
if not isinstance(lowerCAmelCase_ ,lowerCAmelCase_ ) or accuracy <= 0:
raise ValueError("""maclaurin_sin() requires a positive int for accuracy""" )
UpperCAmelCase_= float(lowerCAmelCase_ )
UpperCAmelCase_= theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(lowerCAmelCase_ ) )
def __a ( lowerCAmelCase_ : float ,lowerCAmelCase_ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ ,(int, float) ):
raise ValueError("""maclaurin_cos() requires either an int or float for theta""" )
if not isinstance(lowerCAmelCase_ ,lowerCAmelCase_ ) or accuracy <= 0:
raise ValueError("""maclaurin_cos() requires a positive int for accuracy""" )
UpperCAmelCase_= float(lowerCAmelCase_ )
UpperCAmelCase_= theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(lowerCAmelCase_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15))
| 354 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
'''
__A = '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''
__A = '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
\'google_bleu\': google_bleu score
Examples:
Example 1:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.44
Example 2:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.61
Example 3:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results["google_bleu"], 2))
0.53
Example 4:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results["google_bleu"], 2))
0.4
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowercase ( datasets.Metric):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ),
"""references""": datasets.Sequence(
datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ),
} ) , )
def _SCREAMING_SNAKE_CASE ( self : Any , __UpperCAmelCase : List[List[List[str]]] , __UpperCAmelCase : List[List[str]] , __UpperCAmelCase : int = 1 , __UpperCAmelCase : int = 4 , ) -> Dict[str, float]:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=__UpperCAmelCase , hypotheses=__UpperCAmelCase , min_len=__UpperCAmelCase , max_len=__UpperCAmelCase )
}
| 277 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
lowercase_ = argparse.ArgumentParser()
parser.add_argument('--user', type=str, default='ubuntu')
parser.add_argument('--host', type=str, default='localhost')
parser.add_argument('--key_path', type=str, default=None)
parser.add_argument('--instance', type=str, default='V100:1')
parser.add_argument('--provider', type=str, default='cheapest')
parser.add_argument('--use_spot', type=bool, default=False)
parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
lowercase_ , lowercase_ = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('Cannot specify both BYO and on-demand cluster args')
lowercase_ = rh.cluster(
name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
)
else:
lowercase_ = rh.cluster(
name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
lowercase_ = args.example.rsplit('/', 1)[0]
# Set up remote environment
cluster.install_packages(['pip:./']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([f"pip install -r transformers/examples/{example_dir}/requirements.txt"])
cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([f"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 205 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
"GroupViTTextConfig",
"GroupViTVisionConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GroupViTModel",
"GroupViTPreTrainedModel",
"GroupViTTextModel",
"GroupViTVisionModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGroupViTModel",
"TFGroupViTPreTrainedModel",
"TFGroupViTTextModel",
"TFGroupViTVisionModel",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/beit-base-patch16-224-pt22k": (
"https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase :Optional[Any] = "beit"
def __init__( self , _UpperCAmelCase=8192 , _UpperCAmelCase=768 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=3072 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1e-1_2 , _UpperCAmelCase=224 , _UpperCAmelCase=16 , _UpperCAmelCase=3 , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=True , _UpperCAmelCase=[3, 5, 7, 11] , _UpperCAmelCase=[1, 2, 3, 6] , _UpperCAmelCase=True , _UpperCAmelCase=0.4 , _UpperCAmelCase=256 , _UpperCAmelCase=1 , _UpperCAmelCase=False , _UpperCAmelCase=255 , **_UpperCAmelCase , ):
super().__init__(**_UpperCAmelCase )
lowercase__: Union[str, Any] = vocab_size
lowercase__: List[Any] = hidden_size
lowercase__: Optional[int] = num_hidden_layers
lowercase__: Optional[int] = num_attention_heads
lowercase__: int = intermediate_size
lowercase__: List[str] = hidden_act
lowercase__: List[Any] = hidden_dropout_prob
lowercase__: Dict = attention_probs_dropout_prob
lowercase__: List[str] = initializer_range
lowercase__: Optional[int] = layer_norm_eps
lowercase__: int = image_size
lowercase__: Tuple = patch_size
lowercase__: int = num_channels
lowercase__: Optional[Any] = use_mask_token
lowercase__: List[Any] = use_absolute_position_embeddings
lowercase__: Optional[int] = use_relative_position_bias
lowercase__: Optional[int] = use_shared_relative_position_bias
lowercase__: Optional[Any] = layer_scale_init_value
lowercase__: Union[str, Any] = drop_path_rate
lowercase__: Tuple = use_mean_pooling
# decode head attributes (semantic segmentation)
lowercase__: Tuple = out_indices
lowercase__: Optional[int] = pool_scales
# auxiliary head attributes (semantic segmentation)
lowercase__: List[str] = use_auxiliary_head
lowercase__: Optional[Any] = auxiliary_loss_weight
lowercase__: str = auxiliary_channels
lowercase__: List[str] = auxiliary_num_convs
lowercase__: Tuple = auxiliary_concat_input
lowercase__: Dict = semantic_loss_ignore_index
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase :Dict = version.parse("1.11" )
@property
def _snake_case ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _snake_case ( self ):
return 1e-4
| 2 | """simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
__A = 2_5_6
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase :int = ["melgan"]
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
super().__init__()
# From MELGAN
lowercase__: Union[str, Any] = math.log(1e-5 ) # Matches MelGAN training.
lowercase__: Union[str, Any] = 4.0 # Largest value for most examples
lowercase__: Union[str, Any] = 128
self.register_modules(
notes_encoder=_UpperCAmelCase , continuous_encoder=_UpperCAmelCase , decoder=_UpperCAmelCase , scheduler=_UpperCAmelCase , melgan=_UpperCAmelCase , )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase=(-1.0, 1.0) , _UpperCAmelCase=False ):
lowercase__, lowercase__: int = output_range
if clip:
lowercase__: Any = torch.clip(_UpperCAmelCase , self.min_value , self.max_value )
# Scale to [0, 1].
lowercase__: Optional[int] = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase=(-1.0, 1.0) , _UpperCAmelCase=False ):
lowercase__, lowercase__: str = input_range
lowercase__: Dict = torch.clip(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) if clip else outputs
# Scale to [0, 1].
lowercase__: Tuple = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: List[str] = input_tokens > 0
lowercase__, lowercase__: str = self.notes_encoder(
encoder_input_tokens=_UpperCAmelCase , encoder_inputs_mask=_UpperCAmelCase )
lowercase__, lowercase__: Optional[int] = self.continuous_encoder(
encoder_inputs=_UpperCAmelCase , encoder_inputs_mask=_UpperCAmelCase )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Tuple = noise_time
if not torch.is_tensor(_UpperCAmelCase ):
lowercase__: Tuple = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(_UpperCAmelCase ) and len(timesteps.shape ) == 0:
lowercase__: str = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowercase__: Dict = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
lowercase__: Union[str, Any] = self.decoder(
encodings_and_masks=_UpperCAmelCase , decoder_input_tokens=_UpperCAmelCase , decoder_noise_time=_UpperCAmelCase )
return logits
@torch.no_grad()
def __call__( self , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = 100 , _UpperCAmelCase = True , _UpperCAmelCase = "numpy" , _UpperCAmelCase = None , _UpperCAmelCase = 1 , ):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or callback_steps <= 0)
):
raise ValueError(
F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
F""" {type(_UpperCAmelCase )}.""" )
lowercase__: List[str] = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
lowercase__: Any = np.zeros([1, 0, self.n_dims] , np.floataa )
lowercase__: Tuple = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=_UpperCAmelCase , device=self.device )
for i, encoder_input_tokens in enumerate(_UpperCAmelCase ):
if i == 0:
lowercase__: str = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
lowercase__: Optional[int] = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=_UpperCAmelCase , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
lowercase__: Union[str, Any] = ones
lowercase__: str = self.scale_features(
_UpperCAmelCase , output_range=[-1.0, 1.0] , clip=_UpperCAmelCase )
lowercase__: Dict = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=_UpperCAmelCase , continuous_mask=_UpperCAmelCase , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
lowercase__: int = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=_UpperCAmelCase , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(_UpperCAmelCase )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowercase__: List[Any] = self.decode(
encodings_and_masks=_UpperCAmelCase , input_tokens=_UpperCAmelCase , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
lowercase__: Union[str, Any] = self.scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , generator=_UpperCAmelCase ).prev_sample
lowercase__: int = self.scale_to_features(_UpperCAmelCase , input_range=[-1.0, 1.0] )
lowercase__: Dict = mel[:1]
lowercase__: List[Any] = mel.cpu().float().numpy()
lowercase__: Optional[int] = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_UpperCAmelCase , _UpperCAmelCase )
logger.info('''Generated segment''' , _UpperCAmelCase )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
'''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
'''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )
if output_type == "numpy":
lowercase__: Tuple = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
lowercase__: Dict = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=_UpperCAmelCase )
| 2 | 1 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : Optional[Any] = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class A__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = GPTSwaTokenizer
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase : Optional[Any] = GPTSwaTokenizer(_snake_case , eos_token="<unk>" , bos_token="<unk>" , pad_token="<unk>")
tokenizer.save_pretrained(self.tmpdirname)
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: Tuple) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = '''This is a test'''
__lowerCAmelCase : Optional[Any] = '''This is a test'''
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : int = '''<s>'''
__lowerCAmelCase : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case) , _snake_case)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case) , _snake_case)
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Tuple = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , "<unk>")
self.assertEqual(vocab_keys[1] , "<s>")
self.assertEqual(vocab_keys[-1] , "j")
self.assertEqual(len(_snake_case) , 2000)
def _SCREAMING_SNAKE_CASE ( self: Any) -> Optional[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 2000)
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Dict:
"""simple docstring"""
__lowerCAmelCase : int = GPTSwaTokenizer(_snake_case)
__lowerCAmelCase : Dict = tokenizer.tokenize("This is a test")
self.assertListEqual(_snake_case , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case) , [465, 287, 265, 631, 842])
__lowerCAmelCase : str = tokenizer.tokenize("I was born in 92000, and this is falsé.")
# fmt: off
self.assertListEqual(
_snake_case , ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."] , )
# fmt: on
__lowerCAmelCase : int = tokenizer.convert_tokens_to_ids(_snake_case)
self.assertListEqual(
_snake_case , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] , )
__lowerCAmelCase : List[str] = tokenizer.convert_ids_to_tokens(_snake_case)
# fmt: off
self.assertListEqual(
_snake_case , ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."])
# fmt: on
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = GPTSwaTokenizer(_snake_case)
__lowerCAmelCase : Any = ['''This is a test''', '''I was born in 92000, and this is falsé.''']
__lowerCAmelCase : Dict = [
[465, 287, 265, 631, 842],
[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(_snake_case , _snake_case):
self.assertListEqual(tokenizer.encode_fast(_snake_case) , _snake_case)
# Test that decode_fast returns the input text
for text, token_ids in zip(_snake_case , _snake_case):
self.assertEqual(tokenizer.decode_fast(_snake_case) , _snake_case)
@slow
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Any = [
'''<|python|>def fibonacci(n)\n if n < 0:\n print(\'Incorrect input\')''',
'''Hey there, how are you doing this fine day?''',
'''This is a text with a trailing spaces followed by a dot .''',
'''Häj sväjs lillebrör! =)''',
'''Det är inget fel på Mr. Cool''',
]
# fmt: off
__lowerCAmelCase : str = {'''input_ids''': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_snake_case , model_name="AI-Sweden/gpt-sw3-126m" , sequences=_snake_case , ) | 269 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : str = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : int = [
"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 : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 156 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a__ ( UpperCamelCase__ ):
a : Any = (DDIMParallelScheduler,)
a : Dict = (("""eta""", 0.0), ("""num_inference_steps""", 50))
def lowerCAmelCase_ ( self , **A ) -> List[str]:
'''simple docstring'''
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"clip_sample": True,
}
config.update(**A )
return config
def lowerCAmelCase_ ( self , **A ) -> Any:
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**A )
a = scheduler_class(**A )
a , a = 10, 0.0
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(A )
for t in scheduler.timesteps:
a = model(A , A )
a = scheduler.step(A , A , A , A ).prev_sample
return sample
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=A )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=A )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(steps_offset=1 )
a = scheduler_class(**A )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def lowerCAmelCase_ ( self ) -> int:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=A , beta_end=A )
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=A )
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=A )
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=A )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=A )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
self.check_over_configs(thresholding=A )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=A , prediction_type=A , sample_max_value=A , )
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
for t in [1, 10, 49]:
self.check_over_forward(time_step=A )
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=A , num_inference_steps=A )
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=A , eta=A )
def lowerCAmelCase_ ( self ) -> List[str]:
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**A )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.1_4_7_7_1 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.3_2_4_6_0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.0_2 ) ) < 1e-5
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**A )
a , a = 10, 0.0
scheduler.set_timesteps(A )
a = self.dummy_model()
a = self.dummy_sample_deter
a = self.dummy_sample_deter + 0.1
a = self.dummy_sample_deter - 0.1
a = samplea.shape[0]
a = torch.stack([samplea, samplea, samplea] , dim=0 )
a = torch.arange(A )[0:3, None].repeat(1 , A )
a = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
a = scheduler.batch_step_no_noise(A , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , A )
a = torch.sum(torch.abs(A ) )
a = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 1147.7904 ) < 1e-2
assert abs(result_mean.item() - 0.4_9_8_2 ) < 1e-3
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
a = self.full_loop()
a = torch.sum(torch.abs(A ) )
a = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 172.0067 ) < 1e-2
assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1e-3
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
a = self.full_loop(prediction_type="v_prediction" )
a = torch.sum(torch.abs(A ) )
a = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 52.5302 ) < 1e-2
assert abs(result_mean.item() - 0.0_6_8_4 ) < 1e-3
def lowerCAmelCase_ ( self ) -> str:
'''simple docstring'''
a = self.full_loop(set_alpha_to_one=A , beta_start=0.0_1 )
a = torch.sum(torch.abs(A ) )
a = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 149.8295 ) < 1e-2
assert abs(result_mean.item() - 0.1_9_5_1 ) < 1e-3
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
a = self.full_loop(set_alpha_to_one=A , beta_start=0.0_1 )
a = torch.sum(torch.abs(A ) )
a = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 149.0784 ) < 1e-2
assert abs(result_mean.item() - 0.1_9_4_1 ) < 1e-3
| 350 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : Dict = "▁"
lowercase__ : Union[str, Any] = {"vocab_file": "spiece.model"}
lowercase__ : Union[str, Any] = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
lowercase__ : Tuple = {
"google/reformer-crime-and-punishment": 524_288,
}
class a__ ( UpperCamelCase__ ):
a : List[Any] = VOCAB_FILES_NAMES
a : List[Any] = PRETRAINED_VOCAB_FILES_MAP
a : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : Dict = ["""input_ids""", """attention_mask"""]
def __init__( self , A , A="</s>" , A="<unk>" , A=[] , A = None , **A , ) -> None:
'''simple docstring'''
a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A , unk_token=A , additional_special_tokens=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
a = vocab_file
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
@property
def lowerCAmelCase_ ( self ) -> Optional[Any]:
'''simple docstring'''
return self.sp_model.get_piece_size()
def lowerCAmelCase_ ( self ) -> Dict[str, int]:
'''simple docstring'''
a = {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 ) -> Optional[int]:
'''simple docstring'''
a = self.__dict__.copy()
a = None
return state
def __setstate__( self , A ) -> Union[str, Any]:
'''simple docstring'''
a = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCAmelCase_ ( self , A ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(A , out_type=A )
def lowerCAmelCase_ ( self , A ) -> Union[str, Any]:
'''simple docstring'''
return self.sp_model.piece_to_id(A )
def lowerCAmelCase_ ( self , A ) -> Optional[int]:
'''simple docstring'''
if index < self.sp_model.get_piece_size():
a = self.sp_model.IdToPiece(A )
return token
def lowerCAmelCase_ ( self , A ) -> Union[str, Any]:
'''simple docstring'''
a = []
a = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A ) + token
a = []
else:
current_sub_tokens.append(A )
out_string += self.sp_model.decode(A )
return out_string.strip()
def lowerCAmelCase_ ( self , A , A = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(A ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a = 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:
a = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 180 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ , lowerCAmelCase_ ):
_snake_case : Union[str, Any] = 'maskformer-swin'
_snake_case : int = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Dict , __lowerCAmelCase : Tuple=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : List[str]=3 , __lowerCAmelCase : str=96 , __lowerCAmelCase : List[str]=[2, 2, 6, 2] , __lowerCAmelCase : Tuple=[3, 6, 12, 24] , __lowerCAmelCase : Any=7 , __lowerCAmelCase : Optional[Any]=4.0 , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Any=0.0 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Optional[Any]="gelu" , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : Dict=0.02 , __lowerCAmelCase : Tuple=1e-5 , __lowerCAmelCase : Tuple=None , __lowerCAmelCase : Union[str, Any]=None , **__lowerCAmelCase : Optional[int] , ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(__lowerCAmelCase )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(__lowerCAmelCase ) - 1) )
_UpperCAmelCase = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(__lowerCAmelCase ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 289 | """simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class a :
def __init__( self : Union[str, Any] ):
_UpperCAmelCase = {}
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase : str ):
_UpperCAmelCase = {}
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : float ):
if nodea not in self.connections:
self.add_node(__lowerCAmelCase )
if nodea not in self.connections:
self.add_node(__lowerCAmelCase )
_UpperCAmelCase = probability
def lowerCAmelCase_ ( self : Optional[Any] ):
return list(self.connections )
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : str ):
_UpperCAmelCase = 0
_UpperCAmelCase = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(lowercase ,lowercase ,lowercase )
_UpperCAmelCase = Counter(graph.get_nodes() )
_UpperCAmelCase = start
for _ in range(lowercase ):
_UpperCAmelCase = graph.transition(lowercase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 289 | 1 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCamelCase ( _UpperCamelCase : str ) -> Tuple:
'''simple docstring'''
if is_torch_version("""<""" , """2.0.0""" ) or not hasattr(_UpperCamelCase , """_dynamo""" ):
return False
return isinstance(_UpperCamelCase , torch._dynamo.eval_frame.OptimizedModule )
def lowerCamelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : bool = True ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__UpperCAmelCase : Optional[Any] = is_compiled_module(_UpperCamelCase )
if is_compiled:
__UpperCAmelCase : Optional[Any] = model
__UpperCAmelCase : Union[str, Any] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_UpperCamelCase , _UpperCamelCase ):
__UpperCAmelCase : List[str] = model.module
if not keep_fpaa_wrapper:
__UpperCAmelCase : List[Any] = getattr(_UpperCamelCase , """forward""" )
__UpperCAmelCase : Optional[Any] = model.__dict__.pop("""_original_forward""" , _UpperCamelCase )
if original_forward is not None:
while hasattr(_UpperCamelCase , """__wrapped__""" ):
__UpperCAmelCase : int = forward.__wrapped__
if forward == original_forward:
break
__UpperCAmelCase : Any = forward
if getattr(_UpperCamelCase , """_converted_to_transformer_engine""" , _UpperCamelCase ):
convert_model(_UpperCamelCase , to_transformer_engine=_UpperCamelCase )
if is_compiled:
__UpperCAmelCase : Dict = model
__UpperCAmelCase : int = compiled_model
return model
def lowerCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
PartialState().wait_for_everyone()
def lowerCamelCase ( _UpperCamelCase : List[Any] , _UpperCamelCase : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_UpperCamelCase , _UpperCamelCase )
elif PartialState().local_process_index == 0:
torch.save(_UpperCamelCase , _UpperCamelCase )
@contextmanager
def lowerCamelCase ( **_UpperCamelCase : Optional[Any] ) -> Tuple:
'''simple docstring'''
for key, value in kwargs.items():
__UpperCAmelCase : int = str(_UpperCamelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCamelCase ( _UpperCamelCase : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
if not hasattr(_UpperCamelCase , """__qualname__""" ) and not hasattr(_UpperCamelCase , """__name__""" ):
__UpperCAmelCase : int = getattr(_UpperCamelCase , """__class__""" , _UpperCamelCase )
if hasattr(_UpperCamelCase , """__qualname__""" ):
return obj.__qualname__
if hasattr(_UpperCamelCase , """__name__""" ):
return obj.__name__
return str(_UpperCamelCase )
def lowerCamelCase ( _UpperCamelCase : Optional[Any] , _UpperCamelCase : Any ) -> List[str]:
'''simple docstring'''
for key, value in source.items():
if isinstance(_UpperCamelCase , _UpperCamelCase ):
__UpperCAmelCase : Optional[int] = destination.setdefault(_UpperCamelCase , {} )
merge_dicts(_UpperCamelCase , _UpperCamelCase )
else:
__UpperCAmelCase : Any = value
return destination
def lowerCamelCase ( _UpperCamelCase : int = None ) -> List[Any]:
'''simple docstring'''
if port is None:
__UpperCAmelCase : Tuple = 2_9_5_0_0
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(("""localhost""", port) ) == 0
| 357 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase : Optional[Any] = 'scheduler_config.json'
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = 1
__a = 2
__a = 3
__a = 4
__a = 5
__a = 6
__a = 7
__a = 8
__a = 9
__a = 10
__a = 11
__a = 12
__a = 13
__a = 14
@dataclass
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = 42
class lowerCamelCase__ :
"""simple docstring"""
__a = SCHEDULER_CONFIG_NAME
__a = []
__a = True
@classmethod
def lowerCamelCase__ ( cls : Any , UpperCamelCase : Dict[str, Any] = None , UpperCamelCase : Optional[str] = None , UpperCamelCase : Optional[Any]=False , **UpperCamelCase : int , ):
'''simple docstring'''
__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase : List[Any] = cls.load_config(
pretrained_model_name_or_path=UpperCamelCase , subfolder=UpperCamelCase , return_unused_kwargs=UpperCamelCase , return_commit_hash=UpperCamelCase , **UpperCamelCase , )
return cls.from_config(UpperCamelCase , return_unused_kwargs=UpperCamelCase , **UpperCamelCase )
def lowerCamelCase__ ( self : int , UpperCamelCase : Union[str, os.PathLike] , UpperCamelCase : bool = False , **UpperCamelCase : Optional[Any] ):
'''simple docstring'''
self.save_config(save_directory=UpperCamelCase , push_to_hub=UpperCamelCase , **UpperCamelCase )
@property
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
return self._get_compatibles()
@classmethod
def lowerCamelCase__ ( cls : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = list(set([cls.__name__] + cls._compatibles ) )
__UpperCAmelCase : List[str] = importlib.import_module(__name__.split(""".""" )[0] )
__UpperCAmelCase : List[str] = [
getattr(UpperCamelCase , UpperCamelCase ) for c in compatible_classes_str if hasattr(UpperCamelCase , UpperCamelCase )
]
return compatible_classes
| 320 | 0 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def UpperCamelCase_ ( snake_case_ : int ) -> Optional[Any]:
'''simple docstring'''
if hor == 1_28:
__lowerCAmelCase = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
__lowerCAmelCase = (32, 1_28, 2_56)
__lowerCAmelCase = ('UpResnetBlock1D', 'UpResnetBlock1D')
elif hor == 32:
__lowerCAmelCase = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
__lowerCAmelCase = (32, 64, 1_28, 2_56)
__lowerCAmelCase = ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
__lowerCAmelCase = torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
__lowerCAmelCase = model.state_dict()
__lowerCAmelCase = {
'down_block_types': down_block_types,
'block_out_channels': block_out_channels,
'up_block_types': up_block_types,
'layers_per_block': 1,
'use_timestep_embedding': True,
'out_block_type': 'OutConv1DBlock',
'norm_num_groups': 8,
'downsample_each_block': False,
'in_channels': 14,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'flip_sin_to_cos': False,
'freq_shift': 1,
'sample_size': 6_55_36,
'mid_block_type': 'MidResTemporalBlock1D',
'act_fn': 'mish',
}
__lowerCAmelCase = UNetaDModel(**snake_case_ )
print(f"""length of state dict: {len(state_dict.keys() )}""" )
print(f"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(snake_case_ )
hf_value_function.load_state_dict(snake_case_ )
torch.save(hf_value_function.state_dict() , f"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(f"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , """w""" ) as f:
json.dump(snake_case_ , snake_case_ )
def UpperCamelCase_ ( ) -> Tuple:
'''simple docstring'''
__lowerCAmelCase = {
'in_channels': 14,
'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
'up_block_types': (),
'out_block_type': 'ValueFunction',
'mid_block_type': 'ValueFunctionMidBlock1D',
'block_out_channels': (32, 64, 1_28, 2_56),
'layers_per_block': 1,
'downsample_each_block': True,
'sample_size': 6_55_36,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'use_timestep_embedding': True,
'flip_sin_to_cos': False,
'freq_shift': 1,
'norm_num_groups': 8,
'act_fn': 'mish',
}
__lowerCAmelCase = torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch""" )
__lowerCAmelCase = model
__lowerCAmelCase = UNetaDModel(**snake_case_ )
print(f"""length of state dict: {len(state_dict.keys() )}""" )
print(f"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase = state_dict.pop(snake_case_ )
hf_value_function.load_state_dict(snake_case_ )
torch.save(hf_value_function.state_dict() , """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin""" )
with open("""hub/hopper-medium-v2/value_function/config.json""" , """w""" ) as f:
json.dump(snake_case_ , snake_case_ )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 229 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
a_ :Tuple = logging.get_logger(__name__)
a_ :Union[str, Any] = {
"microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
"microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
"microsoft/deberta-v2-xlarge-mnli": (
"https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
),
"microsoft/deberta-v2-xxlarge-mnli": (
"https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
),
}
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """deberta-v2"""
def __init__( self : Union[str, Any], _snake_case : Dict=1_2_8_1_0_0, _snake_case : Any=1_5_3_6, _snake_case : Tuple=2_4, _snake_case : int=2_4, _snake_case : Optional[int]=6_1_4_4, _snake_case : Optional[int]="gelu", _snake_case : Optional[int]=0.1, _snake_case : List[str]=0.1, _snake_case : str=5_1_2, _snake_case : Optional[int]=0, _snake_case : Optional[int]=0.0_2, _snake_case : Dict=1e-7, _snake_case : int=False, _snake_case : Any=-1, _snake_case : List[str]=0, _snake_case : Tuple=True, _snake_case : Any=None, _snake_case : Union[str, Any]=0, _snake_case : Tuple="gelu", **_snake_case : Union[str, Any], ) ->Optional[int]:
super().__init__(**_snake_case )
snake_case__ : Dict = hidden_size
snake_case__ : Optional[int] = num_hidden_layers
snake_case__ : Any = num_attention_heads
snake_case__ : List[Any] = intermediate_size
snake_case__ : List[Any] = hidden_act
snake_case__ : Union[str, Any] = hidden_dropout_prob
snake_case__ : Dict = attention_probs_dropout_prob
snake_case__ : List[str] = max_position_embeddings
snake_case__ : List[str] = type_vocab_size
snake_case__ : Optional[Any] = initializer_range
snake_case__ : Optional[int] = relative_attention
snake_case__ : Tuple = max_relative_positions
snake_case__ : Union[str, Any] = pad_token_id
snake_case__ : Optional[int] = position_biased_input
# Backwards compatibility
if type(_snake_case ) == str:
snake_case__ : int = [x.strip() for x in pos_att_type.lower().split('|' )]
snake_case__ : List[str] = pos_att_type
snake_case__ : Union[str, Any] = vocab_size
snake_case__ : Optional[int] = layer_norm_eps
snake_case__ : Optional[int] = kwargs.get('pooler_hidden_size', _snake_case )
snake_case__ : int = pooler_dropout
snake_case__ : str = pooler_hidden_act
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
@property
def lowercase_ ( self : Optional[int] ) ->Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case__ : List[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
snake_case__ : int = {0: 'batch', 1: 'sequence'}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
else:
return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )
@property
def lowercase_ ( self : Dict ) ->int:
return 1_2
def lowercase_ ( self : Tuple, _snake_case : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case : int = -1, _snake_case : int = -1, _snake_case : int = -1, _snake_case : bool = False, _snake_case : Optional["TensorType"] = None, _snake_case : int = 3, _snake_case : int = 4_0, _snake_case : int = 4_0, _snake_case : "PreTrainedTokenizerBase" = None, ) ->Mapping[str, Any]:
snake_case__ : Union[str, Any] = super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 277 | 0 |
import math
def A(__a: int = 100 ):
lowerCAmelCase_ = sum(i * i for i in range(1 , n + 1 ) )
lowerCAmelCase_ = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
| 362 |
def A():
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCamelCase__ = generate_large_matrix()
lowerCamelCase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def A(__a: list[list[int]] ):
assert all(row == sorted(__a , reverse=__a ) for row in grid )
assert all(list(__a ) == sorted(__a , reverse=__a ) for col in zip(*__a ) )
def A(__a: list[int] ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(__a ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowerCAmelCase_ = (left + right) // 2
lowerCAmelCase_ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowerCAmelCase_ = mid + 1
else:
lowerCAmelCase_ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__a )
def A(__a: list[list[int]] ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(grid[0] )
for i in range(len(__a ) ):
lowerCAmelCase_ = find_negative_index(grid[i][:bound] )
total += bound
return (len(__a ) * len(grid[0] )) - total
def A(__a: list[list[int]] ):
return len([number for row in grid for number in row if number < 0] )
def A(__a: list[list[int]] ):
lowerCAmelCase_ = 0
for row in grid:
for i, number in enumerate(__a ):
if number < 0:
total += len(__a ) - i
break
return total
def A():
from timeit import timeit
print("Running benchmarks" )
lowerCAmelCase_ = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowerCAmelCase_ = timeit(F"{func}(grid=grid)" , setup=__a , number=500 )
print(F"{func}() took {time:0.4f} seconds" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 22 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : List[str] = logging.get_logger(__name__)
lowerCamelCase : Optional[Any] = {
'microsoft/beit-base-patch16-224-pt22k': (
'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
lowerCAmelCase__ : Dict = """beit"""
def __init__(self : Any , UpperCamelCase : Union[str, Any]=8192 , UpperCamelCase : Optional[Any]=768 , UpperCamelCase : str=12 , UpperCamelCase : Optional[int]=12 , UpperCamelCase : Optional[int]=3072 , UpperCamelCase : str="gelu" , UpperCamelCase : Any=0.0 , UpperCamelCase : List[Any]=0.0 , UpperCamelCase : List[str]=0.02 , UpperCamelCase : Union[str, Any]=1E-12 , UpperCamelCase : Optional[Any]=224 , UpperCamelCase : Tuple=16 , UpperCamelCase : Union[str, Any]=3 , UpperCamelCase : Optional[Any]=False , UpperCamelCase : Optional[Any]=False , UpperCamelCase : Union[str, Any]=False , UpperCamelCase : Tuple=False , UpperCamelCase : Optional[int]=0.1 , UpperCamelCase : Dict=0.1 , UpperCamelCase : Union[str, Any]=True , UpperCamelCase : Union[str, Any]=[3, 5, 7, 11] , UpperCamelCase : Dict=[1, 2, 3, 6] , UpperCamelCase : List[str]=True , UpperCamelCase : Union[str, Any]=0.4 , UpperCamelCase : Optional[int]=256 , UpperCamelCase : Optional[Any]=1 , UpperCamelCase : Optional[Any]=False , UpperCamelCase : str=255 , **UpperCamelCase : Any , ):
'''simple docstring'''
super().__init__(**UpperCamelCase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = use_mask_token
lowercase__ = use_absolute_position_embeddings
lowercase__ = use_relative_position_bias
lowercase__ = use_shared_relative_position_bias
lowercase__ = layer_scale_init_value
lowercase__ = drop_path_rate
lowercase__ = use_mean_pooling
# decode head attributes (semantic segmentation)
lowercase__ = out_indices
lowercase__ = pool_scales
# auxiliary head attributes (semantic segmentation)
lowercase__ = use_auxiliary_head
lowercase__ = auxiliary_loss_weight
lowercase__ = auxiliary_channels
lowercase__ = auxiliary_num_convs
lowercase__ = auxiliary_concat_input
lowercase__ = semantic_loss_ignore_index
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = version.parse("""1.11""" )
@property
def UpperCamelCase__ (self : Dict ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def UpperCamelCase__ (self : str ):
'''simple docstring'''
return 1E-4
| 2 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
lowerCamelCase : Tuple = 'naver-clova-ix/donut-base'
class __lowerCAmelCase (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ (self : int ):
'''simple docstring'''
lowercase__ = DonutProcessor.from_pretrained(UpperCamelCase )
def UpperCamelCase__ (self : Tuple ):
'''simple docstring'''
lowercase__ = {
'''name''': '''John Doe''',
'''age''': '''99''',
'''city''': '''Atlanta''',
'''state''': '''GA''',
'''zip''': '''30301''',
'''phone''': '''123-4567''',
'''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
}
lowercase__ = (
'''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
'''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
'''<s_nicknames><s_nickname>Johnny</s_nickname>'''
'''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
)
lowercase__ = self.processor.tokenajson(UpperCamelCase )
self.assertDictEqual(UpperCamelCase , UpperCamelCase )
| 2 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 16 |
'''simple docstring'''
from statistics import mean
import numpy as np
def lowercase_ ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : Tuple = 0
# Number of processes finished
__UpperCAmelCase : Optional[int] = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
__UpperCAmelCase : Tuple = [0] * no_of_process
# List to include calculation results
__UpperCAmelCase : int = [0] * no_of_process
# Sort by arrival time.
__UpperCAmelCase : Dict = [burst_time[i] for i in np.argsort(lowerCAmelCase__ )]
__UpperCAmelCase : Union[str, Any] = [process_name[i] for i in np.argsort(lowerCAmelCase__ )]
arrival_time.sort()
while no_of_process > finished_process_count:
__UpperCAmelCase : Dict = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
__UpperCAmelCase : Any = arrival_time[i]
__UpperCAmelCase : Any = 0
# Index showing the location of the process being performed
__UpperCAmelCase : Any = 0
# Saves the current response ratio.
__UpperCAmelCase : List[str] = 0
for i in range(0 , lowerCAmelCase__ ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
__UpperCAmelCase : Dict = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
__UpperCAmelCase : Tuple = temp
__UpperCAmelCase : List[str] = i
# Calculate the turn around time
__UpperCAmelCase : Tuple = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
__UpperCAmelCase : List[str] = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def lowercase_ ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = [0] * no_of_process
for i in range(0 , lowerCAmelCase__ ):
__UpperCAmelCase : List[Any] = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
_UpperCamelCase = 5
_UpperCamelCase = ['''A''', '''B''', '''C''', '''D''', '''E''']
_UpperCamelCase = [1, 2, 3, 4, 5]
_UpperCamelCase = [1, 2, 3, 4, 5]
_UpperCamelCase = calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
_UpperCamelCase = calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
for i in range(0, no_of_process):
print(
F'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
F'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
)
print(F'average waiting time : {mean(waiting_time):.5f}')
print(F'average turn around time : {mean(turn_around_time):.5f}')
| 16 | 1 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
A: Optional[Any] = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
"models/graphormer/algos_graphormer.pyx",
]
def _snake_case ( UpperCamelCase : int ):
# Test all the extensions added in the setup
for file in FILES_TO_FIND:
if not (transformers_path / file).exists():
return False
return True
if __name__ == "__main__":
A: Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
A: Any = parser.parse_args()
if args.check_lib:
A: str = importlib.import_module("transformers")
A: Tuple = Path(transformers_module.__file__).parent
else:
A: Union[str, Any] = Path.cwd() / "build/lib/transformers"
if not test_custom_files_are_present(transformers_path):
raise ValueError("The built release does not contain the custom files. Fix this before going further!")
| 109 | from math import isqrt, loga
def snake_case ( snake_case__ :int) -> list[int]:
_A = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , snake_case__ , snake_case__):
_A = False
return [i for i in range(2 , snake_case__) if is_prime[i]]
def snake_case ( snake_case__ :int = 800_800 , snake_case__ :int = 800_800) -> int:
_A = degree * loga(snake_case__)
_A = int(snake_case__)
_A = calculate_prime_numbers(snake_case__)
_A = 0
_A = 0
_A = len(snake_case__) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left])
+ prime_numbers[left] * loga(prime_numbers[right])
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 180 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__(self : Tuple , a__ : Dict , a__ : str=7 , a__ : Dict=3 , a__ : Union[str, Any]=10 , a__ : List[Any]=18 , a__ : List[str]=30 , a__ : Optional[Any]=400 , a__ : int=True , a__ : List[Any]=None , a__ : Dict=True , a__ : List[str]=[0.5, 0.5, 0.5] , a__ : int=[0.5, 0.5, 0.5] , a__ : Any=None , ):
"""simple docstring"""
__snake_case = size if size is not None else {'''shortest_edge''': 18}
__snake_case = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
__snake_case = parent
__snake_case = batch_size
__snake_case = num_channels
__snake_case = num_frames
__snake_case = image_size
__snake_case = min_resolution
__snake_case = max_resolution
__snake_case = do_resize
__snake_case = size
__snake_case = do_normalize
__snake_case = image_mean
__snake_case = image_std
__snake_case = crop_size
def a (self : Optional[Any] ):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase , unittest.TestCase ):
A_ : Tuple = VivitImageProcessor if is_vision_available() else None
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = VivitImageProcessingTester(self )
@property
def a (self : Tuple ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a (self : List[str] ):
"""simple docstring"""
__snake_case = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a__ , '''image_mean''' ) )
self.assertTrue(hasattr(a__ , '''image_std''' ) )
self.assertTrue(hasattr(a__ , '''do_normalize''' ) )
self.assertTrue(hasattr(a__ , '''do_resize''' ) )
self.assertTrue(hasattr(a__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(a__ , '''size''' ) )
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
__snake_case = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
__snake_case = prepare_video_inputs(self.image_processor_tester , equal_resolution=a__ )
for video in video_inputs:
self.assertIsInstance(a__ , a__ )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
__snake_case = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__snake_case = image_processing(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def a (self : List[Any] ):
"""simple docstring"""
__snake_case = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__snake_case = prepare_video_inputs(self.image_processor_tester , equal_resolution=a__ , numpify=a__ )
for video in video_inputs:
self.assertIsInstance(a__ , a__ )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
__snake_case = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__snake_case = image_processing(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def a (self : Any ):
"""simple docstring"""
__snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__snake_case = prepare_video_inputs(self.image_processor_tester , equal_resolution=a__ , torchify=a__ )
for video in video_inputs:
self.assertIsInstance(a__ , a__ )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
__snake_case = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__snake_case = image_processing(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 238 |
# 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 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Optional[Any] = logging.get_logger(__name__)
snake_case_ : Tuple = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __snake_case ( a ):
UpperCAmelCase__ : List[Any] = '''sew-d'''
def __init__( self : List[str] , _snake_case : Tuple=32 , _snake_case : Dict=768 , _snake_case : str=12 , _snake_case : Optional[int]=12 , _snake_case : Optional[int]=3072 , _snake_case : str=2 , _snake_case : Tuple=512 , _snake_case : Optional[Any]=256 , _snake_case : Tuple=True , _snake_case : Dict=True , _snake_case : Optional[Any]=("p2c", "c2p") , _snake_case : int="layer_norm" , _snake_case : Any="gelu_python" , _snake_case : Any=0.1 , _snake_case : Any=0.1 , _snake_case : List[str]=0.1 , _snake_case : List[str]=0.0 , _snake_case : Optional[int]=0.1 , _snake_case : int=0.0_2 , _snake_case : Dict=1e-7 , _snake_case : str=1e-5 , _snake_case : Optional[Any]="group" , _snake_case : List[str]="gelu" , _snake_case : Union[str, Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , _snake_case : int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , _snake_case : Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , _snake_case : int=False , _snake_case : List[str]=128 , _snake_case : Optional[int]=16 , _snake_case : int=True , _snake_case : List[Any]=0.0_5 , _snake_case : str=10 , _snake_case : Any=2 , _snake_case : Tuple=0.0 , _snake_case : Tuple=10 , _snake_case : str=0 , _snake_case : List[Any]="mean" , _snake_case : str=False , _snake_case : List[Any]=False , _snake_case : Dict=256 , _snake_case : Union[str, Any]=0 , _snake_case : Union[str, Any]=1 , _snake_case : Optional[Any]=2 , **_snake_case : Dict , ):
"""simple docstring"""
super().__init__(**_snake_case , pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case)
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = feat_extract_norm
UpperCAmelCase_ = feat_extract_activation
UpperCAmelCase_ = list(_snake_case)
UpperCAmelCase_ = list(_snake_case)
UpperCAmelCase_ = list(_snake_case)
UpperCAmelCase_ = conv_bias
UpperCAmelCase_ = num_conv_pos_embeddings
UpperCAmelCase_ = num_conv_pos_embedding_groups
UpperCAmelCase_ = len(self.conv_dim)
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = squeeze_factor
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = position_buckets
UpperCAmelCase_ = share_att_key
UpperCAmelCase_ = relative_attention
UpperCAmelCase_ = norm_rel_ebd
UpperCAmelCase_ = list(_snake_case)
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = hidden_dropout
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = activation_dropout
UpperCAmelCase_ = feat_proj_dropout
UpperCAmelCase_ = final_dropout
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = feature_layer_norm_eps
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = vocab_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
F"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
F"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase_ = apply_spec_augment
UpperCAmelCase_ = mask_time_prob
UpperCAmelCase_ = mask_time_length
UpperCAmelCase_ = mask_time_min_masks
UpperCAmelCase_ = mask_feature_prob
UpperCAmelCase_ = mask_feature_length
UpperCAmelCase_ = mask_feature_min_masks
# ctc loss
UpperCAmelCase_ = ctc_loss_reduction
UpperCAmelCase_ = ctc_zero_infinity
# sequence classification
UpperCAmelCase_ = use_weighted_layer_sum
UpperCAmelCase_ = classifier_proj_size
@property
def lowerCamelCase ( self : Optional[int]):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1)
| 51 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__snake_case = logging.get_logger('''transformers.models.speecht5''')
__snake_case = {
'''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
'''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
'''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
'''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
__snake_case = {
'''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
'''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__snake_case = {
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
'''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
'''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
'''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
__snake_case = {
'''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
'''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
'''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
'''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
'''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
'''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
'''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
'''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
__snake_case = {
'''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__snake_case = {
'''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__snake_case = {
'''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
'''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
'''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
'''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
'''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
'''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
'''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
'''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
'''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
__snake_case = {
'''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
'''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
'''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
'''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
'''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
'''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
'''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
'''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
'''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
'''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
'''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
'''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
'''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
__snake_case = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__snake_case = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case = []
__snake_case = [
'''encoder.version''',
'''encoder.layers.*.norm_k.weight''',
'''encoder.layers.*.norm_k.bias''',
'''decoder.version''',
'''decoder.layers.*.norm_k.weight''',
'''decoder.layers.*.norm_k.bias''',
'''decoder.pos_emb.pe_k''',
'''speech_encoder_prenet.embed_positions._float_tensor''',
'''text_decoder_prenet.embed_positions._float_tensor''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''speech_decoder_prenet.*''',
'''speech_decoder_postnet.*''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''speech_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Tuple, _lowerCAmelCase : Dict, _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
for attribute in key.split('''.''' ):
_a = getattr(_lowerCAmelCase, _lowerCAmelCase )
if weight_type is not None:
_a = getattr(_lowerCAmelCase, _lowerCAmelCase ).shape
else:
_a = 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":
_a = value
elif weight_type == "weight_g":
_a = value
elif weight_type == "weight_v":
_a = value
elif weight_type == "bias":
_a = value
elif weight_type == "running_mean":
_a = value
elif weight_type == "running_var":
_a = value
elif weight_type == "num_batches_tracked":
_a = value
else:
_a = value
logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Tuple ):
"""simple docstring"""
for key in ignore_keys:
if key.endswith('''.*''' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
_a , _a = key.split('''.*.''' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def A_ ( _lowerCAmelCase : Any, _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : int ):
"""simple docstring"""
_a = []
if task == "s2t":
_a = hf_model.speechta.encoder.prenet.feature_encoder
_a = MAPPING_S2T
_a = IGNORE_KEYS_S2T
elif task == "t2s":
_a = None
_a = MAPPING_T2S
_a = IGNORE_KEYS_T2S
elif task == "s2s":
_a = hf_model.speechta.encoder.prenet.feature_encoder
_a = MAPPING_S2S
_a = IGNORE_KEYS_S2S
else:
raise ValueError(f'Unsupported task: {task}' )
for name, value in fairseq_dict.items():
if should_ignore(_lowerCAmelCase, _lowerCAmelCase ):
logger.info(f'{name} was ignored' )
continue
_a = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, hf_model.config.feat_extract_norm == '''group''', )
_a = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
_a , _a = key.split('''.*.''' )
if prefix in name and suffix in name:
_a = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
_a = True
if "*" in mapped_key:
_a = name.split(_lowerCAmelCase )[0].split('''.''' )[-2]
_a = mapped_key.replace('''*''', _lowerCAmelCase )
if "weight_g" in name:
_a = '''weight_g'''
elif "weight_v" in name:
_a = '''weight_v'''
elif "bias" in name:
_a = '''bias'''
elif "weight" in name:
_a = '''weight'''
elif "running_mean" in name:
_a = '''running_mean'''
elif "running_var" in name:
_a = '''running_var'''
elif "num_batches_tracked" in name:
_a = '''num_batches_tracked'''
else:
_a = None
set_recursively(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Dict, _lowerCAmelCase : List[Any], _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_a = full_name.split('''conv_layers.''' )[-1]
_a = name.split('''.''' )
_a = int(items[0] )
_a = 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.' )
_a = 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.' )
_a = 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.' )
_a = 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.' )
_a = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def A_ ( _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Dict, _lowerCAmelCase : List[Any]=None, _lowerCAmelCase : List[str]=None, _lowerCAmelCase : int=None, ):
"""simple docstring"""
if config_path is not None:
_a = SpeechTaConfig.from_pretrained(_lowerCAmelCase )
else:
_a = SpeechTaConfig()
if task == "s2t":
_a = config.max_text_positions
_a = SpeechTaForSpeechToText(_lowerCAmelCase )
elif task == "t2s":
_a = 18_76
_a = 6_00
_a = config.max_speech_positions
_a = SpeechTaForTextToSpeech(_lowerCAmelCase )
elif task == "s2s":
_a = 18_76
_a = config.max_speech_positions
_a = SpeechTaForSpeechToSpeech(_lowerCAmelCase )
else:
raise ValueError(f'Unknown task name: {task}' )
if vocab_path:
_a = SpeechTaTokenizer(_lowerCAmelCase, model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
_a = AddedToken('''<mask>''', lstrip=_lowerCAmelCase, rstrip=_lowerCAmelCase )
_a = mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
_a = SpeechTaFeatureExtractor()
_a = SpeechTaProcessor(tokenizer=_lowerCAmelCase, feature_extractor=_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
_a = torch.load(_lowerCAmelCase )
recursively_load_weights(fairseq_checkpoint['''model'''], _lowerCAmelCase, _lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if repo_id:
print('''Pushing to the hub...''' )
processor.push_to_hub(_lowerCAmelCase )
model.push_to_hub(_lowerCAmelCase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--task''',
default='''s2t''',
type=str,
help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
__snake_case = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
) | 320 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FocalNetForImageClassification""",
"""FocalNetForMaskedImageModeling""",
"""FocalNetBackbone""",
"""FocalNetModel""",
"""FocalNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 212 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class _lowerCamelCase :
def __init__( self : str , UpperCamelCase : int , UpperCamelCase : str=99 , UpperCamelCase : Optional[int]=13 , UpperCamelCase : Dict=7 , UpperCamelCase : List[Any]=9 , UpperCamelCase : Optional[int]=True , UpperCamelCase : Any=True , UpperCamelCase : Union[str, Any]=False , UpperCamelCase : Optional[int]=32 , UpperCamelCase : str=5 , UpperCamelCase : int=4 , UpperCamelCase : Optional[Any]=37 , UpperCamelCase : Tuple=8 , UpperCamelCase : Any=0.1 , UpperCamelCase : Union[str, Any]=0.002 , UpperCamelCase : List[Any]=1 , UpperCamelCase : Any=0 , UpperCamelCase : Optional[Any]=0 , UpperCamelCase : Dict=None , UpperCamelCase : str=None , ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = parent
lowerCAmelCase__ : Union[str, Any] = batch_size
lowerCAmelCase__ : List[str] = encoder_seq_length
lowerCAmelCase__ : Any = decoder_seq_length
# For common tests
lowerCAmelCase__ : Union[str, Any] = self.decoder_seq_length
lowerCAmelCase__ : List[Any] = is_training
lowerCAmelCase__ : Optional[Any] = use_attention_mask
lowerCAmelCase__ : str = use_labels
lowerCAmelCase__ : Any = vocab_size
lowerCAmelCase__ : Any = hidden_size
lowerCAmelCase__ : Optional[int] = num_hidden_layers
lowerCAmelCase__ : Any = num_attention_heads
lowerCAmelCase__ : int = d_ff
lowerCAmelCase__ : int = relative_attention_num_buckets
lowerCAmelCase__ : Union[str, Any] = dropout_rate
lowerCAmelCase__ : str = initializer_factor
lowerCAmelCase__ : Tuple = eos_token_id
lowerCAmelCase__ : List[str] = pad_token_id
lowerCAmelCase__ : str = decoder_start_token_id
lowerCAmelCase__ : Optional[Any] = None
lowerCAmelCase__ : Dict = decoder_layers
def _lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _lowerCAmelCase ( self : str , UpperCamelCase : Optional[int] , UpperCamelCase : Dict , UpperCamelCase : Optional[int] , UpperCamelCase : List[Any]=None , UpperCamelCase : List[Any]=None , UpperCamelCase : List[Any]=None , UpperCamelCase : int=None , UpperCamelCase : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
if attention_mask is None:
lowerCAmelCase__ : Optional[Any] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCAmelCase__ : List[str] = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCAmelCase__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=UpperCamelCase )
if decoder_head_mask is None:
lowerCAmelCase__ : Optional[int] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=UpperCamelCase )
if cross_attn_head_mask is None:
lowerCAmelCase__ : Optional[int] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=UpperCamelCase )
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,
}
def _lowerCAmelCase ( self : Any ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
lowerCAmelCase__ : Tuple = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCAmelCase__ : int = input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase__ : Optional[Any] = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase__ : Tuple = self.get_config()
lowerCAmelCase__ : Dict = config.num_attention_heads
lowerCAmelCase__ : Dict = self.prepare_inputs_dict(UpperCamelCase , UpperCamelCase , UpperCamelCase )
return config, input_dict
def _lowerCAmelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : Tuple = self.prepare_config_and_inputs()
return config, inputs_dict
def _lowerCAmelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _lowerCAmelCase ( self : List[Any] , UpperCamelCase : List[Any] , UpperCamelCase : Optional[int] , UpperCamelCase : List[str] , UpperCamelCase : Optional[int] , UpperCamelCase : Any , UpperCamelCase : Any , ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = UMTaModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCAmelCase__ : Optional[int] = model(
input_ids=UpperCamelCase , decoder_input_ids=UpperCamelCase , attention_mask=UpperCamelCase , decoder_attention_mask=UpperCamelCase , )
lowerCAmelCase__ : Optional[int] = model(input_ids=UpperCamelCase , decoder_input_ids=UpperCamelCase )
lowerCAmelCase__ : List[Any] = result.last_hidden_state
lowerCAmelCase__ : Any = result.past_key_values
lowerCAmelCase__ : str = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(UpperCamelCase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : List[str] , UpperCamelCase : int , UpperCamelCase : Optional[int] , UpperCamelCase : Any , UpperCamelCase : Optional[int] , UpperCamelCase : Optional[int] , ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : Dict = UMTaModel(config=UpperCamelCase ).get_decoder().to(UpperCamelCase ).eval()
# first forward pass
lowerCAmelCase__ : Optional[int] = model(UpperCamelCase , use_cache=UpperCamelCase )
lowerCAmelCase__ : Any = model(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = model(UpperCamelCase , use_cache=UpperCamelCase )
self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) )
self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) + 1 )
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCAmelCase__ : Any = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
lowerCAmelCase__ : List[str] = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase__ : List[str] = model(UpperCamelCase )["""last_hidden_state"""]
lowerCAmelCase__ : Any = model(UpperCamelCase , past_key_values=UpperCamelCase )["""last_hidden_state"""]
# select random slice
lowerCAmelCase__ : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase__ : List[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
lowerCAmelCase__ : Optional[int] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase , UpperCamelCase , atol=1E-3 ) )
def _lowerCAmelCase ( self : Optional[Any] , UpperCamelCase : str , UpperCamelCase : int , ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = UMTaModel(config=UpperCamelCase ).to(UpperCamelCase ).half().eval()
lowerCAmelCase__ : Dict = model(**UpperCamelCase )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(UpperCamelCase ).any().item() )
@require_torch
class _lowerCamelCase ( a_ , a_ , a_ , unittest.TestCase ):
_lowerCamelCase :List[Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_lowerCamelCase :List[str] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_lowerCamelCase :Optional[Any] = (
{
"conversational": UMTaForConditionalGeneration,
"feature-extraction": UMTaModel,
"summarization": UMTaForConditionalGeneration,
"text2text-generation": UMTaForConditionalGeneration,
"translation": UMTaForConditionalGeneration,
"question-answering": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_lowerCamelCase :Dict = True
_lowerCamelCase :Optional[Any] = False
_lowerCamelCase :List[str] = False
_lowerCamelCase :Dict = True
_lowerCamelCase :str = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_lowerCamelCase :Optional[int] = [0.8, 0.9]
def _lowerCAmelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : Any = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _lowerCAmelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : List[str] = UMTaModel(config_and_inputs[0] ).to(UpperCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
UpperCamelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f"""{tmpdirname}/t5_test.onnx""" , export_params=UpperCamelCase , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase )
def _lowerCAmelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : List[Any] = config_and_inputs[0]
lowerCAmelCase__ : int = UMTaForConditionalGeneration(UpperCamelCase ).eval()
model.to(UpperCamelCase )
lowerCAmelCase__ : List[Any] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=UpperCamelCase ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCamelCase ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCamelCase ),
}
for attn_name, (name, mask) in zip(UpperCamelCase , head_masking.items() ):
lowerCAmelCase__ : Union[str, Any] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
lowerCAmelCase__ : Tuple = torch.ones(
config.num_decoder_layers , config.num_heads , device=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=UpperCamelCase , return_dict_in_generate=UpperCamelCase , **UpperCamelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
lowerCAmelCase__ : str = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _lowerCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase ( unittest.TestCase ):
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : Dict = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=UpperCamelCase ).to(UpperCamelCase )
lowerCAmelCase__ : Dict = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=UpperCamelCase , legacy=UpperCamelCase )
lowerCAmelCase__ : int = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
lowerCAmelCase__ : Union[str, Any] = tokenizer(UpperCamelCase , return_tensors="""pt""" , padding=UpperCamelCase ).input_ids
# fmt: off
lowerCAmelCase__ : List[Any] = torch.tensor(
[
[ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
] )
# fmt: on
torch.testing.assert_allclose(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = model.generate(input_ids.to(UpperCamelCase ) )
lowerCAmelCase__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
lowerCAmelCase__ : Any = tokenizer.batch_decode(UpperCamelCase )
self.assertEqual(UpperCamelCase , UpperCamelCase )
| 212 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowercase_ , int(b / 2 ) ) * actual_power(lowercase_ , int(b / 2 ) )
else:
return a * actual_power(lowercase_ , int(b / 2 ) ) * actual_power(lowercase_ , int(b / 2 ) )
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
if b < 0:
return 1 / actual_power(lowercase_ , lowercase_ )
return actual_power(lowercase_ , lowercase_ )
if __name__ == "__main__":
print(power(-2, -3))
| 78 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmelCase = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 0 |
import numpy as np
def UpperCamelCase ( snake_case__ : List[str] ) -> List[Any]:
return 1 / (1 + np.exp(-vector ))
def UpperCamelCase ( snake_case__ : List[str] ) -> Union[str, Any]:
return vector * sigmoid(snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 355 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : int = "data2vec-text"
def __init__( self, SCREAMING_SNAKE_CASE_=3_0522, SCREAMING_SNAKE_CASE_=768, SCREAMING_SNAKE_CASE_=12, SCREAMING_SNAKE_CASE_=12, SCREAMING_SNAKE_CASE_=3072, SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=512, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=1e-12, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=0, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_="absolute", SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=None, **SCREAMING_SNAKE_CASE_, ) -> Optional[int]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_, bos_token_id=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = vocab_size
UpperCamelCase : Tuple = hidden_size
UpperCamelCase : int = num_hidden_layers
UpperCamelCase : Dict = num_attention_heads
UpperCamelCase : str = hidden_act
UpperCamelCase : List[str] = intermediate_size
UpperCamelCase : Optional[int] = hidden_dropout_prob
UpperCamelCase : Dict = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : List[str] = type_vocab_size
UpperCamelCase : List[Any] = initializer_range
UpperCamelCase : List[str] = layer_norm_eps
UpperCamelCase : List[str] = position_embedding_type
UpperCamelCase : Any = use_cache
UpperCamelCase : Any = classifier_dropout
class lowerCAmelCase_ ( a__ ):
@property
def snake_case_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
UpperCamelCase : List[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
UpperCamelCase : int = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 103 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 16 |
"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase_ = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
lowerCAmelCase_ = '\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n'
lowerCAmelCase_ = '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'
lowerCAmelCase_ = '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n \'meteor\': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric(\'meteor\')\n >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results["meteor"], 4))\n 0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ,id='''sequence''' ),
'''references''': datasets.Value('''string''' ,id='''sequence''' ),
} ) ,codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''] ,reference_urls=[
'''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
'''https://en.wikipedia.org/wiki/METEOR''',
] ,)
def UpperCAmelCase ( self : str ,_snake_case : Dict ) -> Dict:
"""simple docstring"""
import nltk
nltk.download('''wordnet''' )
if NLTK_VERSION >= version.Version('''3.6.5''' ):
nltk.download('''punkt''' )
if NLTK_VERSION >= version.Version('''3.6.6''' ):
nltk.download('''omw-1.4''' )
def UpperCAmelCase ( self : Dict ,_snake_case : Dict ,_snake_case : List[str] ,_snake_case : Tuple=0.9 ,_snake_case : Optional[int]=3 ,_snake_case : Union[str, Any]=0.5 ) -> List[str]:
"""simple docstring"""
if NLTK_VERSION >= version.Version('''3.6.5''' ):
lowercase__ : int = [
meteor_score.single_meteor_score(
word_tokenize(_snake_case ) ,word_tokenize(_snake_case ) ,alpha=_snake_case ,beta=_snake_case ,gamma=_snake_case )
for ref, pred in zip(_snake_case ,_snake_case )
]
else:
lowercase__ : Tuple = [
meteor_score.single_meteor_score(_snake_case ,_snake_case ,alpha=_snake_case ,beta=_snake_case ,gamma=_snake_case )
for ref, pred in zip(_snake_case ,_snake_case )
]
return {"meteor": np.mean(_snake_case )}
| 16 | 1 |
from collections.abc import Iterable
from typing import Generic, TypeVar
_lowerCamelCase : int = TypeVar("_T")
class __snake_case (Generic[_T] ):
def __init__( self : Dict , _UpperCAmelCase : Iterable[_T] | None = None ) -> List[Any]:
'''simple docstring'''
_lowerCAmelCase : list[_T] = list(iterable or [] )
_lowerCAmelCase : list[_T] = []
def __len__( self : Dict ) -> Tuple:
'''simple docstring'''
return len(self._stacka ) + len(self._stacka )
def __repr__( self : str ) -> int:
'''simple docstring'''
return f"Queue({tuple(self._stacka[::-1] + self._stacka )})"
def SCREAMING_SNAKE_CASE ( self : Dict , _UpperCAmelCase : _T ) -> Tuple:
'''simple docstring'''
self._stacka.append(__lowerCamelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Dict:
'''simple docstring'''
_lowerCAmelCase : int = self._stacka.pop
_lowerCAmelCase : Optional[Any] = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 368 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _UpperCAmelCase (UpperCamelCase_ : Sequence[float] , UpperCamelCase_ : int , UpperCamelCase_ : int ):
'''simple docstring'''
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
_lowerCAmelCase : List[str] = (low + high) // 2
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = max_subarray(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[Any] = max_subarray(UpperCamelCase_ , mid + 1 , UpperCamelCase_ )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[int] = max_cross_sum(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def _UpperCAmelCase (UpperCamelCase_ : Sequence[float] , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = float("""-inf""" ), -1
_lowerCAmelCase , _lowerCAmelCase : str = float("""-inf""" ), -1
_lowerCAmelCase : int | float = 0
for i in range(UpperCamelCase_ , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
_lowerCAmelCase : Any = summ
_lowerCAmelCase : Tuple = i
_lowerCAmelCase : int = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
_lowerCAmelCase : List[Any] = summ
_lowerCAmelCase : str = i
return max_left, max_right, (left_sum + right_sum)
def _UpperCAmelCase (UpperCamelCase_ : int ):
'''simple docstring'''
_lowerCAmelCase : str = [randint(1 , UpperCamelCase_ ) for _ in range(UpperCamelCase_ )]
_lowerCAmelCase : str = time.time()
max_subarray(UpperCamelCase_ , 0 , input_size - 1 )
_lowerCAmelCase : Any = time.time()
return end - start
def _UpperCAmelCase ():
'''simple docstring'''
_lowerCAmelCase : Any = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
_lowerCAmelCase : Any = [time_max_subarray(UpperCamelCase_ ) for input_size in input_sizes]
print("""No of Inputs\t\tTime Taken""" )
for input_size, runtime in zip(UpperCamelCase_ , UpperCamelCase_ ):
print(UpperCamelCase_ , """\t\t""" , UpperCamelCase_ )
plt.plot(UpperCamelCase_ , UpperCamelCase_ )
plt.xlabel("""Number of Inputs""" )
plt.ylabel("""Time taken in seconds""" )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 159 | 0 |
"""simple docstring"""
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 __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = KandinskyVaaImgaImgPipeline
_a = ['image_embeds', 'negative_image_embeds', 'image']
_a = [
'image_embeds',
'negative_image_embeds',
'image',
]
_a = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
_a = False
@property
def snake_case ( self : Tuple )-> int:
return 32
@property
def snake_case ( self : Union[str, Any] )-> Union[str, Any]:
return 32
@property
def snake_case ( self : Union[str, Any] )-> Optional[int]:
return self.time_input_dim
@property
def snake_case ( self : Any )-> str:
return self.time_input_dim * 4
@property
def snake_case ( self : int )-> Any:
return 100
@property
def snake_case ( self : Tuple )-> Dict:
torch.manual_seed(0 )
lowerCamelCase__ : Any ={
'''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,
}
lowerCamelCase__ : List[Any] =UNetaDConditionModel(**lowerCamelCase )
return model
@property
def snake_case ( self : str )-> Tuple:
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 snake_case ( self : str )-> Tuple:
torch.manual_seed(0 )
lowerCamelCase__ : Optional[int] =VQModel(**self.dummy_movq_kwargs )
return model
def snake_case ( self : List[Any] )-> str:
lowerCamelCase__ : List[Any] =self.dummy_unet
lowerCamelCase__ : Union[str, Any] =self.dummy_movq
lowerCamelCase__ : List[str] ={
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00_085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowerCamelCase__ : List[Any] =DDIMScheduler(**lowerCamelCase )
lowerCamelCase__ : List[str] ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def snake_case ( self : Tuple, lowerCamelCase : Any, lowerCamelCase : int=0 )-> Union[str, Any]:
lowerCamelCase__ : Any =floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
lowerCamelCase__ : str =floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1 ) ).to(
lowerCamelCase )
# create init_image
lowerCamelCase__ : Tuple =floats_tensor((1, 3, 64, 64), rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
lowerCamelCase__ : Dict =image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase__ : Union[str, Any] =Image.fromarray(np.uinta(lowerCamelCase ) ).convert('''RGB''' ).resize((256, 256) )
if str(lowerCamelCase ).startswith('''mps''' ):
lowerCamelCase__ : int =torch.manual_seed(lowerCamelCase )
else:
lowerCamelCase__ : Dict =torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
lowerCamelCase__ : Dict ={
'''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 snake_case ( self : Tuple )-> Union[str, Any]:
lowerCamelCase__ : Optional[Any] ='''cpu'''
lowerCamelCase__ : Optional[Any] =self.get_dummy_components()
lowerCamelCase__ : int =self.pipeline_class(**lowerCamelCase )
lowerCamelCase__ : str =pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : int =pipe(**self.get_dummy_inputs(lowerCamelCase ) )
lowerCamelCase__ : Any =output.images
lowerCamelCase__ : Dict =pipe(
**self.get_dummy_inputs(lowerCamelCase ), return_dict=lowerCamelCase, )[0]
lowerCamelCase__ : str =image[0, -3:, -3:, -1]
lowerCamelCase__ : Union[str, Any] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : Union[str, Any] =np.array(
[0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
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 __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self : str )-> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self : Tuple )-> str:
lowerCamelCase__ : Any =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
lowerCamelCase__ : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowerCamelCase__ : int ='''A red cartoon frog, 4k'''
lowerCamelCase__ : int =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''', torch_dtype=torch.floataa )
pipe_prior.to(lowerCamelCase )
lowerCamelCase__ : str =KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''', torch_dtype=torch.floataa )
lowerCamelCase__ : int =pipeline.to(lowerCamelCase )
pipeline.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : int =torch.Generator(device='''cpu''' ).manual_seed(0 )
lowerCamelCase__ , lowerCamelCase__ : List[str] =pipe_prior(
lowerCamelCase, generator=lowerCamelCase, num_inference_steps=5, negative_prompt='''''', ).to_tuple()
lowerCamelCase__ : Tuple =pipeline(
image=lowerCamelCase, image_embeds=lowerCamelCase, negative_image_embeds=lowerCamelCase, generator=lowerCamelCase, num_inference_steps=100, height=768, width=768, strength=0.2, output_type='''np''', )
lowerCamelCase__ : Any =output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase, lowerCamelCase )
| 238 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
def __get__( self : Tuple, lowerCamelCase : List[str], lowerCamelCase : Optional[int]=None )-> List[str]:
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
lowerCamelCase__ : List[str] ='''__cached_''' + self.fget.__name__
lowerCamelCase__ : List[Any] =getattr(lowerCamelCase, lowerCamelCase, lowerCamelCase )
if cached is None:
lowerCamelCase__ : Optional[int] =self.fget(lowerCamelCase )
setattr(lowerCamelCase, lowerCamelCase, lowerCamelCase )
return cached
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] =val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'''invalid truth value {val!r}''' )
def snake_case__ ( __lowerCamelCase : List[Any] ):
"""simple docstring"""
if is_torch_fx_proxy(__lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(__lowerCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(__lowerCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(__lowerCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(__lowerCamelCase , np.ndarray )
def snake_case__ ( __lowerCamelCase : List[Any] ):
"""simple docstring"""
return isinstance(__lowerCamelCase , np.ndarray )
def snake_case__ ( __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
return _is_numpy(__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : Any ):
"""simple docstring"""
import torch
return isinstance(__lowerCamelCase , torch.Tensor )
def snake_case__ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return False if not is_torch_available() else _is_torch(__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : List[str] ):
"""simple docstring"""
import torch
return isinstance(__lowerCamelCase , torch.device )
def snake_case__ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return False if not is_torch_available() else _is_torch_device(__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : Tuple ):
"""simple docstring"""
import torch
if isinstance(__lowerCamelCase , __lowerCamelCase ):
if hasattr(__lowerCamelCase , __lowerCamelCase ):
lowerCamelCase__ : Tuple =getattr(__lowerCamelCase , __lowerCamelCase )
else:
return False
return isinstance(__lowerCamelCase , torch.dtype )
def snake_case__ ( __lowerCamelCase : List[Any] ):
"""simple docstring"""
return False if not is_torch_available() else _is_torch_dtype(__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
import tensorflow as tf
return isinstance(__lowerCamelCase , tf.Tensor )
def snake_case__ ( __lowerCamelCase : int ):
"""simple docstring"""
return False if not is_tf_available() else _is_tensorflow(__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(__lowerCamelCase , '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(__lowerCamelCase )
return type(__lowerCamelCase ) == tf.Tensor
def snake_case__ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return False if not is_tf_available() else _is_tf_symbolic_tensor(__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
import jax.numpy as jnp # noqa: F811
return isinstance(__lowerCamelCase , jnp.ndarray )
def snake_case__ ( __lowerCamelCase : Tuple ):
"""simple docstring"""
return False if not is_flax_available() else _is_jax(__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : List[str] ):
"""simple docstring"""
if isinstance(__lowerCamelCase , (dict, UserDict) ):
return {k: to_py_obj(__lowerCamelCase ) for k, v in obj.items()}
elif isinstance(__lowerCamelCase , (list, tuple) ):
return [to_py_obj(__lowerCamelCase ) for o in obj]
elif is_tf_tensor(__lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(__lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(__lowerCamelCase ):
return np.asarray(__lowerCamelCase ).tolist()
elif isinstance(__lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def snake_case__ ( __lowerCamelCase : List[Any] ):
"""simple docstring"""
if isinstance(__lowerCamelCase , (dict, UserDict) ):
return {k: to_numpy(__lowerCamelCase ) for k, v in obj.items()}
elif isinstance(__lowerCamelCase , (list, tuple) ):
return np.array(__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(__lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(__lowerCamelCase ):
return np.asarray(__lowerCamelCase )
else:
return obj
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
def snake_case ( self : int )-> Optional[int]:
lowerCamelCase__ : Union[str, Any] =fields(self )
# Safety and consistency checks
if not len(lowerCamelCase ):
raise ValueError(F'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'''{self.__class__.__name__} should not have more than one required field.''' )
lowerCamelCase__ : List[Any] =getattr(self, class_fields[0].name )
lowerCamelCase__ : Union[str, Any] =all(getattr(self, field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(lowerCamelCase ):
if isinstance(lowerCamelCase, lowerCamelCase ):
lowerCamelCase__ : Optional[int] =first_field.items()
lowerCamelCase__ : Union[str, Any] =True
else:
try:
lowerCamelCase__ : int =iter(lowerCamelCase )
lowerCamelCase__ : List[Any] =True
except TypeError:
lowerCamelCase__ : List[Any] =False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(lowerCamelCase ):
if (
not isinstance(lowerCamelCase, (list, tuple) )
or not len(lowerCamelCase ) == 2
or not isinstance(element[0], lowerCamelCase )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
lowerCamelCase__ : Optional[int] =first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self, element[0], element[1] )
if element[1] is not None:
lowerCamelCase__ : str =element[1]
elif first_field is not None:
lowerCamelCase__ : Dict =first_field
else:
for field in class_fields:
lowerCamelCase__ : Union[str, Any] =getattr(self, field.name )
if v is not None:
lowerCamelCase__ : Optional[int] =v
def __delitem__( self : int, *lowerCamelCase : List[str], **lowerCamelCase : Optional[int] )-> str:
raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def snake_case ( self : Optional[int], *lowerCamelCase : int, **lowerCamelCase : List[str] )-> Optional[Any]:
raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def snake_case ( self : Dict, *lowerCamelCase : Optional[int], **lowerCamelCase : Optional[Any] )-> int:
raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def snake_case ( self : List[Any], *lowerCamelCase : Tuple, **lowerCamelCase : List[Any] )-> Optional[int]:
raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self : Optional[Any], lowerCamelCase : Optional[int] )-> List[Any]:
if isinstance(lowerCamelCase, lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] =dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : Union[str, Any], lowerCamelCase : List[str], lowerCamelCase : List[str] )-> Dict:
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(lowerCamelCase, lowerCamelCase )
super().__setattr__(lowerCamelCase, lowerCamelCase )
def __setitem__( self : Optional[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : int )-> List[Any]:
# Will raise a KeyException if needed
super().__setitem__(lowerCamelCase, lowerCamelCase )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(lowerCamelCase, lowerCamelCase )
def snake_case ( self : str )-> Tuple[Any]:
return tuple(self[k] for k in self.keys() )
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
@classmethod
def snake_case ( cls : Optional[Any], lowerCamelCase : int )-> str:
raise ValueError(
F'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = 'longest'
_a = 'max_length'
_a = 'do_not_pad'
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = 'pt'
_a = 'tf'
_a = 'np'
_a = 'jax'
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[int], lowerCamelCase : List[ContextManager] )-> str:
lowerCamelCase__ : List[str] =context_managers
lowerCamelCase__ : int =ExitStack()
def __enter__( self : List[str] )-> Union[str, Any]:
for context_manager in self.context_managers:
self.stack.enter_context(lowerCamelCase )
def __exit__( self : Tuple, *lowerCamelCase : Union[str, Any], **lowerCamelCase : Tuple )-> List[Any]:
self.stack.__exit__(*lowerCamelCase, **lowerCamelCase )
def snake_case__ ( __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : Tuple =infer_framework(__lowerCamelCase )
if framework == "tf":
lowerCamelCase__ : Any =inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
lowerCamelCase__ : Tuple =inspect.signature(model_class.forward ) # PyTorch models
else:
lowerCamelCase__ : List[str] =inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] =model_class.__name__
lowerCamelCase__ : Tuple =infer_framework(__lowerCamelCase )
if framework == "tf":
lowerCamelCase__ : int =inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
lowerCamelCase__ : Any =inspect.signature(model_class.forward ) # PyTorch models
else:
lowerCamelCase__ : Union[str, Any] =inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def snake_case__ ( __lowerCamelCase : MutableMapping , __lowerCamelCase : str = "" , __lowerCamelCase : str = "." ):
"""simple docstring"""
def _flatten_dict(__lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[int]="" , __lowerCamelCase : str="." ):
for k, v in d.items():
lowerCamelCase__ : List[str] =str(__lowerCamelCase ) + delimiter + str(__lowerCamelCase ) if parent_key else k
if v and isinstance(__lowerCamelCase , __lowerCamelCase ):
yield from flatten_dict(__lowerCamelCase , __lowerCamelCase , delimiter=__lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) )
@contextmanager
def snake_case__ ( __lowerCamelCase : Tuple , __lowerCamelCase : bool = False ):
"""simple docstring"""
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def snake_case__ ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict=None ):
"""simple docstring"""
if is_numpy_array(__lowerCamelCase ):
return np.transpose(__lowerCamelCase , axes=__lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.T if axes is None else array.permute(*__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.transpose(__lowerCamelCase , perm=__lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return jnp.transpose(__lowerCamelCase , axes=__lowerCamelCase )
else:
raise ValueError(f'''Type not supported for transpose: {type(__lowerCamelCase )}.''' )
def snake_case__ ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
if is_numpy_array(__lowerCamelCase ):
return np.reshape(__lowerCamelCase , __lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.reshape(*__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.reshape(__lowerCamelCase , __lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return jnp.reshape(__lowerCamelCase , __lowerCamelCase )
else:
raise ValueError(f'''Type not supported for reshape: {type(__lowerCamelCase )}.''' )
def snake_case__ ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[str]=None ):
"""simple docstring"""
if is_numpy_array(__lowerCamelCase ):
return np.squeeze(__lowerCamelCase , axis=__lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(__lowerCamelCase , axis=__lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return jnp.squeeze(__lowerCamelCase , axis=__lowerCamelCase )
else:
raise ValueError(f'''Type not supported for squeeze: {type(__lowerCamelCase )}.''' )
def snake_case__ ( __lowerCamelCase : Dict , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
if is_numpy_array(__lowerCamelCase ):
return np.expand_dims(__lowerCamelCase , __lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.unsqueeze(dim=__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(__lowerCamelCase , axis=__lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return jnp.expand_dims(__lowerCamelCase , axis=__lowerCamelCase )
else:
raise ValueError(f'''Type not supported for expand_dims: {type(__lowerCamelCase )}.''' )
def snake_case__ ( __lowerCamelCase : List[Any] ):
"""simple docstring"""
if is_numpy_array(__lowerCamelCase ):
return np.size(__lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.numel()
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.size(__lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return array.size
else:
raise ValueError(f'''Type not supported for expand_dims: {type(__lowerCamelCase )}.''' )
def snake_case__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Tuple ):
"""simple docstring"""
for key, value in auto_map.items():
if isinstance(__lowerCamelCase , (tuple, list) ):
lowerCamelCase__ : Optional[int] =[f'''{repo_id}--{v}''' if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
lowerCamelCase__ : Tuple =f'''{repo_id}--{value}'''
return auto_map
def snake_case__ ( __lowerCamelCase : Optional[int] ):
"""simple docstring"""
for base_class in inspect.getmro(__lowerCamelCase ):
lowerCamelCase__ : Tuple =base_class.__module__
lowerCamelCase__ : Tuple =base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'''Could not infer framework from class {model_class}.''' )
| 238 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_lowercase: Optional[int] = logging.get_logger(__name__)
def a( ) -> Tuple:
"""simple docstring"""
a = os.getenv("SM_HP_MP_PARAMETERS" , "{}" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
a = json.loads(A )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
a = os.getenv("SM_FRAMEWORK_PARAMS" , "{}" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
a = json.loads(A )
if not mpi_options.get("sagemaker_mpi_enabled" , A ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("smdistributed" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = field(
default="", metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}, )
def UpperCamelCase_ (self ):
"""simple docstring"""
super().__post_init__()
warnings.warn(
"`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
"`TrainingArguments` instead." , lowerCamelCase_ , )
@cached_property
def UpperCamelCase_ (self ):
"""simple docstring"""
logger.info("PyTorch: setting up devices" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"torch.distributed process group is initialized, but local_rank == -1. "
"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
if self.no_cuda:
a = torch.device("cpu" )
a = 0
elif is_sagemaker_model_parallel_available():
a = smp.local_rank()
a = torch.device("cuda" , lowerCamelCase_ )
a = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="smddp" , timeout=self.ddp_timeout_delta )
a = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
a = torch.device("cuda" , self.local_rank )
a = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
a = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
a = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="nccl" , timeout=self.ddp_timeout_delta )
a = torch.device("cuda" , self.local_rank )
a = 1
if device.type == "cuda":
torch.cuda.set_device(lowerCamelCase_ )
return device
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return not is_sagemaker_model_parallel_available()
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return False
| 358 |
def a( A : int = 200 ) -> int:
"""simple docstring"""
a = [1, 2, 5, 10, 20, 50, 100, 200]
a = [0] * (pence + 1)
a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(200) == 73682
| 71 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
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 PoolFormerImageProcessor
class A__ ( unittest.TestCase ):
def __init__( self : List[Any] , a : Tuple , a : Any=7 , a : Union[str, Any]=3 , a : str=30 , a : Optional[Any]=400 , a : Optional[Any]=True , a : List[str]=None , a : Optional[Any]=0.9 , a : Any=None , a : Optional[Any]=True , a : int=[0.5, 0.5, 0.5] , a : Optional[Any]=[0.5, 0.5, 0.5] , ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = size if size is not None else {'shortest_edge': 30}
lowerCAmelCase__ : int = crop_size if crop_size is not None else {'height': 30, 'width': 30}
lowerCAmelCase__ : Union[str, Any] = parent
lowerCAmelCase__ : str = batch_size
lowerCAmelCase__ : int = num_channels
lowerCAmelCase__ : Optional[int] = min_resolution
lowerCAmelCase__ : int = max_resolution
lowerCAmelCase__ : str = do_resize_and_center_crop
lowerCAmelCase__ : str = size
lowerCAmelCase__ : Any = crop_pct
lowerCAmelCase__ : int = crop_size
lowerCAmelCase__ : Union[str, Any] = do_normalize
lowerCAmelCase__ : str = image_mean
lowerCAmelCase__ : Optional[Any] = image_std
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class A__ ( __magic_name__ , unittest.TestCase ):
lowercase = PoolFormerImageProcessor if is_vision_available() else None
def _lowerCamelCase ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : Any = PoolFormerImageProcessingTester(self )
@property
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a , 'do_resize_and_center_crop' ) )
self.assertTrue(hasattr(a , 'size' ) )
self.assertTrue(hasattr(a , 'crop_pct' ) )
self.assertTrue(hasattr(a , 'do_normalize' ) )
self.assertTrue(hasattr(a , 'image_mean' ) )
self.assertTrue(hasattr(a , 'image_std' ) )
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 30} )
self.assertEqual(image_processor.crop_size , {'height': 30, 'width': 30} )
lowerCAmelCase__ : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
pass
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a )
for image in image_inputs:
self.assertIsInstance(a , Image.Image )
# Test not batched input
lowerCAmelCase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowerCAmelCase__ : Union[str, Any] = image_processing(a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=a , numpify=a )
for image in image_inputs:
self.assertIsInstance(a , np.ndarray )
# Test not batched input
lowerCAmelCase__ : Tuple = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowerCAmelCase__ : Tuple = image_processing(a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a , torchify=a )
for image in image_inputs:
self.assertIsInstance(a , torch.Tensor )
# Test not batched input
lowerCAmelCase__ : Any = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
lowerCAmelCase__ : Any = image_processing(a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , ) | 212 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class A__ ( __magic_name__ ):
lowercase = 'gptj'
lowercase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Optional[Any] , a : Dict=50_400 , a : Union[str, Any]=2_048 , a : List[str]=4_096 , a : Any=28 , a : Optional[Any]=16 , a : Optional[Any]=64 , a : int=None , a : Any="gelu_new" , a : Union[str, Any]=0.0 , a : List[Any]=0.0 , a : List[Any]=0.0 , a : Optional[Any]=1E-5 , a : Optional[int]=0.0_2 , a : int=True , a : str=50_256 , a : str=50_256 , a : Any=False , **a : Dict , ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = vocab_size
lowerCAmelCase__ : List[Any] = n_positions
lowerCAmelCase__ : str = n_embd
lowerCAmelCase__ : str = n_layer
lowerCAmelCase__ : str = n_head
lowerCAmelCase__ : Dict = n_inner
lowerCAmelCase__ : Union[str, Any] = rotary_dim
lowerCAmelCase__ : Optional[int] = activation_function
lowerCAmelCase__ : Any = resid_pdrop
lowerCAmelCase__ : int = embd_pdrop
lowerCAmelCase__ : int = attn_pdrop
lowerCAmelCase__ : List[Any] = layer_norm_epsilon
lowerCAmelCase__ : str = initializer_range
lowerCAmelCase__ : Dict = use_cache
lowerCAmelCase__ : str = bos_token_id
lowerCAmelCase__ : int = eos_token_id
super().__init__(
bos_token_id=a , eos_token_id=a , tie_word_embeddings=a , **a )
class A__ ( __magic_name__ ):
def __init__( self : str , a : PretrainedConfig , a : str = "default" , a : List[PatchingSpec] = None , a : bool = False , ):
'''simple docstring'''
super().__init__(a , task=a , patching_specs=a , use_past=a )
if not getattr(self._config , 'pad_token_id' , a ):
# TODO: how to do that better?
lowerCAmelCase__ : int = 0
@property
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : Dict = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(a , direction='inputs' )
lowerCAmelCase__ : Optional[Any] = {0: 'batch', 1: 'past_sequence + sequence'}
else:
lowerCAmelCase__ : Tuple = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
return self._config.n_layer
@property
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
return self._config.n_head
def _lowerCamelCase ( self : Tuple , a : PreTrainedTokenizer , a : int = -1 , a : int = -1 , a : bool = False , a : Optional[TensorType] = None , ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = super(a , self ).generate_dummy_inputs(
a , batch_size=a , seq_length=a , is_pair=a , framework=a )
# We need to order the input in the way they appears in the forward()
lowerCAmelCase__ : Optional[int] = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
lowerCAmelCase__ , lowerCAmelCase__ : int = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
lowerCAmelCase__ : Optional[int] = seqlen + 2
lowerCAmelCase__ : Tuple = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCAmelCase__ : Tuple = [
(torch.zeros(a ), torch.zeros(a )) for _ in range(self.num_layers )
]
lowerCAmelCase__ : Any = common_inputs['attention_mask']
if self.use_past:
lowerCAmelCase__ : List[str] = ordered_inputs['attention_mask'].dtype
lowerCAmelCase__ : Optional[Any] = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(a , a , dtype=a )] , dim=1 )
return ordered_inputs
@property
def _lowerCamelCase ( self : int ):
'''simple docstring'''
return 13 | 212 | 1 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int = 10_00 ):
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 8 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
'feature_extraction_whisper': ['WhisperFeatureExtractor'],
'processing_whisper': ['WhisperProcessor'],
'tokenization_whisper': ['WhisperTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = ['WhisperTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'WhisperForConditionalGeneration',
'WhisperModel',
'WhisperPreTrainedModel',
'WhisperForAudioClassification',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFWhisperForConditionalGeneration',
'TFWhisperModel',
'TFWhisperPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'FlaxWhisperForConditionalGeneration',
'FlaxWhisperModel',
'FlaxWhisperPreTrainedModel',
'FlaxWhisperForAudioClassification',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 8 | 1 |
__A = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 90 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Tuple):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def UpperCAmelCase__ ( self : List[str]):
lowerCAmelCase_ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''')
lowerCAmelCase_ : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
lowerCAmelCase_ : List[Any] = '''xvjiarui/stable-diffusion-2-inpainting'''
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = FlaxStableDiffusionInpaintPipeline.from_pretrained(A_ , safety_checker=A_)
lowerCAmelCase_ : List[str] = '''Face of a yellow cat, high resolution, sitting on a park bench'''
lowerCAmelCase_ : List[Any] = jax.random.PRNGKey(0)
lowerCAmelCase_ : str = 5_0
lowerCAmelCase_ : List[Any] = jax.device_count()
lowerCAmelCase_ : Union[str, Any] = num_samples * [prompt]
lowerCAmelCase_ : str = num_samples * [init_image]
lowerCAmelCase_ : Union[str, Any] = num_samples * [mask_image]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = pipeline.prepare_inputs(A_ , A_ , A_)
# shard inputs and rng
lowerCAmelCase_ : str = replicate(A_)
lowerCAmelCase_ : str = jax.random.split(A_ , jax.device_count())
lowerCAmelCase_ : List[Any] = shard(A_)
lowerCAmelCase_ : str = shard(A_)
lowerCAmelCase_ : Tuple = shard(A_)
lowerCAmelCase_ : int = pipeline(
A_ , A_ , A_ , A_ , A_ , A_ , jit=A_)
lowerCAmelCase_ : Optional[int] = output.images.reshape(A_ , 5_1_2 , 5_1_2 , 3)
lowerCAmelCase_ : List[str] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
lowerCAmelCase_ : int = jnp.asarray(jax.device_get(image_slice.flatten()))
lowerCAmelCase_ : str = jnp.array(
[0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084])
print(F"""output_slice: {output_slice}""")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
| 103 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : List[str] = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
"tokenization_electra": ["ElectraTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Tuple = ["ElectraTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[int] = [
"ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"ElectraForCausalLM",
"ElectraForMaskedLM",
"ElectraForMultipleChoice",
"ElectraForPreTraining",
"ElectraForQuestionAnswering",
"ElectraForSequenceClassification",
"ElectraForTokenClassification",
"ElectraModel",
"ElectraPreTrainedModel",
"load_tf_weights_in_electra",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFElectraForMaskedLM",
"TFElectraForMultipleChoice",
"TFElectraForPreTraining",
"TFElectraForQuestionAnswering",
"TFElectraForSequenceClassification",
"TFElectraForTokenClassification",
"TFElectraModel",
"TFElectraPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[int] = [
"FlaxElectraForCausalLM",
"FlaxElectraForMaskedLM",
"FlaxElectraForMultipleChoice",
"FlaxElectraForPreTraining",
"FlaxElectraForQuestionAnswering",
"FlaxElectraForSequenceClassification",
"FlaxElectraForTokenClassification",
"FlaxElectraModel",
"FlaxElectraPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
_lowercase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 1 |
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 UpperCAmelCase :
def __init__(self : List[str] , snake_case__ : str , snake_case__ : List[str]=3 , snake_case__ : int=32 , snake_case__ : Optional[int]=3 , snake_case__ : Tuple=10 , snake_case__ : Dict=[10, 20, 30, 40] , snake_case__ : List[Any]=[1, 1, 2, 1] , snake_case__ : Optional[Any]=True , snake_case__ : Dict=True , snake_case__ : Tuple="relu" , snake_case__ : Optional[int]=3 , snake_case__ : Any=None , ) -> int:
'''simple docstring'''
snake_case : Optional[int] = parent
snake_case : List[str] = batch_size
snake_case : Optional[Any] = image_size
snake_case : str = num_channels
snake_case : Dict = embeddings_size
snake_case : List[str] = hidden_sizes
snake_case : List[Any] = depths
snake_case : Union[str, Any] = is_training
snake_case : List[Any] = use_labels
snake_case : Optional[Any] = hidden_act
snake_case : Union[str, Any] = num_labels
snake_case : Union[str, Any] = scope
snake_case : List[str] = len(snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Dict ) -> Optional[int]:
'''simple docstring'''
snake_case : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case : Tuple = None
if self.use_labels:
snake_case : Tuple = ids_tensor([self.batch_size] , self.num_labels )
snake_case : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE (self : Tuple ) -> Dict:
'''simple docstring'''
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 _SCREAMING_SNAKE_CASE (self : Optional[Any] , snake_case__ : Optional[int] , snake_case__ : str , snake_case__ : Dict ) -> List[str]:
'''simple docstring'''
snake_case : int = TFResNetModel(config=snake_case__ )
snake_case : List[str] = model(snake_case__ )
# 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 _SCREAMING_SNAKE_CASE (self : Optional[Any] , snake_case__ : Any , snake_case__ : Dict , snake_case__ : str ) -> List[str]:
'''simple docstring'''
snake_case : Optional[int] = self.num_labels
snake_case : Dict = TFResNetForImageClassification(snake_case__ )
snake_case : List[Any] = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _SCREAMING_SNAKE_CASE (self : str ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case : Tuple = config_and_inputs
snake_case : Optional[int] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class UpperCAmelCase ( A_ ,A_ ,unittest.TestCase ):
A__ : Optional[int] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
A__ : Optional[Any] = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
A__ : Union[str, Any] = False
A__ : Union[str, Any] = False
A__ : List[Any] = False
A__ : Tuple = False
A__ : Optional[Any] = False
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
snake_case : int = TFResNetModelTester(self )
snake_case : Optional[int] = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ )
def _SCREAMING_SNAKE_CASE (self : int ) -> int:
'''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 _SCREAMING_SNAKE_CASE (self : str ) -> Union[str, Any]:
'''simple docstring'''
return
@unittest.skip(reason="ResNet does not use inputs_embeds" )
def _SCREAMING_SNAKE_CASE (self : Tuple ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason="ResNet does not support input and output embeddings" )
def _SCREAMING_SNAKE_CASE (self : Optional[Any] ) -> str:
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case , snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case : Dict = model_class(snake_case__ )
snake_case : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case : Union[str, Any] = [*signature.parameters.keys()]
snake_case : Tuple = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Any ) -> Tuple:
'''simple docstring'''
snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
def check_hidden_states_output(snake_case__ : Union[str, Any] , snake_case__ : str , snake_case__ : List[str] ):
snake_case : Dict = model_class(snake_case__ )
snake_case : Optional[int] = model(**self._prepare_for_class(snake_case__ , snake_case__ ) )
snake_case : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case : Optional[Any] = self.model_tester.num_stages
self.assertEqual(len(snake_case__ ) , 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] , )
snake_case , snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case : Tuple = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
snake_case : Any = layer_type
snake_case : 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"]
snake_case : str = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE (self : List[Any] ) -> List[Any]:
'''simple docstring'''
snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
@slow
def _SCREAMING_SNAKE_CASE (self : Any ) -> Any:
'''simple docstring'''
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case : str = TFResNetModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def UpperCamelCase ( ):
snake_case : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
@cached_property
def _SCREAMING_SNAKE_CASE (self : int ) -> Optional[Any]:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _SCREAMING_SNAKE_CASE (self : Optional[Any] ) -> Any:
'''simple docstring'''
snake_case : List[Any] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
snake_case : List[Any] = self.default_image_processor
snake_case : Tuple = prepare_img()
snake_case : Optional[Any] = image_processor(images=snake_case__ , return_tensors="tf" )
# forward pass
snake_case : Any = model(**snake_case__ )
# verify the logits
snake_case : Any = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , snake_case__ )
snake_case : List[str] = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , snake_case__ , atol=1e-4 ) )
| 59 |
def _lowerCAmelCase ( lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Tuple , lowerCAmelCase_ :Any )->List[Any]:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(lowerCAmelCase_ , n - 1 , lowerCAmelCase_ ) * a) % mod
else:
snake_case_ = binary_exponentiation(lowerCAmelCase_ , n / 2 , lowerCAmelCase_ )
return (b * b) % mod
# a prime number
SCREAMING_SNAKE_CASE :List[str] = 7_01
SCREAMING_SNAKE_CASE :Optional[int] = 10_00_00_00_00
SCREAMING_SNAKE_CASE :Tuple = 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)
| 159 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__name__)
A_ : str = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
'BridgeTower/bridgetower-base-itm-mlm': (
'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
),
}
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : List[Any] ='''bridgetower_vision_model'''
def __init__( self , _lowerCamelCase=7_6_8 , _lowerCamelCase=1_2 , _lowerCamelCase=3 , _lowerCamelCase=1_6 , _lowerCamelCase=2_8_8 , _lowerCamelCase=1 , _lowerCamelCase=1e-05 , _lowerCamelCase=False , _lowerCamelCase=True , _lowerCamelCase=False , **_lowerCamelCase , ):
super().__init__(**_lowerCamelCase )
UpperCamelCase_: Any = hidden_size
UpperCamelCase_: Union[str, Any] = num_hidden_layers
UpperCamelCase_: List[str] = num_channels
UpperCamelCase_: int = patch_size
UpperCamelCase_: List[Any] = image_size
UpperCamelCase_: str = initializer_factor
UpperCamelCase_: str = layer_norm_eps
UpperCamelCase_: int = stop_gradient
UpperCamelCase_: int = share_layernorm
UpperCamelCase_: Union[str, Any] = remove_last_layer
@classmethod
def _a ( cls , _lowerCamelCase , **_lowerCamelCase ):
UpperCamelCase_ ,UpperCamelCase_: Optional[Any] = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
if config_dict.get('model_type' ) == "bridgetower":
UpperCamelCase_: int = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : List[str] ='''bridgetower_text_model'''
def __init__( self , _lowerCamelCase=5_0_2_6_5 , _lowerCamelCase=7_6_8 , _lowerCamelCase=1_2 , _lowerCamelCase=1_2 , _lowerCamelCase=1 , _lowerCamelCase=3_0_7_2 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=5_1_4 , _lowerCamelCase=1 , _lowerCamelCase=1e-05 , _lowerCamelCase=1 , _lowerCamelCase=0 , _lowerCamelCase=2 , _lowerCamelCase="absolute" , _lowerCamelCase=True , **_lowerCamelCase , ):
super().__init__(**_lowerCamelCase )
UpperCamelCase_: Tuple = vocab_size
UpperCamelCase_: str = hidden_size
UpperCamelCase_: List[str] = num_hidden_layers
UpperCamelCase_: Tuple = num_attention_heads
UpperCamelCase_: Optional[Any] = hidden_act
UpperCamelCase_: int = initializer_factor
UpperCamelCase_: Optional[int] = intermediate_size
UpperCamelCase_: Optional[Any] = hidden_dropout_prob
UpperCamelCase_: Dict = attention_probs_dropout_prob
UpperCamelCase_: int = max_position_embeddings
UpperCamelCase_: int = type_vocab_size
UpperCamelCase_: Optional[Any] = layer_norm_eps
UpperCamelCase_: Any = position_embedding_type
UpperCamelCase_: int = use_cache
UpperCamelCase_: Optional[Any] = pad_token_id
UpperCamelCase_: Optional[Any] = bos_token_id
UpperCamelCase_: List[str] = eos_token_id
@classmethod
def _a ( cls , _lowerCamelCase , **_lowerCamelCase ):
UpperCamelCase_ ,UpperCamelCase_: int = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
if config_dict.get('model_type' ) == "bridgetower":
UpperCamelCase_: Dict = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Optional[Any] ='''bridgetower'''
def __init__( self , _lowerCamelCase=True , _lowerCamelCase="gelu" , _lowerCamelCase=7_6_8 , _lowerCamelCase=1 , _lowerCamelCase=1e-05 , _lowerCamelCase=False , _lowerCamelCase="add" , _lowerCamelCase=1_2 , _lowerCamelCase=6 , _lowerCamelCase=False , _lowerCamelCase=False , _lowerCamelCase=None , _lowerCamelCase=None , **_lowerCamelCase , ):
# TODO: remove this once the Hub files are updated.
UpperCamelCase_: Optional[int] = kwargs.pop('text_config_dict' , _lowerCamelCase )
UpperCamelCase_: Union[str, Any] = kwargs.pop('vision_config_dict' , _lowerCamelCase )
super().__init__(**_lowerCamelCase )
UpperCamelCase_: List[Any] = share_cross_modal_transformer_layers
UpperCamelCase_: List[str] = hidden_act
UpperCamelCase_: Optional[Any] = hidden_size
UpperCamelCase_: Optional[Any] = initializer_factor
UpperCamelCase_: Any = layer_norm_eps
UpperCamelCase_: Dict = share_link_tower_layers
UpperCamelCase_: int = link_tower_type
UpperCamelCase_: Union[str, Any] = num_attention_heads
UpperCamelCase_: Optional[int] = num_hidden_layers
UpperCamelCase_: Tuple = tie_word_embeddings
UpperCamelCase_: Optional[int] = init_layernorm_from_vision_encoder
if text_config is None:
UpperCamelCase_: Any = {}
logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.' )
if vision_config is None:
UpperCamelCase_: Any = {}
logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.' )
UpperCamelCase_: Optional[Any] = BridgeTowerTextConfig(**_lowerCamelCase )
UpperCamelCase_: Optional[Any] = BridgeTowerVisionConfig(**_lowerCamelCase )
@classmethod
def _a ( cls , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_lowerCamelCase )
def _a ( self ):
UpperCamelCase_: Tuple = copy.deepcopy(self.__dict__ )
UpperCamelCase_: Optional[Any] = self.text_config.to_dict()
UpperCamelCase_: Union[str, Any] = self.vision_config.to_dict()
UpperCamelCase_: Tuple = self.__class__.model_type
return output | 292 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def snake_case (UpperCAmelCase__ ) -> Optional[int]: # picklable for multiprocessing
return x.sum()
def snake_case (UpperCAmelCase__ ) -> Any: # picklable for multiprocessing
return i + 1
@dataclass
class _lowerCAmelCase:
"""simple docstring"""
a : int
a : str
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
def _a ( self ):
UpperCamelCase_: Optional[Any] = {}
UpperCamelCase_: List[str] = []
UpperCamelCase_: Any = 1
UpperCamelCase_: Optional[int] = [1, 2]
UpperCamelCase_: List[str] = {'a': 1, 'b': 2}
UpperCamelCase_: Tuple = {'a': [1, 2], 'b': [3, 4]}
UpperCamelCase_: Optional[int] = {'a': {'1': 1}, 'b': 2}
UpperCamelCase_: Optional[Any] = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
UpperCamelCase_: Tuple = {}
UpperCamelCase_: str = []
UpperCamelCase_: List[Any] = 2
UpperCamelCase_: List[Any] = [2, 3]
UpperCamelCase_: Optional[Any] = {'a': 2, 'b': 3}
UpperCamelCase_: List[str] = {'a': [2, 3], 'b': [4, 5]}
UpperCamelCase_: Any = {'a': {'1': 2}, 'b': 3}
UpperCamelCase_: List[str] = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
UpperCamelCase_: Optional[int] = 2
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
UpperCamelCase_: Tuple = {'a': np.eye(2 ), 'b': np.zeros(3 ), 'c': np.ones(2 )}
UpperCamelCase_: Tuple = {'a': 2, 'b': 0, 'c': 2}
UpperCamelCase_: str = {
'a': np.eye(2 ).astype(_lowerCamelCase ),
'b': np.zeros(3 ).astype(_lowerCamelCase ),
'c': np.ones(2 ).astype(_lowerCamelCase ),
}
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , map_numpy=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(_lowerCamelCase , _lowerCamelCase , map_numpy=_lowerCamelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(_lowerCamelCase , _lowerCamelCase , map_numpy=_lowerCamelCase , num_proc=_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(_lowerCamelCase , _lowerCamelCase , map_numpy=_lowerCamelCase , num_proc=_lowerCamelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(_lowerCamelCase ): # can't pickle a local lambda
map_nested(lambda _lowerCamelCase : x + 1 , _lowerCamelCase , num_proc=_lowerCamelCase )
def _a ( self ):
UpperCamelCase_: Optional[Any] = {'a': 1, 'b': 2}
UpperCamelCase_: Dict = {'a': 3, 'b': 4}
UpperCamelCase_: Optional[int] = {'a': 5, 'b': 6}
UpperCamelCase_: int = sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) ) , _lowerCamelCase )
def _a ( self ):
class _lowerCAmelCase:
"""simple docstring"""
a : str ='''bar'''
UpperCamelCase_: int = Foo()
self.assertEqual(foo.my_attr , 'bar' )
with temporary_assignment(_lowerCamelCase , 'my_attr' , 'BAR' ):
self.assertEqual(foo.my_attr , 'BAR' )
self.assertEqual(foo.my_attr , 'bar' )
@pytest.mark.parametrize(
'iterable_length, num_proc, expected_num_proc' , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(1_6, 1_6, 1_6),
(1_6, 1_7, 1_6),
(1_7, 1_6, 1_6),
] , )
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Dict:
with patch('datasets.utils.py_utils._single_map_nested' ) as mock_single_map_nested, patch(
'datasets.parallel.parallel.Pool' ) as mock_multiprocessing_pool:
UpperCamelCase_: Any = {F'''{i}''': i for i in range(UpperCAmelCase__ )}
UpperCamelCase_: int = map_nested(lambda UpperCAmelCase__ : x + 1_0 , UpperCAmelCase__ , num_proc=UpperCAmelCase__ , parallel_min_length=1_6 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
@require_tf
def _a ( self ):
import tensorflow as tf
from tensorflow.keras import layers
UpperCamelCase_: Dict = layers.Dense(2 )
def gen_random_output():
UpperCamelCase_: Optional[Any] = tf.random.uniform((1, 3) )
return model(_lowerCamelCase ).numpy()
with temp_seed(4_2 , set_tensorflow=_lowerCamelCase ):
UpperCamelCase_: int = gen_random_output()
with temp_seed(4_2 , set_tensorflow=_lowerCamelCase ):
UpperCamelCase_: List[str] = gen_random_output()
UpperCamelCase_: str = gen_random_output()
np.testing.assert_equal(_lowerCamelCase , _lowerCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def _a ( self ):
import torch
def gen_random_output():
UpperCamelCase_: Any = torch.nn.Linear(3 , 2 )
UpperCamelCase_: Optional[Any] = torch.rand(1 , 3 )
return model(_lowerCamelCase ).detach().numpy()
with temp_seed(4_2 , set_pytorch=_lowerCamelCase ):
UpperCamelCase_: Dict = gen_random_output()
with temp_seed(4_2 , set_pytorch=_lowerCamelCase ):
UpperCamelCase_: str = gen_random_output()
UpperCamelCase_: str = gen_random_output()
np.testing.assert_equal(_lowerCamelCase , _lowerCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def _a ( self ):
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(4_2 ):
UpperCamelCase_: Optional[Any] = gen_random_output()
with temp_seed(4_2 ):
UpperCamelCase_: Tuple = gen_random_output()
UpperCamelCase_: Optional[int] = gen_random_output()
np.testing.assert_equal(_lowerCamelCase , _lowerCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize('input_data' , [{}] )
def snake_case (UpperCAmelCase__ ) -> Dict:
UpperCamelCase_: str = NestedDataStructure(UpperCAmelCase__ ).data
assert output_data == input_data
@pytest.mark.parametrize(
'data, expected_output' , [
({}, []),
([], []),
('foo', ['foo']),
(['foo', 'bar'], ['foo', 'bar']),
([['foo', 'bar']], ['foo', 'bar']),
([[['foo'], ['bar']]], ['foo', 'bar']),
([[['foo'], 'bar']], ['foo', 'bar']),
({'a': 1, 'b': 2}, [1, 2]),
({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
({'a': {'1': 1}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
] , )
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> Dict:
UpperCamelCase_: Optional[Any] = NestedDataStructure(UpperCAmelCase__ ).flatten()
assert output == expected_output
def snake_case () -> Optional[int]:
UpperCamelCase_: List[Any] = A(x=1 , y='foobar' )
UpperCamelCase_: Optional[int] = {'x': 1, 'y': 'foobar'}
assert asdict(UpperCAmelCase__ ) == expected_output
UpperCamelCase_: List[str] = {'a': {'b': A(x=1_0 , y='foo' )}, 'c': [A(x=2_0 , y='bar' )]}
UpperCamelCase_: Tuple = {'a': {'b': {'x': 1_0, 'y': 'foo'}}, 'c': [{'x': 2_0, 'y': 'bar'}]}
assert asdict(UpperCAmelCase__ ) == expected_output
with pytest.raises(UpperCAmelCase__ ):
asdict([1, A(x=1_0 , y='foo' )] )
def snake_case (UpperCAmelCase__ ) -> Optional[Any]:
return text.split()
def snake_case (UpperCAmelCase__ ) -> str:
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def snake_case () -> Union[str, Any]:
with Pool(2 ) as pool:
UpperCamelCase_: Optional[Any] = list(iflatmap_unordered(UpperCAmelCase__ , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 1_0 ) )
assert out.count('hello' ) == 1_0
assert out.count('there' ) == 1_0
assert len(UpperCAmelCase__ ) == 2_0
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
UpperCamelCase_: Optional[int] = list(iflatmap_unordered(UpperCAmelCase__ , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 1_0 ) )
assert out.count('hello' ) == 1_0
assert out.count('there' ) == 1_0
assert len(UpperCAmelCase__ ) == 2_0
# check that we get items as fast as possible
with Pool(2 ) as pool:
UpperCamelCase_: Any = []
for yield_time, content in iflatmap_unordered(
UpperCAmelCase__ , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{'content': 'a'}, {'content': 'b'}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(UpperCAmelCase__ )
assert out.count('a' ) == 2
assert out.count('b' ) == 2
assert len(UpperCAmelCase__ ) == 4 | 292 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
UpperCAmelCase__ = [int(a_ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(a_ ) == 4 and all(0 <= int(a_ ) <= 254 for octet in octets )
if __name__ == "__main__":
UpperCAmelCase_ = input().strip()
UpperCAmelCase_ = '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
print(f"{ip} is a {valid_or_invalid} IP v4 address.")
| 346 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.environ['GITHUB_TOKEN'] )
__UpperCamelCase : Union[str, Any] =g.get_repo('huggingface/accelerate' )
__UpperCamelCase : Tuple =repo.get_issues(state='open' )
for issue in open_issues:
__UpperCamelCase : List[Any] =sorted([comment for comment in issue.get_comments()] ,key=lambda a_ : i.created_at ,reverse=a_ )
__UpperCamelCase : str =comments[0] if len(a_ ) > 0 else None
__UpperCamelCase : Any =dt.utcnow()
__UpperCamelCase : List[str] =(current_time - issue.updated_at).days
__UpperCamelCase : Union[str, Any] =(current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 71 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase_ ( lowerCamelCase__ , unittest.TestCase ):
'''simple docstring'''
__snake_case = KandinskyVaaPriorPipeline
__snake_case = ["prompt"]
__snake_case = ["prompt", "negative_prompt"]
__snake_case = [
"num_images_per_prompt",
"generator",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
__snake_case = False
@property
def UpperCamelCase__ ( self ):
return 32
@property
def UpperCamelCase__ ( self ):
return 32
@property
def UpperCamelCase__ ( self ):
return self.time_input_dim
@property
def UpperCamelCase__ ( self ):
return self.time_input_dim * 4
@property
def UpperCamelCase__ ( self ):
return 1_00
@property
def UpperCamelCase__ ( self ):
snake_case_ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCamelCase__ ( self ):
torch.manual_seed(0 )
snake_case_ = 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=10_00 , )
return CLIPTextModelWithProjection(_UpperCAmelCase )
@property
def UpperCamelCase__ ( self ):
torch.manual_seed(0 )
snake_case_ = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 12,
'''embedding_dim''': self.text_embedder_hidden_size,
'''num_layers''': 1,
}
snake_case_ = PriorTransformer(**_UpperCAmelCase )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
snake_case_ = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def UpperCamelCase__ ( self ):
torch.manual_seed(0 )
snake_case_ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=2_24 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
snake_case_ = CLIPVisionModelWithProjection(_UpperCAmelCase )
return model
@property
def UpperCamelCase__ ( self ):
snake_case_ = CLIPImageProcessor(
crop_size=2_24 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=2_24 , )
return image_processor
def UpperCamelCase__ ( self ):
snake_case_ = self.dummy_prior
snake_case_ = self.dummy_image_encoder
snake_case_ = self.dummy_text_encoder
snake_case_ = self.dummy_tokenizer
snake_case_ = self.dummy_image_processor
snake_case_ = UnCLIPScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=10_00 , clip_sample=_UpperCAmelCase , clip_sample_range=10.0 , )
snake_case_ = {
'''prior''': prior,
'''image_encoder''': image_encoder,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''scheduler''': scheduler,
'''image_processor''': image_processor,
}
return components
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase=0 ):
if str(_UpperCAmelCase ).startswith('''mps''' ):
snake_case_ = torch.manual_seed(_UpperCAmelCase )
else:
snake_case_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
snake_case_ = {
'''prompt''': '''horse''',
'''generator''': generator,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase__ ( self ):
snake_case_ = '''cpu'''
snake_case_ = self.get_dummy_components()
snake_case_ = self.pipeline_class(**_UpperCAmelCase )
snake_case_ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
snake_case_ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
snake_case_ = output.image_embeds
snake_case_ = pipe(
**self.get_dummy_inputs(_UpperCAmelCase ) , return_dict=_UpperCAmelCase , )[0]
snake_case_ = image[0, -10:]
snake_case_ = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
snake_case_ = np.array(
[-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def UpperCamelCase__ ( self ):
snake_case_ = torch_device == '''cpu'''
snake_case_ = True
snake_case_ = False
self._test_inference_batch_single_identical(
test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , test_mean_pixel_difference=_UpperCAmelCase , )
@skip_mps
def UpperCamelCase__ ( self ):
snake_case_ = torch_device == '''cpu'''
snake_case_ = False
self._test_attention_slicing_forward_pass(
test_max_difference=_UpperCAmelCase , test_mean_pixel_difference=_UpperCAmelCase , ) | 267 |
import numpy as np
import datasets
UpperCAmelCase = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""
UpperCAmelCase = """\
@article{de2000mahalanobis,
title={The mahalanobis distance},
author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
journal={Chemometrics and intelligent laboratory systems},
volume={50},
number={1},
pages={1--18},
year={2000},
publisher={Elsevier}
}
"""
UpperCAmelCase = """
Args:
X: List of datapoints to be compared with the `reference_distribution`.
reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:
>>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
>>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
>>> print(results)
{'mahalanobis': array([0.5])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''X''': datasets.Sequence(datasets.Value('''float''' , id='''sequence''' ) , id='''X''' ),
} ) , )
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase ):
# convert to numpy arrays
snake_case_ = np.array(_UpperCAmelCase )
snake_case_ = np.array(_UpperCAmelCase )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError('''Expected `X` to be a 2D vector''' )
if len(reference_distribution.shape ) != 2:
raise ValueError('''Expected `reference_distribution` to be a 2D vector''' )
if reference_distribution.shape[0] < 2:
raise ValueError(
'''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''' )
# Get mahalanobis distance for each prediction
snake_case_ = X - np.mean(_UpperCAmelCase )
snake_case_ = np.cov(reference_distribution.T )
try:
snake_case_ = np.linalg.inv(_UpperCAmelCase )
except np.linalg.LinAlgError:
snake_case_ = np.linalg.pinv(_UpperCAmelCase )
snake_case_ = np.dot(_UpperCAmelCase , _UpperCAmelCase )
snake_case_ = np.dot(_UpperCAmelCase , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist} | 267 | 1 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 8 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return [ord(SCREAMING_SNAKE_CASE__ ) - 96 for elem in plain]
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return "".join(chr(elem + 96 ) for elem in encoded )
def __SCREAMING_SNAKE_CASE ():
snake_case_ = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''' , SCREAMING_SNAKE_CASE__ )
print('''Decoded:''' , decode(SCREAMING_SNAKE_CASE__ ) )
if __name__ == "__main__":
main() | 8 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class __UpperCAmelCase :
def __init__( self : Optional[int], __A : List[Any], __A : List[str]=3, __A : List[Any]=7, __A : str=True, __A : Optional[int]=True, __A : List[Any]=False, __A : str=True, __A : Any=9_9, __A : int=3_2, __A : List[Any]=5, __A : str=4, __A : Optional[int]=3_7, __A : int="gelu", __A : List[str]=0.1, __A : str=0.1, __A : int=5_1_2, __A : int=1_6, __A : Optional[int]=2, __A : Optional[Any]=0.0_2, __A : Any=3, __A : Optional[int]=4, __A : Union[str, Any]=None, ):
UpperCAmelCase : List[str] = parent
UpperCAmelCase : List[Any] = batch_size
UpperCAmelCase : Tuple = seq_length
UpperCAmelCase : Optional[int] = is_training
UpperCAmelCase : List[Any] = use_input_mask
UpperCAmelCase : int = use_token_type_ids
UpperCAmelCase : Tuple = use_labels
UpperCAmelCase : Dict = vocab_size
UpperCAmelCase : List[str] = hidden_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : Tuple = num_attention_heads
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : Dict = hidden_act
UpperCAmelCase : Any = hidden_dropout_prob
UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
UpperCAmelCase : Union[str, Any] = max_position_embeddings
UpperCAmelCase : List[Any] = type_vocab_size
UpperCAmelCase : str = type_sequence_label_size
UpperCAmelCase : List[Any] = initializer_range
UpperCAmelCase : Tuple = num_labels
UpperCAmelCase : List[Any] = num_choices
UpperCAmelCase : List[Any] = scope
def __magic_name__ ( self : Dict ):
UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase : Tuple = None
if self.use_input_mask:
UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : int = None
UpperCAmelCase : Optional[Any] = None
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Optional[Any] = None
if self.use_labels:
UpperCAmelCase : List[str] = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size], self.num_choices )
UpperCAmelCase : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ ( self : Optional[int] ):
return FalconConfig(
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=__A, initializer_range=self.initializer_range, pad_token_id=1, new_decoder_architecture=__A, )
def __magic_name__ ( self : List[str], __A : Union[str, Any], __A : Optional[Any], __A : List[Any], __A : Any, __A : Dict, __A : List[str], __A : Dict ):
UpperCAmelCase : str = FalconModel(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : str = model(__A, attention_mask=__A )
UpperCAmelCase : Any = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : int, __A : Dict, __A : Dict, __A : List[Any], __A : int, __A : str, __A : str, __A : Any, __A : Optional[Any], __A : Union[str, Any], ):
UpperCAmelCase : Dict = True
UpperCAmelCase : List[str] = FalconModel(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Union[str, Any] = model(
__A, attention_mask=__A, encoder_hidden_states=__A, encoder_attention_mask=__A, )
UpperCAmelCase : int = model(
__A, attention_mask=__A, encoder_hidden_states=__A, )
UpperCAmelCase : List[Any] = model(__A, attention_mask=__A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : List[Any], __A : List[Any], __A : List[Any], __A : Optional[int], __A : Union[str, Any], __A : Optional[Any], __A : Optional[Any], __A : List[Any], __A : Any, __A : Union[str, Any], ):
UpperCAmelCase : Any = FalconForCausalLM(config=__A )
model.to(__A )
model.eval()
UpperCAmelCase : str = model(__A, attention_mask=__A, labels=__A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : List[str], __A : Optional[Any], __A : List[Any], __A : List[str], __A : str, __A : Any, __A : List[Any], __A : Optional[int], __A : Tuple, __A : Tuple, ):
UpperCAmelCase : List[Any] = True
UpperCAmelCase : Optional[Any] = True
UpperCAmelCase : Dict = FalconForCausalLM(config=__A )
model.to(__A )
model.eval()
# first forward pass
UpperCAmelCase : Union[str, Any] = model(
__A, attention_mask=__A, encoder_hidden_states=__A, encoder_attention_mask=__A, use_cache=__A, )
UpperCAmelCase : Union[str, Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase : Tuple = ids_tensor((self.batch_size, 3), config.vocab_size )
UpperCAmelCase : Dict = ids_tensor((self.batch_size, 3), vocab_size=2 )
# append to next input_ids and
UpperCAmelCase : Tuple = torch.cat([input_ids, next_tokens], dim=-1 )
UpperCAmelCase : List[Any] = torch.cat([input_mask, next_mask], dim=-1 )
UpperCAmelCase : Tuple = model(
__A, attention_mask=__A, encoder_hidden_states=__A, encoder_attention_mask=__A, output_hidden_states=__A, )['''hidden_states'''][0]
UpperCAmelCase : Any = model(
__A, attention_mask=__A, encoder_hidden_states=__A, encoder_attention_mask=__A, past_key_values=__A, output_hidden_states=__A, )['''hidden_states'''][0]
# select random slice
UpperCAmelCase : List[str] = ids_tensor((1,), output_from_past.shape[-1] ).item()
UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase : int = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__A, __A, atol=1E-3 ) )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Dict = self.prepare_config_and_inputs()
(
UpperCAmelCase
) : Dict = config_and_inputs
UpperCAmelCase : Tuple = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase = (FalconForCausalLM,) if is_torch_available() else ()
UpperCamelCase = (
{
"""feature-extraction""": FalconModel,
"""text-classification""": FalconForSequenceClassification,
"""text-generation""": FalconForCausalLM,
"""question-answering""": FalconForQuestionAnswering,
"""token-classification""": FalconForTokenClassification,
"""zero-shot""": FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Optional[int] = FalconModelTester(self )
UpperCAmelCase : Any = ConfigTester(self, config_class=__A, hidden_size=3_7 )
def __magic_name__ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
UpperCAmelCase : int = alibi
self.model_tester.create_and_check_model(__A, *__A )
def __magic_name__ ( self : List[str] ):
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : str = 3
UpperCAmelCase : Optional[int] = input_dict['''input_ids''']
UpperCAmelCase : Dict = input_ids.ne(1 ).to(__A )
UpperCAmelCase : List[Any] = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size )
UpperCAmelCase : Union[str, Any] = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : str = model(__A, attention_mask=__A, labels=__A )
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels) )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Any = 3
UpperCAmelCase : Dict = '''single_label_classification'''
UpperCAmelCase : Tuple = input_dict['''input_ids''']
UpperCAmelCase : Any = input_ids.ne(1 ).to(__A )
UpperCAmelCase : str = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size )
UpperCAmelCase : str = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Dict = model(__A, attention_mask=__A, labels=__A )
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels) )
def __magic_name__ ( self : Any ):
UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : Optional[int] = input_dict['''input_ids''']
UpperCAmelCase : Union[str, Any] = FalconForCausalLM(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Optional[int] = model(__A, use_cache=__A )
UpperCAmelCase : Union[str, Any] = input_ids.shape[0]
UpperCAmelCase : str = model._convert_to_rw_cache(result.past_key_values )
UpperCAmelCase : Optional[Any] = model._convert_cache_to_standard_format(__A, __A )
for layer in range(len(__A ) ):
for tensor_idx in range(2 ):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3 )
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4 )
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx] ) )
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : List[str] = 3
UpperCAmelCase : Optional[Any] = '''multi_label_classification'''
UpperCAmelCase : Optional[Any] = input_dict['''input_ids''']
UpperCAmelCase : Optional[Any] = input_ids.ne(1 ).to(__A )
UpperCAmelCase : Optional[Any] = ids_tensor(
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCAmelCase : str = FalconForSequenceClassification(__A )
model.to(__A )
model.eval()
UpperCAmelCase : Tuple = model(__A, attention_mask=__A, labels=__A )
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels) )
def __magic_name__ ( self : Optional[Any] ):
# Falcon can have different numbers of KV-heads than the number of query heads, so we need
# to override this test to use the right head counts.
for model_class in self.all_generative_model_classes:
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(__A, '''use_cache''' ):
return
UpperCAmelCase : List[Any] = model_class(__A ).to(__A )
if "use_cache" not in inputs:
UpperCAmelCase : Optional[Any] = True
UpperCAmelCase : Optional[int] = model(**__A )
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
UpperCAmelCase : List[str] = (
getattr(__A, '''decoder_layers''', __A )
or getattr(__A, '''num_decoder_layers''', __A )
or config.num_hidden_layers
)
UpperCAmelCase : Optional[int] = getattr(__A, '''num_kv_heads''', config.num_attention_heads )
UpperCAmelCase : List[Any] = getattr(__A, '''d_model''', config.hidden_size )
UpperCAmelCase : int = embed_dim // num_attention_heads
UpperCAmelCase : int = outputs['''past_key_values''']
self.assertEqual(len(__A ), __A )
UpperCAmelCase : str = inputs['''input_ids'''].shape
for i in range(__A ):
if config.new_decoder_architecture:
UpperCAmelCase : Dict = config.num_attention_heads
elif config.multi_query:
UpperCAmelCase : Tuple = 1
self.assertEqual(len(past_kv[0] ), 2 ) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
self.assertEqual(
past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
@slow
def __magic_name__ ( self : Union[str, Any] ):
UpperCAmelCase : Tuple = AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b''' )
UpperCAmelCase : Tuple = FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b''' )
model.eval()
model.to(__A )
UpperCAmelCase : Tuple = tokenizer('''My favorite food is''', return_tensors='''pt''' ).to(__A )
UpperCAmelCase : int = (
'''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
)
UpperCAmelCase : int = model.generate(**__A, do_sample=__A, max_new_tokens=1_9 )
UpperCAmelCase : int = tokenizer.batch_decode(__A )[0]
self.assertEqual(__A, __A )
@slow
def __magic_name__ ( self : List[Any] ):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
UpperCAmelCase : int = AutoTokenizer.from_pretrained(__A )
UpperCAmelCase : List[str] = FalconForCausalLM.from_pretrained(__A )
model.eval()
model.to(__A )
UpperCAmelCase : Optional[int] = tokenizer('''My favorite food is''', return_tensors='''pt''' ).to(__A )
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**__A, do_sample=__A, max_new_tokens=4 )
model.generate(**__A, do_sample=__A, max_new_tokens=4 )
model.generate(**__A, num_beams=2, max_new_tokens=4 )
@slow
def __magic_name__ ( self : Union[str, Any] ):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(__A )
UpperCAmelCase : Union[str, Any] = FalconForCausalLM.from_pretrained(__A )
model.eval()
model.to(device=__A )
UpperCAmelCase : Dict = tokenizer('''My favorite food is''', return_tensors='''pt''' ).to(__A )
# Test results are the same with and without cache
UpperCAmelCase : int = model.generate(**__A, do_sample=__A, max_new_tokens=2_0, use_cache=__A )
UpperCAmelCase : Dict = model.generate(**__A, do_sample=__A, max_new_tokens=2_0, use_cache=__A )
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0 )
| 354 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCamelCase : Tuple = 3_0_0 # TEMPERATURE (unit = K)
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float , ) -> float:
if donor_conc <= 0:
raise ValueError('''Donor concentration should be positive''' )
elif acceptor_conc <= 0:
raise ValueError('''Acceptor concentration should be positive''' )
elif intrinsic_conc <= 0:
raise ValueError('''Intrinsic concentration should be positive''' )
elif donor_conc <= intrinsic_conc:
raise ValueError(
'''Donor concentration should be greater than intrinsic concentration''' )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
'''Acceptor concentration should be greater than intrinsic concentration''' )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99 | 0 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 21 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater than 0' )
_lowercase : Tuple = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowercase ( a ):
def __init__( self : Optional[int] , _UpperCamelCase : NestedDataStructureLike[PathLike] , _UpperCamelCase : Optional[NamedSplit] = None , _UpperCamelCase : Optional[Features] = None , _UpperCamelCase : str = None , _UpperCamelCase : bool = False , _UpperCamelCase : bool = False , _UpperCamelCase : Optional[str] = None , _UpperCamelCase : Optional[int] = None , **_UpperCamelCase : Optional[int] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
_UpperCamelCase , split=_UpperCamelCase , features=_UpperCamelCase , cache_dir=_UpperCamelCase , keep_in_memory=_UpperCamelCase , streaming=_UpperCamelCase , num_proc=_UpperCamelCase , **_UpperCamelCase , )
SCREAMING_SNAKE_CASE = field
SCREAMING_SNAKE_CASE = path_or_paths if isinstance(_UpperCamelCase , _UpperCamelCase ) else {self.split: path_or_paths}
SCREAMING_SNAKE_CASE = Json(
cache_dir=_UpperCamelCase , data_files=_UpperCamelCase , features=_UpperCamelCase , field=_UpperCamelCase , **_UpperCamelCase , )
def __snake_case( self : List[Any] ) -> Any:
'''simple docstring'''
if self.streaming:
SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=_UpperCamelCase , download_mode=_UpperCamelCase , verification_mode=_UpperCamelCase , base_path=_UpperCamelCase , num_proc=self.num_proc , )
SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split=self.split , verification_mode=_UpperCamelCase , in_memory=self.keep_in_memory )
return dataset
class lowercase :
def __init__( self : Any , _UpperCamelCase : Dataset , _UpperCamelCase : Union[PathLike, BinaryIO] , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : Optional[int] = None , **_UpperCamelCase : str , ) -> Union[str, Any]:
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(F"num_proc {num_proc} must be an integer > 0." )
SCREAMING_SNAKE_CASE = dataset
SCREAMING_SNAKE_CASE = path_or_buf
SCREAMING_SNAKE_CASE = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE = num_proc
SCREAMING_SNAKE_CASE = "utf-8"
SCREAMING_SNAKE_CASE = to_json_kwargs
def __snake_case( self : Optional[int] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("path_or_buf" , _UpperCamelCase )
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("orient" , "records" )
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("lines" , True if orient == "records" else False )
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("index" , False if orient in ["split", "table"] else True )
SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("compression" , _UpperCamelCase )
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(F"`datasets` currently does not support {compression} compression" )
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with fsspec.open(self.path_or_buf , "wb" , compression=_UpperCamelCase ) as buffer:
SCREAMING_SNAKE_CASE = self._write(file_obj=_UpperCamelCase , orient=_UpperCamelCase , lines=_UpperCamelCase , index=_UpperCamelCase , **self.to_json_kwargs )
else:
if compression:
raise NotImplementedError(
F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
" was passed. Please provide a local path instead." )
SCREAMING_SNAKE_CASE = self._write(
file_obj=self.path_or_buf , orient=_UpperCamelCase , lines=_UpperCamelCase , index=_UpperCamelCase , **self.to_json_kwargs )
return written
def __snake_case( self : str , _UpperCamelCase : str ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = args
SCREAMING_SNAKE_CASE = query_table(
table=self.dataset.data , key=slice(_UpperCamelCase , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE = batch.to_pandas().to_json(
path_or_buf=_UpperCamelCase , orient=_UpperCamelCase , lines=_UpperCamelCase , index=_UpperCamelCase , **_UpperCamelCase )
if not json_str.endswith("\n" ):
json_str += "\n"
return json_str.encode(self.encoding )
def __snake_case( self : List[str] , _UpperCamelCase : BinaryIO , _UpperCamelCase : Tuple , _UpperCamelCase : Optional[Any] , _UpperCamelCase : List[Any] , **_UpperCamelCase : Optional[int] , ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating json from Arrow format" , ):
SCREAMING_SNAKE_CASE = self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(_UpperCamelCase )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , _UpperCamelCase , _UpperCamelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating json from Arrow format" , ):
written += file_obj.write(_UpperCamelCase )
return written
| 206 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class lowercase ( a ):
lowercase__ : Tuple = """unispeech-sat"""
def __init__( self : str , _UpperCamelCase : Tuple=32 , _UpperCamelCase : Union[str, Any]=768 , _UpperCamelCase : Tuple=12 , _UpperCamelCase : List[str]=12 , _UpperCamelCase : Tuple=3_072 , _UpperCamelCase : List[str]="gelu" , _UpperCamelCase : Tuple=0.1 , _UpperCamelCase : Any=0.1 , _UpperCamelCase : Union[str, Any]=0.1 , _UpperCamelCase : Dict=0.0 , _UpperCamelCase : Tuple=0.0 , _UpperCamelCase : Tuple=0.1 , _UpperCamelCase : Optional[Any]=0.1 , _UpperCamelCase : Tuple=0.0_2 , _UpperCamelCase : Optional[int]=1e-5 , _UpperCamelCase : Union[str, Any]="group" , _UpperCamelCase : Optional[int]="gelu" , _UpperCamelCase : Tuple=(512, 512, 512, 512, 512, 512, 512) , _UpperCamelCase : List[str]=(5, 2, 2, 2, 2, 2, 2) , _UpperCamelCase : Optional[int]=(10, 3, 3, 3, 3, 2, 2) , _UpperCamelCase : Optional[int]=False , _UpperCamelCase : Dict=128 , _UpperCamelCase : Optional[int]=16 , _UpperCamelCase : Tuple=False , _UpperCamelCase : Union[str, Any]=True , _UpperCamelCase : Optional[Any]=0.0_5 , _UpperCamelCase : Union[str, Any]=10 , _UpperCamelCase : Union[str, Any]=2 , _UpperCamelCase : str=0.0 , _UpperCamelCase : List[Any]=10 , _UpperCamelCase : Optional[int]=0 , _UpperCamelCase : Any=320 , _UpperCamelCase : List[Any]=2 , _UpperCamelCase : str=0.1 , _UpperCamelCase : str=100 , _UpperCamelCase : int=256 , _UpperCamelCase : Optional[Any]=256 , _UpperCamelCase : List[Any]=0.1 , _UpperCamelCase : str="mean" , _UpperCamelCase : int=False , _UpperCamelCase : Optional[Any]=False , _UpperCamelCase : Any=256 , _UpperCamelCase : str=(512, 512, 512, 512, 1_500) , _UpperCamelCase : List[Any]=(5, 3, 3, 1, 1) , _UpperCamelCase : Union[str, Any]=(1, 2, 3, 1, 1) , _UpperCamelCase : Any=512 , _UpperCamelCase : str=0 , _UpperCamelCase : int=1 , _UpperCamelCase : Any=2 , _UpperCamelCase : Optional[Any]=504 , **_UpperCamelCase : str , ) -> int:
'''simple docstring'''
super().__init__(**_UpperCamelCase , pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = feat_extract_norm
SCREAMING_SNAKE_CASE = feat_extract_activation
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = conv_bias
SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE = len(self.conv_dim )
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = feat_proj_dropout
SCREAMING_SNAKE_CASE = final_dropout
SCREAMING_SNAKE_CASE = layerdrop
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = num_clusters
SCREAMING_SNAKE_CASE = do_stable_layer_norm
SCREAMING_SNAKE_CASE = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
F" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"
F" `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE = apply_spec_augment
SCREAMING_SNAKE_CASE = mask_time_prob
SCREAMING_SNAKE_CASE = mask_time_length
SCREAMING_SNAKE_CASE = mask_time_min_masks
SCREAMING_SNAKE_CASE = mask_feature_prob
SCREAMING_SNAKE_CASE = mask_feature_length
SCREAMING_SNAKE_CASE = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
SCREAMING_SNAKE_CASE = num_codevectors_per_group
SCREAMING_SNAKE_CASE = num_codevector_groups
SCREAMING_SNAKE_CASE = contrastive_logits_temperature
SCREAMING_SNAKE_CASE = feat_quantizer_dropout
SCREAMING_SNAKE_CASE = num_negatives
SCREAMING_SNAKE_CASE = codevector_dim
SCREAMING_SNAKE_CASE = proj_codevector_dim
SCREAMING_SNAKE_CASE = diversity_loss_weight
# ctc loss
SCREAMING_SNAKE_CASE = ctc_loss_reduction
SCREAMING_SNAKE_CASE = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = xvector_output_dim
@property
def __snake_case( self : Tuple ) -> str:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 206 | 1 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def A__ ( UpperCamelCase ): # picklable for multiprocessing
return x.sum()
def A__ ( UpperCamelCase ): # picklable for multiprocessing
return i + 1
@dataclass
class _UpperCAmelCase :
UpperCamelCase = 42
UpperCamelCase = 42
class _UpperCAmelCase ( lowercase_ ):
def lowerCamelCase ( self :Any ):
A = {}
A = []
A = 1
A = [1, 2]
A = {"a": 1, "b": 2}
A = {"a": [1, 2], "b": [3, 4]}
A = {"a": {"1": 1}, "b": 2}
A = {"a": 1, "b": 2, "c": 3, "d": 4}
A = {}
A = []
A = 2
A = [2, 3]
A = {"a": 2, "b": 3}
A = {"a": [2, 3], "b": [4, 5]}
A = {"a": {"1": 2}, "b": 3}
A = {"a": 2, "b": 3, "c": 4, "d": 5}
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
A = 2
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
A = {"a": np.eye(2 ), "b": np.zeros(3 ), "c": np.ones(2 )}
A = {"a": 2, "b": 0, "c": 2}
A = {
"a": np.eye(2 ).astype(__UpperCamelCase ),
"b": np.zeros(3 ).astype(__UpperCamelCase ),
"c": np.ones(2 ).astype(__UpperCamelCase ),
}
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , map_numpy=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__UpperCamelCase , __UpperCamelCase , map_numpy=__UpperCamelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(__UpperCamelCase , __UpperCamelCase , map_numpy=__UpperCamelCase , num_proc=__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__UpperCamelCase , __UpperCamelCase , map_numpy=__UpperCamelCase , num_proc=__UpperCamelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(__UpperCamelCase ): # can't pickle a local lambda
map_nested(lambda __UpperCamelCase : x + 1 , __UpperCamelCase , num_proc=__UpperCamelCase )
def lowerCamelCase ( self :Dict ):
A = {"a": 1, "b": 2}
A = {"a": 3, "b": 4}
A = {"a": 5, "b": 6}
A = sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) ) , __UpperCamelCase )
def lowerCamelCase ( self :List[Any] ):
class _UpperCAmelCase :
UpperCamelCase = '''bar'''
A = Foo()
self.assertEqual(foo.my_attr , "bar" )
with temporary_assignment(__UpperCamelCase , "my_attr" , "BAR" ):
self.assertEqual(foo.my_attr , "BAR" )
self.assertEqual(foo.my_attr , "bar" )
@pytest.mark.parametrize(
"iterable_length, num_proc, expected_num_proc" , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
with patch("datasets.utils.py_utils._single_map_nested" ) as mock_single_map_nested, patch(
"datasets.parallel.parallel.Pool" ) as mock_multiprocessing_pool:
A = {F"{i}": i for i in range(UpperCamelCase )}
A = map_nested(lambda UpperCamelCase : x + 10 , UpperCamelCase , num_proc=UpperCamelCase , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class _UpperCAmelCase ( lowercase_ ):
@require_tf
def lowerCamelCase ( self :str ):
import tensorflow as tf
from tensorflow.keras import layers
A = layers.Dense(2 )
def gen_random_output():
A = tf.random.uniform((1, 3) )
return model(__UpperCamelCase ).numpy()
with temp_seed(42 , set_tensorflow=__UpperCamelCase ):
A = gen_random_output()
with temp_seed(42 , set_tensorflow=__UpperCamelCase ):
A = gen_random_output()
A = gen_random_output()
np.testing.assert_equal(__UpperCamelCase , __UpperCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def lowerCamelCase ( self :Optional[int] ):
import torch
def gen_random_output():
A = torch.nn.Linear(3 , 2 )
A = torch.rand(1 , 3 )
return model(__UpperCamelCase ).detach().numpy()
with temp_seed(42 , set_pytorch=__UpperCamelCase ):
A = gen_random_output()
with temp_seed(42 , set_pytorch=__UpperCamelCase ):
A = gen_random_output()
A = gen_random_output()
np.testing.assert_equal(__UpperCamelCase , __UpperCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def lowerCamelCase ( self :Tuple ):
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
A = gen_random_output()
with temp_seed(42 ):
A = gen_random_output()
A = gen_random_output()
np.testing.assert_equal(__UpperCamelCase , __UpperCamelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize("input_data" , [{}] )
def A__ ( UpperCamelCase ):
A = NestedDataStructure(UpperCamelCase ).data
assert output_data == input_data
@pytest.mark.parametrize(
"data, expected_output" , [
({}, []),
([], []),
("foo", ["foo"]),
(["foo", "bar"], ["foo", "bar"]),
([["foo", "bar"]], ["foo", "bar"]),
([[["foo"], ["bar"]]], ["foo", "bar"]),
([[["foo"], "bar"]], ["foo", "bar"]),
({"a": 1, "b": 2}, [1, 2]),
({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
({"a": {"1": 1}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": [2]}, [1, 2]),
] , )
def A__ ( UpperCamelCase , UpperCamelCase ):
A = NestedDataStructure(UpperCamelCase ).flatten()
assert output == expected_output
def A__ ( ):
A = A(x=1 , y="foobar" )
A = {"x": 1, "y": "foobar"}
assert asdict(UpperCamelCase ) == expected_output
A = {"a": {"b": A(x=10 , y="foo" )}, "c": [A(x=20 , y="bar" )]}
A = {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
assert asdict(UpperCamelCase ) == expected_output
with pytest.raises(UpperCamelCase ):
asdict([1, A(x=10 , y="foo" )] )
def A__ ( UpperCamelCase ):
return text.split()
def A__ ( UpperCamelCase ):
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def A__ ( ):
with Pool(2 ) as pool:
A = list(iflatmap_unordered(UpperCamelCase , _split_text , kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(UpperCamelCase ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
A = list(iflatmap_unordered(UpperCamelCase , _split_text , kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(UpperCamelCase ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
A = []
for yield_time, content in iflatmap_unordered(
UpperCamelCase , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{"content": "a"}, {"content": "b"}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(UpperCamelCase )
assert out.count("a" ) == 2
assert out.count("b" ) == 2
assert len(UpperCamelCase ) == 4
| 292 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperCamelCase ):
A = False
return [i for i in range(2 , UpperCamelCase ) if is_prime[i]]
def A__ ( UpperCamelCase = 800_800 , UpperCamelCase = 800_800 ):
A = degree * loga(UpperCamelCase )
A = int(UpperCamelCase )
A = calculate_prime_numbers(UpperCamelCase )
A = 0
A = 0
A = len(UpperCamelCase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 292 | 1 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __a ( __UpperCamelCase ):
def __init__( self , *lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> int:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
lowercase__: List[Any] = eval_examples
lowercase__: List[Any] = post_process_function
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ = None , lowerCAmelCase__=None , lowerCAmelCase__ = None , lowerCAmelCase__ = "eval" , **lowerCAmelCase__ , ) -> Dict[str, float]:
'''simple docstring'''
lowercase__: List[Any] = gen_kwargs.copy()
lowercase__: Union[str, Any] = (
gen_kwargs["max_length"] if gen_kwargs.get('max_length' ) is not None else self.args.generation_max_length
)
lowercase__: Optional[Any] = (
gen_kwargs["num_beams"] if gen_kwargs.get('num_beams' ) is not None else self.args.generation_num_beams
)
lowercase__: Dict = gen_kwargs
lowercase__: Any = self.eval_dataset if eval_dataset is None else eval_dataset
lowercase__: Any = self.get_eval_dataloader(lowerCAmelCase__ )
lowercase__: Tuple = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowercase__: Dict = self.compute_metrics
lowercase__: Optional[int] = None
lowercase__: Tuple = time.time()
lowercase__: Any = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowercase__: Optional[Any] = eval_loop(
lowerCAmelCase__ , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCAmelCase__ , metric_key_prefix=lowerCAmelCase__ , )
finally:
lowercase__: Any = compute_metrics
lowercase__: List[Any] = self.args.eval_batch_size * self.args.world_size
if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
output.metrics.update(
speed_metrics(
lowerCAmelCase__ , lowerCAmelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
lowercase__: Optional[Any] = self.post_process_function(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__: List[Any] = self.compute_metrics(lowerCAmelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'{metric_key_prefix}_' ):
lowercase__: List[str] = metrics.pop(lowerCAmelCase__ )
metrics.update(output.metrics )
else:
lowercase__: List[Any] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(lowerCAmelCase__ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
lowercase__: Union[str, Any] = self.callback_handler.on_evaluate(self.args , self.state , self.control , lowerCAmelCase__ )
return metrics
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__ = "test" , **lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
lowercase__: Dict = gen_kwargs.copy()
lowercase__: Any = self.get_test_dataloader(lowerCAmelCase__ )
# Temporarily disable metric computation, we will do it in the loop here.
lowercase__: List[Any] = self.compute_metrics
lowercase__: Tuple = None
lowercase__: str = time.time()
lowercase__: Any = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowercase__: Optional[int] = eval_loop(
lowerCAmelCase__ , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCAmelCase__ , metric_key_prefix=lowerCAmelCase__ , )
finally:
lowercase__: Optional[Any] = compute_metrics
lowercase__: Optional[Any] = self.args.eval_batch_size * self.args.world_size
if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
output.metrics.update(
speed_metrics(
lowerCAmelCase__ , lowerCAmelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
lowercase__: Dict = self.post_process_function(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , 'predict' )
lowercase__: Union[str, Any] = self.compute_metrics(lowerCAmelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'{metric_key_prefix}_' ):
lowercase__: Tuple = metrics.pop(lowerCAmelCase__ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowerCAmelCase__ )
| 367 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __a ( tf.keras.layers.Layer ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None ) -> int:
'''simple docstring'''
super().__init__()
lowercase__: Union[str, Any] = pad_token_id
lowercase__: List[str] = max_length
lowercase__: int = vocab
lowercase__: List[Any] = merges
lowercase__: str = BytePairTokenizer(lowerCAmelCase__ , lowerCAmelCase__ , sequence_length=lowerCAmelCase__ )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Any:
'''simple docstring'''
lowercase__: Tuple = [' '.join(lowerCAmelCase__ ) for m in tokenizer.bpe_ranks.keys()]
lowercase__: List[Any] = tokenizer.get_vocab()
return cls(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
lowercase__: int = GPTaTokenizer.from_pretrained(lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ )
return cls.from_tokenizer(lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
return cls(**lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Optional[Any]:
'''simple docstring'''
lowercase__: Optional[Any] = self.tf_tokenizer(lowerCAmelCase__ )
lowercase__: List[Any] = tf.ones_like(lowerCAmelCase__ )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowercase__: int = max_length if max_length is not None else self.max_length
if max_length is not None:
lowercase__ , lowercase__: List[Any] = pad_model_inputs(
lowerCAmelCase__ , max_seq_length=lowerCAmelCase__ , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 288 | 0 |
'''simple docstring'''
import os
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(a__ ) , """num.txt""" )
with open(a__ ) as file_hand:
return str(sum(int(a__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 267 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : Optional[int] = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCAmelCase : Optional[int] = 2_5_6_0_4_7
UpperCAmelCase : Union[str, Any] = 2_5_6_1_4_5
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase__ ( a , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = NllbTokenizer
lowerCAmelCase__ = NllbTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = {}
def UpperCAmelCase__ ( self : List[Any] ) -> int:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE = NllbTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self : Dict ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = NllbTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__SCREAMING_SNAKE_CASE , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def UpperCAmelCase__ ( self : Dict ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
__SCREAMING_SNAKE_CASE = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=True
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it save with the same files
self.assertSequenceEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
# Save tokenizer rust, legacy_format=False
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
shutil.rmtree(__SCREAMING_SNAKE_CASE )
@require_torch
def UpperCAmelCase__ ( self : List[Any] ) -> Any:
"""simple docstring"""
if not self.test_seqaseq:
return
__SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
__SCREAMING_SNAKE_CASE = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
""" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
""" will only worsen the violence and misery for millions of people.""",
]
__SCREAMING_SNAKE_CASE = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
""" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
""" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
try:
__SCREAMING_SNAKE_CASE = tokenizer.prepare_seqaseq_batch(
src_texts=__SCREAMING_SNAKE_CASE , tgt_texts=__SCREAMING_SNAKE_CASE , max_length=3 , max_target_length=10 , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__SCREAMING_SNAKE_CASE = tokenizer.prepare_seqaseq_batch(
__SCREAMING_SNAKE_CASE , tgt_texts=__SCREAMING_SNAKE_CASE , max_length=3 , return_tensors="""pt""" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__SCREAMING_SNAKE_CASE = tokenizer.prepare_seqaseq_batch(
src_texts=__SCREAMING_SNAKE_CASE , max_length=3 , max_target_length=10 , return_tensors="""pt""" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("""decoder_input_ids""" , __SCREAMING_SNAKE_CASE )
@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece.""" )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
pass
def UpperCAmelCase__ ( self : List[str] ) -> Dict:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__SCREAMING_SNAKE_CASE = [AddedToken("""<special>""" , lstrip=__SCREAMING_SNAKE_CASE )]
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , additional_special_tokens=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_r.encode("""Hey this is a <special> token""" )
__SCREAMING_SNAKE_CASE = tokenizer_r.encode("""<special>""" , add_special_tokens=__SCREAMING_SNAKE_CASE )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , additional_special_tokens=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , additional_special_tokens=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = tokenizer_p.encode("""Hey this is a <special> token""" )
__SCREAMING_SNAKE_CASE = tokenizer_cr.encode("""Hey this is a <special> token""" )
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = "facebook/nllb-200-distilled-600M"
lowerCAmelCase__ = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
lowerCAmelCase__ = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
lowerCAmelCase__ = [
256047,
16297,
134408,
8165,
248066,
14734,
950,
1135,
105721,
3573,
83,
27352,
108,
49486,
2,
]
@classmethod
def UpperCAmelCase__ ( cls : List[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" )
__SCREAMING_SNAKE_CASE = 1
return cls
def UpperCAmelCase__ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""] , 256_001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""] , 256_002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""] , 256_057 )
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Any ) -> int:
"""simple docstring"""
self.assertIn(__SCREAMING_SNAKE_CASE , self.tokenizer.all_special_ids )
# fmt: off
__SCREAMING_SNAKE_CASE = [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
# fmt: on
__SCREAMING_SNAKE_CASE = self.tokenizer.decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertNotIn(self.tokenizer.eos_token , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = 10
__SCREAMING_SNAKE_CASE = self.tokenizer(__SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , __SCREAMING_SNAKE_CASE )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : int ) -> List[Any]:
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [256_203, 3] )
def UpperCAmelCase__ ( self : str ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = NllbTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __SCREAMING_SNAKE_CASE )
@require_torch
def UpperCAmelCase__ ( self : str ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
__SCREAMING_SNAKE_CASE = shift_tokens_right(
batch["""labels"""] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["""ron_Latn"""] )
self.assertIsInstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__SCREAMING_SNAKE_CASE = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __SCREAMING_SNAKE_CASE )
self.assertEqual(__SCREAMING_SNAKE_CASE , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , max_length=3 , return_tensors="""pt""" )
__SCREAMING_SNAKE_CASE = self.tokenizer(
text_target=self.tgt_text , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , max_length=10 , return_tensors="""pt""" )
__SCREAMING_SNAKE_CASE = targets["""input_ids"""]
__SCREAMING_SNAKE_CASE = shift_tokens_right(
__SCREAMING_SNAKE_CASE , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCAmelCase__ ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
nested_simplify(__SCREAMING_SNAKE_CASE ) , {
# A, test, EOS, en_XX
"""input_ids""": [[256_047, 70, 7_356, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 256_057,
} , )
@require_torch
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047] )
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2] )
| 267 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def snake_case ( lowerCamelCase : ArgumentParser )-> int:
raise NotImplementedError()
@abstractmethod
def snake_case ( self : Optional[int] )-> Tuple:
raise NotImplementedError()
| 353 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def snake_case__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int , __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ : Any ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
'''de''': '''Maschinelles Lernen ist großartig, nicht wahr?''',
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
lowerCamelCase__ : Optional[Any] ={
'''wmt16-en-de-dist-12-1''': [28.3, 27.52],
'''wmt16-en-de-dist-6-1''': [27.4, 27.11],
'''wmt16-en-de-12-1''': [26.9, 25.75],
}
lowerCamelCase__ : Any =f'''{src_lang}-{tgt_lang}'''
lowerCamelCase__ : Any =f'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 models are available:
* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
## Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
## Eval results
Here are the BLEU scores:
model | fairseq | transformers
-------|---------|----------
{model_name} | {scores[model_name][0]} | {scores[model_name][1]}
The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
## Data Sources
- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)
### BibTeX entry and citation info
```
@misc{{kasai2020deep,
title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
year={{2020}},
eprint={{2006.10369}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}}
}}
```
'''
model_card_dir.mkdir(parents=__lowerCamelCase , exist_ok=__lowerCamelCase )
lowerCamelCase__ : str =os.path.join(__lowerCamelCase , '''README.md''' )
print(f'''Generating {path}''' )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(__lowerCamelCase )
# make sure we are under the root of the project
_lowercase : List[str] = Path(__file__).resolve().parent.parent.parent
_lowercase : Dict = repo_dir / "model_cards"
for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
_lowercase : int = model_cards_dir / "allenai" / model_name
write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)
| 272 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
def _snake_case ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
lowercase_ : int = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Tuple = 'sshleifer/tiny-gpt2'
lowercase_ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=__SCREAMING_SNAKE_CASE , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : List[Any] = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE )
lowercase_ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[Any] = 'sgugger/tiny-distilbert-classification'
lowercase_ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__SCREAMING_SNAKE_CASE , only_pretrain_model=__SCREAMING_SNAKE_CASE , )
lowercase_ : str = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE )
lowercase_ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Union[str, Any] = 'sshleifer/tiny-gpt2'
lowercase_ : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : List[str] = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE )
lowercase_ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : str = 'sshleifer/tiny-gpt2'
lowercase_ : str = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase_ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=__SCREAMING_SNAKE_CASE , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : Optional[Any] = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE , [config] )
lowercase_ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Dict = 'sshleifer/tiny-gpt2'
lowercase_ : Union[str, Any] = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : str = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE , [config] )
lowercase_ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = 'sshleifer/tiny-gpt2'
lowercase_ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : Tuple = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE )
lowercase_ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[Any] = 'sshleifer/tiny-gpt2'
lowercase_ : List[Any] = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase_ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : Optional[Any] = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE , [config] )
lowercase_ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = 'patrickvonplaten/t5-tiny-random'
lowercase_ : Optional[int] = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase_ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : List[Any] = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE , configs=[config] )
lowercase_ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , '''Cannot do xla on CPU.''' )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Optional[int] = 'sshleifer/tiny-gpt2'
lowercase_ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=__SCREAMING_SNAKE_CASE , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , use_xla=__SCREAMING_SNAKE_CASE , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : int = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE )
lowercase_ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : str = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=__SCREAMING_SNAKE_CASE , save_to_csv=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(__SCREAMING_SNAKE_CASE , '''inf_time.csv''' ) , inference_memory_csv_file=os.path.join(__SCREAMING_SNAKE_CASE , '''inf_mem.csv''' ) , env_info_csv_file=os.path.join(__SCREAMING_SNAKE_CASE , '''env.csv''' ) , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : List[str] = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE )
benchmark.run()
self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE , '''env.csv''' ) ).exists() )
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : Union[str, Any] = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(__SCREAMING_SNAKE_CASE ):
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''sequential''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''cumulative''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''current''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=__SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(__SCREAMING_SNAKE_CASE , '''log.txt''' ) , log_print=__SCREAMING_SNAKE_CASE , trace_memory_line_by_line=__SCREAMING_SNAKE_CASE , eager_mode=__SCREAMING_SNAKE_CASE , multi_process=__SCREAMING_SNAKE_CASE , )
lowercase_ : List[str] = TensorFlowBenchmark(__SCREAMING_SNAKE_CASE )
lowercase_ : Any = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE , '''log.txt''' ) ).exists() )
| 93 |
def A_ ( A__ ) -> int:
stooge(A__ , 0 , len(A__ ) - 1 )
return arr
def A_ ( A__ , A__ , A__ ) -> List[Any]:
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
a__ , a__ : List[str] = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
a__ : Dict = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(A__ , A__ , (h - t) )
# Recursively sort last 2/3 elements
stooge(A__ , i + t , (A__) )
# Recursively sort first 2/3 elements
stooge(A__ , A__ , (h - t) )
if __name__ == "__main__":
lowercase : Dict = input("""Enter numbers separated by a comma:\n""").strip()
lowercase : Dict = [int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted))
| 99 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 10_00 ):
"""simple docstring"""
_snake_case , _snake_case : List[Any] = 1, 1
_snake_case : str = []
for i in range(1 , n + 1 ):
_snake_case : Any = prev_numerator + 2 * prev_denominator
_snake_case : Optional[Any] = prev_numerator + prev_denominator
if len(str(snake_case__ ) ) > len(str(snake_case__ ) ):
result.append(snake_case__ )
_snake_case : int = numerator
_snake_case : Any = denominator
return len(snake_case__ )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 132 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : Optional[Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = []
_snake_case : Dict = set({"""(""", """[""", """{"""} )
_snake_case : Union[str, Any] = set({""")""", """]""", """}"""} )
_snake_case : Tuple = {"""{""": """}""", """[""": """]""", """(""": """)"""}
for i in range(len(snake_case__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(snake_case__ ) == 0 or (len(snake_case__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(snake_case__ ) == 0
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case : Any = input("""Enter sequence of brackets: """ )
if is_balanced(snake_case__ ):
print(snake_case__ , """is balanced""" )
else:
print(snake_case__ , """is not balanced""" )
if __name__ == "__main__":
main()
| 132 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCamelCase :Optional[int] = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase :Optional[int] = [
'''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ErnieForCausalLM''',
'''ErnieForMaskedLM''',
'''ErnieForMultipleChoice''',
'''ErnieForNextSentencePrediction''',
'''ErnieForPreTraining''',
'''ErnieForQuestionAnswering''',
'''ErnieForSequenceClassification''',
'''ErnieForTokenClassification''',
'''ErnieModel''',
'''ErniePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
lowerCamelCase :str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 206 |
'''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 :str = TypeVar('''T''')
class _lowerCAmelCase ( Generic[T] ):
def __init__(self , lowercase = True ):
A_ : dict[T, list[T]] = {} # dictionary of lists
A_ : Any = directed
def _a (self , lowercase , lowercase ):
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(lowercase )
self.adj_list[destination_vertex].append(lowercase )
# 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(lowercase )
A_ : Dict = [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(lowercase )
A_ : 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:
A_ : Optional[Any] = [destination_vertex]
A_ : Tuple = [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(lowercase )
# 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(lowercase )
A_ : Tuple = []
# 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:
A_ : Tuple = [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:
A_ : int = [destination_vertex]
A_ : List[str] = []
return self
def __repr__(self ):
return pformat(self.adj_list ) | 206 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A ( __snake_case ):
lowercase__: Union[str, Any] = ['''image_processor''', '''tokenizer''']
lowercase__: int = '''CLIPImageProcessor'''
lowercase__: List[str] = ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')
def __init__( self : List[str] , __magic_name__ : List[str]=None , __magic_name__ : List[Any]=None , **__magic_name__ : List[str] ) -> int:
"""simple docstring"""
__snake_case : Any = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , a_ , )
__snake_case : Union[str, Any] = kwargs.pop("""feature_extractor""" )
__snake_case : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(a_ , a_ )
def __call__( self : Tuple , __magic_name__ : Any=None , __magic_name__ : Union[str, Any]=None , __magic_name__ : Union[str, Any]=None , **__magic_name__ : int ) -> Any:
"""simple docstring"""
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
__snake_case : Dict = self.tokenizer(a_ , return_tensors=a_ , **a_ )
if images is not None:
__snake_case : Optional[int] = self.image_processor(a_ , return_tensors=a_ , **a_ )
if text is not None and images is not None:
__snake_case : List[str] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**a_ ) , tensor_type=a_ )
def lowercase__ ( self : str , *__magic_name__ : Tuple , **__magic_name__ : Tuple ) -> List[str]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def lowercase__ ( self : Optional[Any] , *__magic_name__ : Optional[Any] , **__magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@property
def lowercase__ ( self : Tuple ) -> Any:
"""simple docstring"""
__snake_case : int = self.tokenizer.model_input_names
__snake_case : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 351 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
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 import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _A :
def __init__( self : Optional[Any] , __magic_name__ : List[Any] , __magic_name__ : Tuple=2 , __magic_name__ : List[Any]=3 , __magic_name__ : Optional[int]=4 , __magic_name__ : Any=2 , __magic_name__ : Union[str, Any]=7 , __magic_name__ : Dict=True , __magic_name__ : Optional[Any]=True , __magic_name__ : Union[str, Any]=True , __magic_name__ : int=True , __magic_name__ : List[Any]=99 , __magic_name__ : List[Any]=36 , __magic_name__ : List[Any]=2 , __magic_name__ : str=4 , __magic_name__ : int=37 , __magic_name__ : int="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : Union[str, Any]=0.1 , __magic_name__ : int=5_12 , __magic_name__ : Union[str, Any]=16 , __magic_name__ : Optional[Any]=2 , __magic_name__ : Tuple=0.02 , __magic_name__ : List[str]=6 , __magic_name__ : Dict=6 , __magic_name__ : Optional[Any]=3 , __magic_name__ : str=4 , __magic_name__ : Union[str, Any]=None , __magic_name__ : Union[str, Any]=10_00 , ) -> int:
"""simple docstring"""
__snake_case : Optional[Any] = parent
__snake_case : Tuple = batch_size
__snake_case : List[Any] = num_channels
__snake_case : Dict = image_size
__snake_case : Tuple = patch_size
__snake_case : str = is_training
__snake_case : Optional[Any] = use_input_mask
__snake_case : int = use_token_type_ids
__snake_case : str = use_labels
__snake_case : Dict = vocab_size
__snake_case : List[Any] = hidden_size
__snake_case : List[str] = num_hidden_layers
__snake_case : Dict = num_attention_heads
__snake_case : Union[str, Any] = intermediate_size
__snake_case : str = hidden_act
__snake_case : Dict = hidden_dropout_prob
__snake_case : Any = attention_probs_dropout_prob
__snake_case : int = max_position_embeddings
__snake_case : Optional[int] = type_vocab_size
__snake_case : Tuple = type_sequence_label_size
__snake_case : int = initializer_range
__snake_case : Optional[int] = coordinate_size
__snake_case : List[Any] = shape_size
__snake_case : Tuple = num_labels
__snake_case : List[Any] = num_choices
__snake_case : Optional[Any] = scope
__snake_case : List[str] = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__snake_case : List[str] = text_seq_length
__snake_case : str = (image_size // patch_size) ** 2 + 1
__snake_case : Optional[Any] = self.text_seq_length + self.image_seq_length
def lowercase__ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__snake_case : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__snake_case : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
__snake_case : Optional[int] = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__snake_case : Union[str, Any] = bbox[i, j, 3]
__snake_case : Union[str, Any] = bbox[i, j, 1]
__snake_case : Any = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
__snake_case : Optional[Any] = bbox[i, j, 2]
__snake_case : Tuple = bbox[i, j, 0]
__snake_case : Optional[Any] = tmp_coordinate
__snake_case : Dict = tf.constant(__magic_name__ )
__snake_case : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__snake_case : Any = None
if self.use_input_mask:
__snake_case : str = random_attention_mask([self.batch_size, self.text_seq_length] )
__snake_case : List[Any] = None
if self.use_token_type_ids:
__snake_case : Any = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__snake_case : str = None
__snake_case : List[Any] = None
if self.use_labels:
__snake_case : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case : str = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__snake_case : List[str] = LayoutLMvaConfig(
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 , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def lowercase__ ( self : List[str] , __magic_name__ : Optional[int] , __magic_name__ : str , __magic_name__ : int , __magic_name__ : Any , __magic_name__ : Optional[int] , __magic_name__ : Dict ) -> List[str]:
"""simple docstring"""
__snake_case : Optional[int] = TFLayoutLMvaModel(config=__magic_name__ )
# text + image
__snake_case : Optional[int] = model(__magic_name__ , pixel_values=__magic_name__ , training=__magic_name__ )
__snake_case : List[str] = model(
__magic_name__ , bbox=__magic_name__ , pixel_values=__magic_name__ , attention_mask=__magic_name__ , token_type_ids=__magic_name__ , training=__magic_name__ , )
__snake_case : Optional[int] = model(__magic_name__ , bbox=__magic_name__ , pixel_values=__magic_name__ , training=__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__snake_case : Union[str, Any] = model(__magic_name__ , training=__magic_name__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__snake_case : Optional[Any] = model({"""pixel_values""": pixel_values} , training=__magic_name__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def lowercase__ ( self : List[Any] , __magic_name__ : Optional[int] , __magic_name__ : Union[str, Any] , __magic_name__ : List[Any] , __magic_name__ : List[Any] , __magic_name__ : Tuple , __magic_name__ : Tuple , __magic_name__ : str ) -> Any:
"""simple docstring"""
__snake_case : Any = self.num_labels
__snake_case : Optional[int] = TFLayoutLMvaForSequenceClassification(config=__magic_name__ )
__snake_case : List[Any] = model(
__magic_name__ , bbox=__magic_name__ , pixel_values=__magic_name__ , attention_mask=__magic_name__ , token_type_ids=__magic_name__ , labels=__magic_name__ , training=__magic_name__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase__ ( self : Any , __magic_name__ : Any , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : Tuple , __magic_name__ : Union[str, Any] , __magic_name__ : int , __magic_name__ : Tuple ) -> List[str]:
"""simple docstring"""
__snake_case : str = self.num_labels
__snake_case : str = TFLayoutLMvaForTokenClassification(config=__magic_name__ )
__snake_case : Tuple = model(
__magic_name__ , bbox=__magic_name__ , pixel_values=__magic_name__ , attention_mask=__magic_name__ , token_type_ids=__magic_name__ , labels=__magic_name__ , training=__magic_name__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def lowercase__ ( self : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : List[Any] , __magic_name__ : Optional[Any] , __magic_name__ : Optional[Any] , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : List[str] ) -> List[str]:
"""simple docstring"""
__snake_case : Optional[int] = 2
__snake_case : Dict = TFLayoutLMvaForQuestionAnswering(config=__magic_name__ )
__snake_case : List[Any] = model(
__magic_name__ , bbox=__magic_name__ , pixel_values=__magic_name__ , attention_mask=__magic_name__ , token_type_ids=__magic_name__ , start_positions=__magic_name__ , end_positions=__magic_name__ , training=__magic_name__ , )
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 lowercase__ ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
__snake_case : List[Any] = self.prepare_config_and_inputs()
((__snake_case) , (__snake_case) , (__snake_case) , (__snake_case) , (__snake_case) , (__snake_case) , (__snake_case) , (__snake_case)) : Dict = config_and_inputs
__snake_case : List[Any] = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_tf
class _A ( __lowercase , __lowercase , unittest.TestCase ):
lowercase__: Optional[int] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
lowercase__: Union[str, Any] = (
{'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
lowercase__: Dict = False
lowercase__: int = False
lowercase__: Dict = False
def lowercase__ ( self : int , __magic_name__ : Optional[Any] , __magic_name__ : List[Any] , __magic_name__ : Dict , __magic_name__ : Dict , __magic_name__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
return True
def lowercase__ ( self : int , __magic_name__ : Optional[int] , __magic_name__ : List[Any] , __magic_name__ : int=False ) -> dict:
"""simple docstring"""
__snake_case : Any = copy.deepcopy(__magic_name__ )
if model_class in get_values(__magic_name__ ):
__snake_case : Union[str, Any] = {
k: tf.tile(tf.expand_dims(__magic_name__ , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(__magic_name__ , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(__magic_name__ ):
__snake_case : str = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__magic_name__ ):
__snake_case : Any = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
__snake_case : Dict = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__magic_name__ ):
__snake_case : Dict = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__magic_name__ ):
__snake_case : int = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def lowercase__ ( self : Any ) -> int:
"""simple docstring"""
__snake_case : str = TFLayoutLMvaModelTester(self )
__snake_case : int = ConfigTester(self , config_class=__magic_name__ , hidden_size=37 )
def lowercase__ ( self : List[str] ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
__snake_case , __snake_case : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case : str = model_class(__magic_name__ )
if getattr(__magic_name__ , """hf_compute_loss""" , __magic_name__ ):
# The number of elements in the loss should be the same as the number of elements in the label
__snake_case : str = self._prepare_for_class(inputs_dict.copy() , __magic_name__ , return_labels=__magic_name__ )
__snake_case : Any = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=__magic_name__ )[0]
]
__snake_case : List[str] = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
__snake_case : Any = self._prepare_for_class(inputs_dict.copy() , __magic_name__ , return_labels=__magic_name__ )
__snake_case : Tuple = prepared_for_class.pop("""input_ids""" )
__snake_case : Union[str, Any] = model(__magic_name__ , **__magic_name__ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
__snake_case : Union[str, Any] = self._prepare_for_class(inputs_dict.copy() , __magic_name__ , return_labels=__magic_name__ )
__snake_case : str = prepared_for_class.pop("""input_ids""" )
if "labels" in prepared_for_class:
__snake_case : str = prepared_for_class["""labels"""].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
__snake_case : Dict = -1_00
__snake_case : str = tf.convert_to_tensor(__magic_name__ )
__snake_case : Optional[Any] = model(__magic_name__ , **__magic_name__ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
__snake_case : Optional[int] = self._prepare_for_class(inputs_dict.copy() , __magic_name__ , return_labels=__magic_name__ )
__snake_case : Tuple = model(__magic_name__ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
__snake_case : str = self._prepare_for_class(inputs_dict.copy() , __magic_name__ , return_labels=__magic_name__ )
# Get keys that were added with the _prepare_for_class function
__snake_case : Tuple = prepared_for_class.keys() - inputs_dict.keys()
__snake_case : Optional[Any] = inspect.signature(model.call ).parameters
__snake_case : int = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
__snake_case : Union[str, Any] = {0: """input_ids"""}
for label_key in label_keys:
__snake_case : int = signature_names.index(__magic_name__ )
__snake_case : Optional[int] = label_key
__snake_case : Optional[int] = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
__snake_case : Any = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
__snake_case : List[str] = prepared_for_class[value]
__snake_case : str = tuple(__magic_name__ )
# Send to model
__snake_case : List[Any] = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def lowercase__ ( self : List[str] ) -> List[Any]:
"""simple docstring"""
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
def lowercase__ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) : List[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__snake_case : Tuple = type
self.model_tester.create_and_check_model(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
def lowercase__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
def lowercase__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
def lowercase__ ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
@slow
def lowercase__ ( self : str ) -> Optional[int]:
"""simple docstring"""
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : str = TFLayoutLMvaModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def _a ( ) -> Optional[Any]:
"""simple docstring"""
__snake_case : int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
class _A ( unittest.TestCase ):
@cached_property
def lowercase__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=__magic_name__ ) if is_vision_available() else None
@slow
def lowercase__ ( self : str ) -> str:
"""simple docstring"""
__snake_case : Dict = TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )
__snake_case : str = self.default_image_processor
__snake_case : Union[str, Any] = prepare_img()
__snake_case : List[Any] = image_processor(images=__magic_name__ , return_tensors="""tf""" ).pixel_values
__snake_case : Tuple = tf.constant([[1, 2]] )
__snake_case : Tuple = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
__snake_case : List[Any] = model(input_ids=__magic_name__ , bbox=__magic_name__ , pixel_values=__magic_name__ , training=__magic_name__ )
# verify the logits
__snake_case : List[str] = (1, 1_99, 7_68)
self.assertEqual(outputs.last_hidden_state.shape , __magic_name__ )
__snake_case : Tuple = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , __magic_name__ , atol=1E-4 ) )
| 13 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Optional[Any]:
'''simple docstring'''
lowerCamelCase__: Optional[int] =FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
lowerCamelCase__: Optional[int] =AutoTokenizer.from_pretrained("google/mt5-small")
lowerCamelCase__: List[Any] =tokenizer("Hello there" , return_tensors="np").input_ids
lowerCamelCase__: Dict =tokenizer("Hi I am" , return_tensors="np").input_ids
lowerCamelCase__: Tuple =shift_tokens_right(UpperCAmelCase_ , model.config.pad_token_id , model.config.decoder_start_token_id)
lowerCamelCase__: Optional[Any] =model(UpperCAmelCase_ , decoder_input_ids=UpperCAmelCase_).logits
lowerCamelCase__: Optional[Any] =optax.softmax_cross_entropy(UpperCAmelCase_ , onehot(UpperCAmelCase_ , logits.shape[-1])).mean()
lowerCamelCase__: Dict =-(labels.shape[-1] * loss.item())
lowerCamelCase__: List[str] =-84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)
| 10 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
UpperCAmelCase__ = ['TensorrtExecutionProvider', 'CUDAExecutionProvider']
UpperCAmelCase__ = ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase__ = ort.RunOptions()
UpperCAmelCase__ = 128
UpperCAmelCase__ = 1
UpperCAmelCase__ = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__ = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__ = np.ones((batch, sequence), dtype=np.intaa)
print('Warm up phase...')
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print('Start inference...')
UpperCAmelCase__ = time.time()
UpperCAmelCase__ = 2000
UpperCAmelCase__ = {}
for iter in range(max_iters):
UpperCAmelCase__ = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))
| 288 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def UpperCamelCase ( _A : Optional[Any] , _A : List[str] , _A : List[str] )-> Optional[int]:
"""simple docstring"""
if isinstance(_A , torch.Tensor ):
return image
elif isinstance(_A , PIL.Image.Image ):
A__ = [image]
if isinstance(image[0] , PIL.Image.Image ):
A__ = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
A__ = np.concatenate(_A , axis=0 )
A__ = np.array(_A ).astype(np.floataa ) / 255.0
A__ = image.transpose(0 , 3 , 1 , 2 )
A__ = 2.0 * image - 1.0
A__ = torch.from_numpy(_A )
elif isinstance(image[0] , torch.Tensor ):
A__ = torch.cat(_A , dim=0 )
return image
def UpperCamelCase ( _A : int , _A : Optional[Any] , _A : int , _A : Union[str, Any]=0.9995 )-> Any:
"""simple docstring"""
if not isinstance(_A , np.ndarray ):
A__ = True
A__ = va.device
A__ = va.cpu().numpy()
A__ = va.cpu().numpy()
A__ = np.sum(va * va / (np.linalg.norm(_A ) * np.linalg.norm(_A )) )
if np.abs(_A ) > DOT_THRESHOLD:
A__ = (1 - t) * va + t * va
else:
A__ = np.arccos(_A )
A__ = np.sin(_A )
A__ = theta_a * t
A__ = np.sin(_A )
A__ = np.sin(theta_a - theta_t ) / sin_theta_a
A__ = sin_theta_t / sin_theta_a
A__ = sa * va + sa * va
if inputs_are_torch:
A__ = torch.from_numpy(_A ).to(_A )
return va
def UpperCamelCase ( _A : Optional[Any] , _A : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
A__ = F.normalize(_A , dim=-1 )
A__ = F.normalize(_A , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def UpperCamelCase ( _A : Dict , _A : List[Any] )-> Any:
"""simple docstring"""
for param in model.parameters():
A__ = value
class UpperCamelCase ( _UpperCAmelCase ):
def __init__( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , ):
super().__init__()
self.register_modules(
vae=UpperCAmelCase__ , text_encoder=UpperCAmelCase__ , clip_model=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ , feature_extractor=UpperCAmelCase__ , coca_model=UpperCAmelCase__ , coca_tokenizer=UpperCAmelCase__ , coca_transform=UpperCAmelCase__ , )
A__ = (
feature_extractor.size
if isinstance(feature_extractor.size , UpperCAmelCase__ )
else feature_extractor.size["shortest_edge"]
)
A__ = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , UpperCAmelCase__ )
set_requires_grad(self.clip_model , UpperCAmelCase__ )
def __A ( self , UpperCAmelCase__ = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
A__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(UpperCAmelCase__ )
def __A ( self ):
self.enable_attention_slicing(UpperCAmelCase__ )
def __A ( self ):
set_requires_grad(self.vae , UpperCAmelCase__ )
def __A ( self ):
set_requires_grad(self.vae , UpperCAmelCase__ )
def __A ( self ):
set_requires_grad(self.unet , UpperCAmelCase__ )
def __A ( self ):
set_requires_grad(self.unet , UpperCAmelCase__ )
def __A ( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
# get the original timestep using init_timestep
A__ = min(int(num_inference_steps * strength ) , UpperCAmelCase__ )
A__ = max(num_inference_steps - init_timestep , 0 )
A__ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def __A ( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None ):
if not isinstance(UpperCAmelCase__ , torch.Tensor ):
raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(UpperCAmelCase__ )}""" )
A__ = image.to(device=UpperCAmelCase__ , dtype=UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
A__ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(UpperCAmelCase__ )
]
A__ = torch.cat(UpperCAmelCase__ , dim=0 )
else:
A__ = self.vae.encode(UpperCAmelCase__ ).latent_dist.sample(UpperCAmelCase__ )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
A__ = 0.18_215 * init_latents
A__ = init_latents.repeat_interleave(UpperCAmelCase__ , dim=0 )
A__ = randn_tensor(init_latents.shape , generator=UpperCAmelCase__ , device=UpperCAmelCase__ , dtype=UpperCAmelCase__ )
# get latents
A__ = self.scheduler.add_noise(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
A__ = init_latents
return latents
def __A ( self , UpperCAmelCase__ ):
A__ = self.coca_transform(UpperCAmelCase__ ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
A__ = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
A__ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("<end_of_text>" )[0].replace("<start_of_text>" , "" ).rstrip(" .," )
def __A ( self , UpperCAmelCase__ , UpperCAmelCase__ ):
A__ = self.feature_extractor.preprocess(UpperCAmelCase__ )
A__ = torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
A__ = self.clip_model.get_image_features(UpperCAmelCase__ )
A__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=UpperCAmelCase__ )
A__ = image_embeddings_clip.repeat_interleave(UpperCAmelCase__ , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def __A ( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ):
A__ = latents.detach().requires_grad_()
A__ = self.scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
# predict the noise residual
A__ = self.unet(UpperCAmelCase__ , UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
A__ = self.scheduler.alphas_cumprod[timestep]
A__ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
A__ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
A__ = torch.sqrt(UpperCAmelCase__ )
A__ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , UpperCAmelCase__ ):
A__ = self.scheduler.sigmas[index]
A__ = latents - sigma * noise_pred
else:
raise ValueError(F"""scheduler type {type(self.scheduler )} not supported""" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
A__ = 1 / 0.18_215 * sample
A__ = self.vae.decode(UpperCAmelCase__ ).sample
A__ = (image / 2 + 0.5).clamp(0 , 1 )
A__ = transforms.Resize(self.feature_extractor_size )(UpperCAmelCase__ )
A__ = self.normalize(UpperCAmelCase__ ).to(latents.dtype )
A__ = self.clip_model.get_image_features(UpperCAmelCase__ )
A__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=UpperCAmelCase__ )
A__ = spherical_dist_loss(UpperCAmelCase__ , UpperCAmelCase__ ).mean() * clip_guidance_scale
A__ = -torch.autograd.grad(UpperCAmelCase__ , UpperCAmelCase__ )[0]
if isinstance(self.scheduler , UpperCAmelCase__ ):
A__ = latents.detach() + grads * (sigma**2)
A__ = noise_pred_original
else:
A__ = noise_pred_original - torch.sqrt(UpperCAmelCase__ ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = None , UpperCAmelCase__ = None , UpperCAmelCase__ = 512 , UpperCAmelCase__ = 512 , UpperCAmelCase__ = 0.6 , UpperCAmelCase__ = 50 , UpperCAmelCase__ = 7.5 , UpperCAmelCase__ = 1 , UpperCAmelCase__ = 0.0 , UpperCAmelCase__ = 100 , UpperCAmelCase__ = None , UpperCAmelCase__ = "pil" , UpperCAmelCase__ = True , UpperCAmelCase__ = 0.8 , UpperCAmelCase__ = 0.1 , UpperCAmelCase__ = 0.1 , ):
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and len(UpperCAmelCase__ ) != batch_size:
raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(UpperCAmelCase__ )} generators.""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if isinstance(UpperCAmelCase__ , torch.Generator ) and batch_size > 1:
A__ = [generator] + [None] * (batch_size - 1)
A__ = [
("model", self.coca_model is None),
("tokenizer", self.coca_tokenizer is None),
("transform", self.coca_transform is None),
]
A__ = [x[0] for x in coca_is_none if x[1]]
A__ = ", ".join(UpperCAmelCase__ )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(UpperCAmelCase__ ):
raise ValueError(
F"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
F"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
A__ = self.get_image_description(UpperCAmelCase__ )
if style_prompt is None:
if len(UpperCAmelCase__ ):
raise ValueError(
F"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
F""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
A__ = self.get_image_description(UpperCAmelCase__ )
# get prompt text embeddings for content and style
A__ = self.tokenizer(
UpperCAmelCase__ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=UpperCAmelCase__ , return_tensors="pt" , )
A__ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
A__ = self.tokenizer(
UpperCAmelCase__ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=UpperCAmelCase__ , return_tensors="pt" , )
A__ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
A__ = slerp(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# duplicate text embeddings for each generation per prompt
A__ = text_embeddings.repeat_interleave(UpperCAmelCase__ , dim=0 )
# set timesteps
A__ = "offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
A__ = {}
if accepts_offset:
A__ = 1
self.scheduler.set_timesteps(UpperCAmelCase__ , **UpperCAmelCase__ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
A__ , A__ = self.get_timesteps(UpperCAmelCase__ , UpperCAmelCase__ , self.device )
A__ = timesteps[:1].repeat(UpperCAmelCase__ )
# Preprocess image
A__ = preprocess(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
A__ = self.prepare_latents(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , text_embeddings.dtype , self.device , UpperCAmelCase__ )
A__ = preprocess(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
A__ = self.prepare_latents(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , text_embeddings.dtype , self.device , UpperCAmelCase__ )
A__ = slerp(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if clip_guidance_scale > 0:
A__ = self.get_clip_image_embeddings(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = self.get_clip_image_embeddings(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = slerp(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
A__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
A__ = content_text_input.input_ids.shape[-1]
A__ = self.tokenizer([""] , padding="max_length" , max_length=UpperCAmelCase__ , return_tensors="pt" )
A__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
A__ = uncond_embeddings.repeat_interleave(UpperCAmelCase__ , dim=0 )
# 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
A__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
A__ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
A__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
A__ = torch.randn(UpperCAmelCase__ , generator=UpperCAmelCase__ , device="cpu" , dtype=UpperCAmelCase__ ).to(
self.device )
else:
A__ = torch.randn(UpperCAmelCase__ , generator=UpperCAmelCase__ , device=self.device , dtype=UpperCAmelCase__ )
else:
if latents.shape != latents_shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
A__ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
A__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
A__ = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
A__ = {}
if accepts_eta:
A__ = eta
# check if the scheduler accepts generator
A__ = "generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
A__ = generator
with self.progress_bar(total=UpperCAmelCase__ ):
for i, t in enumerate(UpperCAmelCase__ ):
# expand the latents if we are doing classifier free guidance
A__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A__ = self.scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
# predict the noise residual
A__ = self.unet(UpperCAmelCase__ , UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
A__ , A__ = noise_pred.chunk(2 )
A__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
A__ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
A__ , A__ = self.cond_fn(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , )
# compute the previous noisy sample x_t -> x_t-1
A__ = self.scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
A__ = 1 / 0.18_215 * latents
A__ = self.vae.decode(UpperCAmelCase__ ).sample
A__ = (image / 2 + 0.5).clamp(0 , 1 )
A__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A__ = self.numpy_to_pil(UpperCAmelCase__ )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=UpperCAmelCase__ , nsfw_content_detected=UpperCAmelCase__ )
| 198 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( _A : List[Any] , _A : List[str]=7 )-> Optional[Any]:
"""simple docstring"""
A__ = None
if token is not None:
A__ = {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}
# The id of a workflow (not of a workflow run)
A__ = "636036"
A__ = f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""
A__ = requests.get(_A , headers=_A ).json()
return result["workflow_runs"]
def UpperCamelCase ( _A : str )-> Dict:
"""simple docstring"""
A__ = get_daily_ci_runs(_A )
A__ = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
A__ = workflow_run["id"]
break
return workflow_run_id
def UpperCamelCase ( _A : int , _A : List[str] , _A : str )-> Any:
"""simple docstring"""
A__ = get_last_daily_ci_runs(_A )
if workflow_run_id is not None:
A__ = get_artifacts_links(worflow_run_id=_A , token=_A )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
A__ = artifacts_links[artifact_name]
download_artifact(
artifact_name=_A , artifact_url=_A , output_dir=_A , token=_A )
def UpperCamelCase ( _A : Optional[Any] , _A : Any , _A : List[Any] )-> Optional[int]:
"""simple docstring"""
get_last_daily_ci_artifacts(_A , _A , _A )
A__ = {}
for artifact_name in artifact_names:
A__ = os.path.join(_A , f"""{artifact_name}.zip""" )
if os.path.isfile(_A ):
A__ = {}
with zipfile.ZipFile(_A ) as z:
for filename in z.namelist():
if not os.path.isdir(_A ):
# read the file
with z.open(_A ) as f:
A__ = f.read().decode("UTF-8" )
return results
| 198 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self : Any ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = BlipImageProcessor()
__SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel""" )
__SCREAMING_SNAKE_CASE = BlipProcessor(__lowerCAmelCase , __lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase ).tokenizer
def UpperCAmelCase__ ( self : Dict , **__SCREAMING_SNAKE_CASE : str ) -> List[Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase ).image_processor
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ ( self : int ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase__ ( self : Any ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
__SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCAmelCase )
def UpperCAmelCase__ ( self : Any ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = image_processor(__lowerCAmelCase , return_tensors="""np""" )
__SCREAMING_SNAKE_CASE = processor(images=__lowerCAmelCase , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
__SCREAMING_SNAKE_CASE = """lower newer"""
__SCREAMING_SNAKE_CASE = processor(text=__lowerCAmelCase )
__SCREAMING_SNAKE_CASE = tokenizer(__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase__ ( self : int ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
__SCREAMING_SNAKE_CASE = """lower newer"""
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def UpperCAmelCase__ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
__SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE = processor.batch_decode(__lowerCAmelCase )
__SCREAMING_SNAKE_CASE = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def UpperCAmelCase__ ( self : str ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = BlipProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
__SCREAMING_SNAKE_CASE = """lower newer"""
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
| 267 | '''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : str = '''EncodecFeatureExtractor'''
UpperCAmelCase_ : Dict = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self , __lowerCAmelCase , __lowerCAmelCase):
"""simple docstring"""
super().__init__(__lowerCAmelCase , __lowerCAmelCase)
lowerCAmelCase = self.feature_extractor
lowerCAmelCase = False
def a_ ( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=True):
"""simple docstring"""
return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase , language=__lowerCAmelCase , no_timestamps=__lowerCAmelCase)
def __call__( self , *__lowerCAmelCase , **__lowerCAmelCase):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*__lowerCAmelCase , **__lowerCAmelCase)
lowerCAmelCase = kwargs.pop("""audio""" , __lowerCAmelCase)
lowerCAmelCase = kwargs.pop("""sampling_rate""" , __lowerCAmelCase)
lowerCAmelCase = kwargs.pop("""text""" , __lowerCAmelCase)
if len(__lowerCAmelCase) > 0:
lowerCAmelCase = args[0]
lowerCAmelCase = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""")
if text is not None:
lowerCAmelCase = self.tokenizer(__lowerCAmelCase , **__lowerCAmelCase)
if audio is not None:
lowerCAmelCase = self.feature_extractor(__lowerCAmelCase , *__lowerCAmelCase , sampling_rate=__lowerCAmelCase , **__lowerCAmelCase)
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
lowerCAmelCase = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
lowerCAmelCase = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *__lowerCAmelCase , **__lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = kwargs.pop("""audio""" , __lowerCAmelCase)
lowerCAmelCase = kwargs.pop("""padding_mask""" , __lowerCAmelCase)
if len(__lowerCAmelCase) > 0:
lowerCAmelCase = args[0]
lowerCAmelCase = args[1:]
if audio_values is not None:
return self._decode_audio(__lowerCAmelCase , padding_mask=__lowerCAmelCase)
else:
return self.tokenizer.batch_decode(*__lowerCAmelCase , **__lowerCAmelCase)
def a_ ( self , *__lowerCAmelCase , **__lowerCAmelCase):
"""simple docstring"""
return self.tokenizer.decode(*__lowerCAmelCase , **__lowerCAmelCase)
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase = None):
"""simple docstring"""
lowerCAmelCase = to_numpy(__lowerCAmelCase)
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = audio_values.shape
if padding_mask is None:
return list(__lowerCAmelCase)
lowerCAmelCase = to_numpy(__lowerCAmelCase)
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
lowerCAmelCase = seq_len - padding_mask.shape[-1]
lowerCAmelCase = 1 - self.feature_extractor.padding_value
lowerCAmelCase = np.pad(__lowerCAmelCase , ((0, 0), (0, difference)) , """constant""" , constant_values=__lowerCAmelCase)
lowerCAmelCase = audio_values.tolist()
for i in range(__lowerCAmelCase):
lowerCAmelCase = np.asarray(audio_values[i])[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
lowerCAmelCase = sliced_audio.reshape(__lowerCAmelCase , -1)
return audio_values
| 272 | 0 |
"""simple docstring"""
class lowercase__ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict ) -> int:
'''simple docstring'''
UpperCAmelCase_ = name
UpperCAmelCase_ = value
UpperCAmelCase_ = weight
def __repr__( self : Optional[int] ) -> List[str]:
'''simple docstring'''
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def lowercase__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
return self.value
def lowercase__ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
return self.name
def lowercase__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
return self.weight
def lowercase__ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
return self.value / self.weight
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = []
for i in range(len(lowerCAmelCase__ ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = sorted(lowerCAmelCase__ , key=lowerCAmelCase__ , reverse=lowerCAmelCase__ )
UpperCAmelCase_ = []
UpperCAmelCase_ , UpperCAmelCase_ = 0.0, 0.0
for i in range(len(lowerCAmelCase__ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def a__ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 241 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = XCLIPTextConfig()
# derive patch size from model name
UpperCAmelCase_ = model_name.find("patch" )
UpperCAmelCase_ = int(model_name[start_idx + len("patch" ) : start_idx + len("patch" ) + 2] )
UpperCAmelCase_ = XCLIPVisionConfig(patch_size=lowerCAmelCase__ , num_frames=lowerCAmelCase__ )
if "large" in model_name:
UpperCAmelCase_ = 768
UpperCAmelCase_ = 3072
UpperCAmelCase_ = 12
UpperCAmelCase_ = 1024
UpperCAmelCase_ = 4096
UpperCAmelCase_ = 16
UpperCAmelCase_ = 24
UpperCAmelCase_ = 768
UpperCAmelCase_ = 3072
if model_name == "xclip-large-patch14-16-frames":
UpperCAmelCase_ = 336
UpperCAmelCase_ = XCLIPConfig.from_text_vision_configs(lowerCAmelCase__ , lowerCAmelCase__ )
if "large" in model_name:
UpperCAmelCase_ = 768
return config
def a__ ( lowerCAmelCase__ ):
# text encoder
if name == "token_embedding.weight":
UpperCAmelCase_ = name.replace("token_embedding.weight" , "text_model.embeddings.token_embedding.weight" )
if name == "positional_embedding":
UpperCAmelCase_ = name.replace("positional_embedding" , "text_model.embeddings.position_embedding.weight" )
if "ln_1" in name:
UpperCAmelCase_ = name.replace("ln_1" , "layer_norm1" )
if "ln_2" in name:
UpperCAmelCase_ = name.replace("ln_2" , "layer_norm2" )
if "c_fc" in name:
UpperCAmelCase_ = name.replace("c_fc" , "fc1" )
if "c_proj" in name:
UpperCAmelCase_ = name.replace("c_proj" , "fc2" )
if name.startswith("transformer.resblocks" ):
UpperCAmelCase_ = name.replace("transformer.resblocks" , "text_model.encoder.layers" )
if "attn.out_proj" in name and "message" not in name:
UpperCAmelCase_ = name.replace("attn.out_proj" , "self_attn.out_proj" )
if "ln_final" in name:
UpperCAmelCase_ = name.replace("ln_final" , "text_model.final_layer_norm" )
# visual encoder
if name == "visual.class_embedding":
UpperCAmelCase_ = name.replace("visual.class_embedding" , "vision_model.embeddings.class_embedding" )
if name == "visual.positional_embedding":
UpperCAmelCase_ = name.replace("visual.positional_embedding" , "vision_model.embeddings.position_embedding.weight" )
if name.startswith("visual.transformer.resblocks" ):
UpperCAmelCase_ = name.replace("visual.transformer.resblocks" , "vision_model.encoder.layers" )
if "visual.conv1" in name:
UpperCAmelCase_ = name.replace("visual.conv1" , "vision_model.embeddings.patch_embedding" )
if "visual.ln_pre" in name:
UpperCAmelCase_ = name.replace("visual.ln_pre" , "vision_model.pre_layernorm" )
if "visual.ln_post" in name:
UpperCAmelCase_ = name.replace("visual.ln_post" , "vision_model.post_layernorm" )
if "visual.proj" in name:
UpperCAmelCase_ = name.replace("visual.proj" , "visual_projection.weight" )
if "text_projection" in name:
UpperCAmelCase_ = name.replace("text_projection" , "text_projection.weight" )
# things on top
if "prompts_visual_proj" in name:
UpperCAmelCase_ = name.replace("prompts_visual_proj" , "prompts_visual_projection" )
if "prompts_visual_ln" in name:
UpperCAmelCase_ = name.replace("prompts_visual_ln" , "prompts_visual_layernorm" )
# mit
if name == "mit.positional_embedding":
UpperCAmelCase_ = name.replace("positional" , "position" )
if name.startswith("mit.resblocks" ):
UpperCAmelCase_ = name.replace("mit.resblocks" , "mit.encoder.layers" )
# prompts generator
if name.startswith("prompts_generator.norm" ):
UpperCAmelCase_ = name.replace("prompts_generator.norm" , "prompts_generator.layernorm" )
return name
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase_ = orig_state_dict.pop(lowerCAmelCase__ )
if "attn.in_proj" in key:
UpperCAmelCase_ = key.split("." )
if key.startswith("visual" ):
UpperCAmelCase_ = key_split[3]
UpperCAmelCase_ = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
UpperCAmelCase_ = val[
:dim, :
]
UpperCAmelCase_ = val[
dim : dim * 2, :
]
UpperCAmelCase_ = val[
-dim:, :
]
else:
UpperCAmelCase_ = val[
:dim
]
UpperCAmelCase_ = val[
dim : dim * 2
]
UpperCAmelCase_ = val[
-dim:
]
else:
if "weight" in key:
UpperCAmelCase_ = val[
:dim, :
]
UpperCAmelCase_ = val[
dim : dim * 2, :
]
UpperCAmelCase_ = val[
-dim:, :
]
else:
UpperCAmelCase_ = val[:dim]
UpperCAmelCase_ = val[
dim : dim * 2
]
UpperCAmelCase_ = val[-dim:]
elif key.startswith("mit" ):
UpperCAmelCase_ = key_split[2]
UpperCAmelCase_ = config.vision_config.mit_hidden_size
if "weight" in key:
UpperCAmelCase_ = val[:dim, :]
UpperCAmelCase_ = val[dim : dim * 2, :]
UpperCAmelCase_ = val[-dim:, :]
else:
UpperCAmelCase_ = val[:dim]
UpperCAmelCase_ = val[dim : dim * 2]
UpperCAmelCase_ = val[-dim:]
else:
UpperCAmelCase_ = key_split[2]
UpperCAmelCase_ = config.text_config.hidden_size
if "weight" in key:
UpperCAmelCase_ = val[:dim, :]
UpperCAmelCase_ = val[
dim : dim * 2, :
]
UpperCAmelCase_ = val[-dim:, :]
else:
UpperCAmelCase_ = val[:dim]
UpperCAmelCase_ = val[
dim : dim * 2
]
UpperCAmelCase_ = val[-dim:]
else:
UpperCAmelCase_ = rename_key(lowerCAmelCase__ )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
UpperCAmelCase_ = val.T
UpperCAmelCase_ = val
return orig_state_dict
def a__ ( lowerCAmelCase__ ):
if num_frames == 8:
UpperCAmelCase_ = "eating_spaghetti_8_frames.npy"
elif num_frames == 16:
UpperCAmelCase_ = "eating_spaghetti.npy"
elif num_frames == 32:
UpperCAmelCase_ = "eating_spaghetti_32_frames.npy"
UpperCAmelCase_ = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video" , filename=lowerCAmelCase__ , repo_type="dataset" , )
UpperCAmelCase_ = np.load(lowerCAmelCase__ )
return list(lowerCAmelCase__ )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=False ):
UpperCAmelCase_ = {
# fully supervised kinetics-400 checkpoints
"xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
"xclip-base-patch32-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
),
"xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
"xclip-base-patch16-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
),
"xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
"xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f",
# fully supervised kinetics-600 checkpoints
"xclip-base-patch16-kinetics-600": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
),
"xclip-base-patch16-kinetics-600-16-frames": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
),
"xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
# few shot
"xclip-base-patch16-hmdb-2-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
),
"xclip-base-patch16-hmdb-4-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
),
"xclip-base-patch16-hmdb-8-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
),
"xclip-base-patch16-hmdb-16-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
),
"xclip-base-patch16-ucf-2-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
),
"xclip-base-patch16-ucf-4-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
),
"xclip-base-patch16-ucf-8-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
),
"xclip-base-patch16-ucf-16-shot": (
"https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
),
# zero shot
"xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
}
UpperCAmelCase_ = model_to_url[model_name]
UpperCAmelCase_ = 8
if "16-frames" in model_name:
UpperCAmelCase_ = 16
elif "shot" in model_name:
UpperCAmelCase_ = 32
UpperCAmelCase_ = get_xclip_config(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase_ = XCLIPModel(lowerCAmelCase__ )
model.eval()
if "drive" in checkpoint_url:
UpperCAmelCase_ = "pytorch_model.bin"
gdown.cached_download(lowerCAmelCase__ , lowerCAmelCase__ , quiet=lowerCAmelCase__ )
UpperCAmelCase_ = torch.load(lowerCAmelCase__ , map_location="cpu" )["model"]
else:
UpperCAmelCase_ = torch.hub.load_state_dict_from_url(lowerCAmelCase__ )["model"]
UpperCAmelCase_ = convert_state_dict(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase_ = XCLIPModel(lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model.load_state_dict(lowerCAmelCase__ , strict=lowerCAmelCase__ )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
UpperCAmelCase_ = 336 if model_name == "xclip-large-patch14-16-frames" else 224
UpperCAmelCase_ = VideoMAEImageProcessor(size=lowerCAmelCase__ )
UpperCAmelCase_ = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32" )
UpperCAmelCase_ = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32" )
UpperCAmelCase_ = XCLIPProcessor(image_processor=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ )
UpperCAmelCase_ = prepare_video(lowerCAmelCase__ )
UpperCAmelCase_ = processor(
text=["playing sports", "eating spaghetti", "go shopping"] , videos=lowerCAmelCase__ , return_tensors="pt" , padding=lowerCAmelCase__ )
print("Shape of pixel values:" , inputs.pixel_values.shape )
with torch.no_grad():
UpperCAmelCase_ = model(**lowerCAmelCase__ )
# Verify outputs
UpperCAmelCase_ = outputs.logits_per_video
UpperCAmelCase_ = logits_per_video.softmax(dim=1 )
print("Probs:" , lowerCAmelCase__ )
# kinetics-400
if model_name == "xclip-base-patch32":
UpperCAmelCase_ = torch.tensor([[0.0019, 0.9951, 0.0030]] )
elif model_name == "xclip-base-patch32-16-frames":
UpperCAmelCase_ = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
UpperCAmelCase_ = torch.tensor([[0.0083, 0.9681, 0.0236]] )
elif model_name == "xclip-base-patch16-16-frames":
UpperCAmelCase_ = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
UpperCAmelCase_ = torch.tensor([[0.0062, 0.9864, 0.0075]] )
elif model_name == "xclip-large-patch14-16-frames":
UpperCAmelCase_ = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
UpperCAmelCase_ = torch.tensor([[0.0555, 0.8914, 0.0531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
UpperCAmelCase_ = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
UpperCAmelCase_ = torch.tensor([[0.0036, 0.9920, 0.0045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
UpperCAmelCase_ = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
UpperCAmelCase_ = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
UpperCAmelCase_ = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
UpperCAmelCase_ = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
UpperCAmelCase_ = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
UpperCAmelCase_ = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
UpperCAmelCase_ = torch.tensor([[0.0027, 0.9904, 0.0070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
UpperCAmelCase_ = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
UpperCAmelCase_ = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(f"""Model name {model_name} not supported""" )
assert torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1e-3 )
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(lowerCAmelCase__ )
if push_to_hub:
print("Pushing model, processor and slow tokenizer files to the hub..." )
model.push_to_hub(lowerCAmelCase__ , organization="nielsr" )
processor.push_to_hub(lowerCAmelCase__ , organization="nielsr" )
slow_tokenizer.push_to_hub(lowerCAmelCase__ , organization="nielsr" )
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""xclip-base-patch32""",
type=str,
help="""Name of the model.""",
)
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."""
)
lowerCamelCase = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 241 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a :Dict = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[int] = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
a :Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 132 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE__ : Any = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["""scripts""", """external_deps""", """test_metrics.py"""] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
SCREAMING_SNAKE_CASE__ : Optional[int] = test_metrics
@require_cpu
def _a ( self ) -> List[Any]:
"""simple docstring"""
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def _a ( self ) -> List[str]:
"""simple docstring"""
debug_launcher(self.test_metrics.main )
@require_single_gpu
def _a ( self ) -> int:
"""simple docstring"""
self.test_metrics.main()
@require_multi_gpu
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
print(f'''Found {torch.cuda.device_count()} devices.''' )
SCREAMING_SNAKE_CASE__ : List[Any] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_a , env=os.environ.copy() )
| 132 | 1 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
snake_case_ = 'xlm-prophetnet'
snake_case_ = ['past_key_values']
snake_case_ = {
'num_attention_heads': 'num_encoder_attention_heads',
}
def __init__( self : Optional[int] , snake_case : Optional[float] = 0.1 , snake_case : Optional[Union[str, Callable]] = "gelu" , snake_case : Optional[int] = 3_0522 , snake_case : Optional[int] = 1024 , snake_case : Optional[int] = 4096 , snake_case : Optional[int] = 12 , snake_case : Optional[int] = 16 , snake_case : Optional[int] = 4096 , snake_case : Optional[int] = 12 , snake_case : Optional[int] = 16 , snake_case : Optional[float] = 0.1 , snake_case : Optional[float] = 0.1 , snake_case : Optional[int] = 512 , snake_case : Optional[float] = 0.02 , snake_case : Optional[bool] = True , snake_case : Optional[bool] = True , snake_case : Optional[int] = 0 , snake_case : Optional[int] = 2 , snake_case : Optional[int] = 32 , snake_case : Optional[int] = 128 , snake_case : Optional[bool] = False , snake_case : Optional[float] = 0.0 , snake_case : Optional[bool] = True , snake_case : Optional[int] = 0 , snake_case : Optional[int] = 1 , snake_case : Optional[int] = 2 , **snake_case : Optional[Any] , ):
'''simple docstring'''
A__ : Union[str, Any] = vocab_size
A__ : Dict = hidden_size
A__ : List[Any] = encoder_ffn_dim
A__ : int = num_encoder_layers
A__ : str = num_encoder_attention_heads
A__ : Tuple = decoder_ffn_dim
A__ : str = num_decoder_layers
A__ : Union[str, Any] = num_decoder_attention_heads
A__ : int = max_position_embeddings
A__ : str = init_std # Normal(0, this parameter)
A__ : Optional[int] = activation_function
# parameters for xlmprophetnet
A__ : List[str] = ngram
A__ : Tuple = num_buckets
A__ : List[Any] = relative_max_distance
A__ : Dict = disable_ngram_loss
A__ : Any = eps
# 3 Types of Dropout
A__ : str = attention_dropout
A__ : Any = activation_dropout
A__ : str = dropout
A__ : Optional[Any] = use_cache
super().__init__(
pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , is_encoder_decoder=snake_case , add_cross_attention=snake_case , decoder_start_token_id=snake_case , **snake_case , )
@property
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def _UpperCamelCase ( self : str , snake_case : Tuple ):
'''simple docstring'''
raise NotImplementedError(
"""This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
""" `num_decoder_layers`.""" )
| 362 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_ = '''src/diffusers'''
A_ = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
A_ = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
A_ = spec.loader.load_module()
def _lowerCAmelCase ( UpperCAmelCase__ : Optional[int], UpperCAmelCase__ : Optional[Any] ) ->Any:
return line.startswith(UpperCAmelCase__ ) or len(UpperCAmelCase__ ) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$""", UpperCAmelCase__ ) is not None
def _lowerCAmelCase ( UpperCAmelCase__ : List[str] ) ->Union[str, Any]:
A__ : Any = object_name.split(""".""" )
A__ : int = 0
# First let's find the module where our object lives.
A__ : str = parts[i]
while i < len(UpperCAmelCase__ ) and not os.path.isfile(os.path.join(UpperCAmelCase__, f'{module}.py' ) ):
i += 1
if i < len(UpperCAmelCase__ ):
A__ : Union[str, Any] = os.path.join(UpperCAmelCase__, parts[i] )
if i >= len(UpperCAmelCase__ ):
raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.' )
with open(os.path.join(UpperCAmelCase__, f'{module}.py' ), """r""", encoding="""utf-8""", newline="""\n""" ) as f:
A__ : List[Any] = f.readlines()
# Now let's find the class / func in the code!
A__ : Optional[Any] = """"""
A__ : Any = 0
for name in parts[i + 1 :]:
while (
line_index < len(UpperCAmelCase__ ) and re.search(Rf'^{indent}(class|def)\s+{name}(\(|\:)', lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(UpperCAmelCase__ ):
raise ValueError(f' {object_name} does not match any function or class in {module}.' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
A__ : List[Any] = line_index
while line_index < len(UpperCAmelCase__ ) and _should_continue(lines[line_index], UpperCAmelCase__ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
A__ : List[Any] = lines[start_index:line_index]
return "".join(UpperCAmelCase__ )
A_ = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
A_ = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
A_ = re.compile(r'''<FILL\s+[^>]*>''')
def _lowerCAmelCase ( UpperCAmelCase__ : List[str] ) ->Optional[Any]:
A__ : Dict = code.split("""\n""" )
A__ : List[Any] = 0
while idx < len(UpperCAmelCase__ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(UpperCAmelCase__ ):
return re.search(R"""^(\s*)\S""", lines[idx] ).groups()[0]
return ""
def _lowerCAmelCase ( UpperCAmelCase__ : Optional[Any] ) ->int:
A__ : str = len(get_indent(UpperCAmelCase__ ) ) > 0
if has_indent:
A__ : Union[str, Any] = f'class Bla:\n{code}'
A__ : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa}, line_length=1_1_9, preview=UpperCAmelCase__ )
A__ : Tuple = black.format_str(UpperCAmelCase__, mode=UpperCAmelCase__ )
A__ , A__ : List[Any] = style_docstrings_in_code(UpperCAmelCase__ )
return result[len("""class Bla:\n""" ) :] if has_indent else result
def _lowerCAmelCase ( UpperCAmelCase__ : Any, UpperCAmelCase__ : Dict=False ) ->List[Any]:
with open(UpperCAmelCase__, """r""", encoding="""utf-8""", newline="""\n""" ) as f:
A__ : int = f.readlines()
A__ : Dict = []
A__ : List[str] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(UpperCAmelCase__ ):
A__ : Dict = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
A__ , A__ , A__ : Dict = search.groups()
A__ : Tuple = find_code_in_diffusers(UpperCAmelCase__ )
A__ : int = get_indent(UpperCAmelCase__ )
A__ : List[str] = line_index + 1 if indent == theoretical_indent else line_index + 2
A__ : Tuple = theoretical_indent
A__ : Optional[Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
A__ : Tuple = True
while line_index < len(UpperCAmelCase__ ) and should_continue:
line_index += 1
if line_index >= len(UpperCAmelCase__ ):
break
A__ : Optional[int] = lines[line_index]
A__ : Tuple = _should_continue(UpperCAmelCase__, UpperCAmelCase__ ) and re.search(f'^{indent}# End copy', UpperCAmelCase__ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
A__ : Dict = lines[start_index:line_index]
A__ : Tuple = """""".join(UpperCAmelCase__ )
# Remove any nested `Copied from` comments to avoid circular copies
A__ : Optional[int] = [line for line in theoretical_code.split("""\n""" ) if _re_copy_warning.search(UpperCAmelCase__ ) is None]
A__ : Optional[Any] = """\n""".join(UpperCAmelCase__ )
# Before comparing, use the `replace_pattern` on the original code.
if len(UpperCAmelCase__ ) > 0:
A__ : int = replace_pattern.replace("""with""", """""" ).split(""",""" )
A__ : List[Any] = [_re_replace_pattern.search(UpperCAmelCase__ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
A__ , A__ , A__ : Union[str, Any] = pattern.groups()
A__ : Union[str, Any] = re.sub(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ )
if option.strip() == "all-casing":
A__ : List[Any] = re.sub(obja.lower(), obja.lower(), UpperCAmelCase__ )
A__ : Tuple = re.sub(obja.upper(), obja.upper(), UpperCAmelCase__ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
A__ : Optional[int] = blackify(lines[start_index - 1] + theoretical_code )
A__ : List[Any] = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
A__ : List[Any] = lines[:start_index] + [theoretical_code] + lines[line_index:]
A__ : Tuple = start_index + 1
if overwrite and len(UpperCAmelCase__ ) > 0:
# Warn the user a file has been modified.
print(f'Detected changes, rewriting {filename}.' )
with open(UpperCAmelCase__, """w""", encoding="""utf-8""", newline="""\n""" ) as f:
f.writelines(UpperCAmelCase__ )
return diffs
def _lowerCAmelCase ( UpperCAmelCase__ : bool = False ) ->Any:
A__ : Dict = glob.glob(os.path.join(UpperCAmelCase__, """**/*.py""" ), recursive=UpperCAmelCase__ )
A__ : str = []
for filename in all_files:
A__ : Any = is_copy_consistent(UpperCAmelCase__, UpperCAmelCase__ )
diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
if not overwrite and len(UpperCAmelCase__ ) > 0:
A__ : Any = """\n""".join(UpperCAmelCase__ )
raise Exception(
"""Found the following copy inconsistencies:\n"""
+ diff
+ """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.""" )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
A_ = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 296 | 0 |
def lowerCamelCase__ ( snake_case_ : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
__snake_case = set()
# Replace all the whitespace in our sentence
__snake_case = input_str.replace(''' ''' , '''''' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(snake_case_ ) == 26
def lowerCamelCase__ ( snake_case_ : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
__snake_case = [False] * 26
for char in input_str:
if char.islower():
__snake_case = True
elif char.isupper():
__snake_case = True
return all(snake_case_ )
def lowerCamelCase__ ( snake_case_ : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def lowerCamelCase__ ( ) -> None:
from timeit import timeit
__snake_case = '''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
print(timeit('''is_pangram()''' , setup=snake_case_ ) )
print(timeit('''is_pangram_faster()''' , setup=snake_case_ ) )
print(timeit('''is_pangram_fastest()''' , setup=snake_case_ ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24 |
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[str] = name
SCREAMING_SNAKE_CASE_: Union[str, Any] = val
def __str__( self : Dict):
return F"{self.__class__.__name__}({self.name}, {self.val})"
def __lt__( self : List[str] , lowerCAmelCase__ : Any):
return self.val < other.val
class __lowercase :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Dict):
SCREAMING_SNAKE_CASE_: str = {}
SCREAMING_SNAKE_CASE_: int = {}
SCREAMING_SNAKE_CASE_: Any = self.build_heap(lowerCAmelCase__)
def __getitem__( self : List[Any] , lowerCAmelCase__ : Dict):
return self.get_value(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : Dict):
return (idx - 1) // 2
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase__ : Optional[Any]):
return idx * 2 + 1
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : Tuple):
return idx * 2 + 2
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : Optional[int]):
return self.heap_dict[key]
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ : Union[str, Any]):
SCREAMING_SNAKE_CASE_: Tuple = len(lowerCAmelCase__) - 1
SCREAMING_SNAKE_CASE_: List[str] = self.get_parent_idx(lowerCAmelCase__)
for idx, i in enumerate(lowerCAmelCase__):
SCREAMING_SNAKE_CASE_: Union[str, Any] = idx
SCREAMING_SNAKE_CASE_: str = i.val
for i in range(lowerCAmelCase__ , -1 , -1):
self.sift_down(lowerCAmelCase__ , lowerCAmelCase__)
return array
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str]):
while True:
SCREAMING_SNAKE_CASE_: Optional[Any] = self.get_left_child_idx(lowerCAmelCase__) # noqa: E741
SCREAMING_SNAKE_CASE_: Dict = self.get_right_child_idx(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = idx
if l < len(lowerCAmelCase__) and array[l] < array[idx]:
SCREAMING_SNAKE_CASE_: List[str] = l
if r < len(lowerCAmelCase__) and array[r] < array[smallest]:
SCREAMING_SNAKE_CASE_: str = r
if smallest != idx:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Any = array[smallest], array[idx]
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
): Optional[Any] = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
SCREAMING_SNAKE_CASE_: Optional[int] = smallest
else:
break
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : str):
SCREAMING_SNAKE_CASE_: Any = self.get_parent_idx(lowerCAmelCase__)
while p >= 0 and self.heap[p] > self.heap[idx]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] = self.heap[idx], self.heap[p]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
SCREAMING_SNAKE_CASE_: Union[str, Any] = p
SCREAMING_SNAKE_CASE_: Optional[int] = self.get_parent_idx(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
return self.heap[0]
def _SCREAMING_SNAKE_CASE ( self : Dict):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple = self.heap[-1], self.heap[0]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
SCREAMING_SNAKE_CASE_: int = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap)
return x
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase__ : Tuple):
self.heap.append(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = len(self.heap) - 1
SCREAMING_SNAKE_CASE_: List[str] = node.val
self.sift_up(len(self.heap) - 1)
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
return len(self.heap) == 0
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int]):
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
SCREAMING_SNAKE_CASE_: Any = new_value
SCREAMING_SNAKE_CASE_: Tuple = new_value
self.sift_up(self.idx_of_element[node])
lowerCAmelCase : int = Node("""R""", -1)
lowerCAmelCase : str = Node("""B""", 6)
lowerCAmelCase : str = Node("""A""", 3)
lowerCAmelCase : List[str] = Node("""X""", 1)
lowerCAmelCase : Union[str, Any] = Node("""E""", 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
lowerCAmelCase : Optional[Any] = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
print(i)
print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : int = {
"""facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : int = 'wav2vec2'
def __init__(self , __lowercase=32 , __lowercase=7_68 , __lowercase=12 , __lowercase=12 , __lowercase=30_72 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.0_2 , __lowercase=1e-5 , __lowercase="group" , __lowercase="gelu" , __lowercase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __lowercase=(5, 2, 2, 2, 2, 2, 2) , __lowercase=(10, 3, 3, 3, 3, 2, 2) , __lowercase=False , __lowercase=1_28 , __lowercase=16 , __lowercase=False , __lowercase=True , __lowercase=0.0_5 , __lowercase=10 , __lowercase=2 , __lowercase=0.0 , __lowercase=10 , __lowercase=0 , __lowercase=3_20 , __lowercase=2 , __lowercase=0.1 , __lowercase=1_00 , __lowercase=2_56 , __lowercase=2_56 , __lowercase=0.1 , __lowercase="sum" , __lowercase=False , __lowercase=False , __lowercase=2_56 , __lowercase=(5_12, 5_12, 5_12, 5_12, 15_00) , __lowercase=(5, 3, 3, 1, 1) , __lowercase=(1, 2, 3, 1, 1) , __lowercase=5_12 , __lowercase=0 , __lowercase=1 , __lowercase=2 , __lowercase=False , __lowercase=3 , __lowercase=2 , __lowercase=3 , __lowercase=None , __lowercase=None , **__lowercase , ):
super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase )
__lowerCAmelCase = hidden_size
__lowerCAmelCase = feat_extract_norm
__lowerCAmelCase = feat_extract_activation
__lowerCAmelCase = list(__lowercase )
__lowerCAmelCase = list(__lowercase )
__lowerCAmelCase = list(__lowercase )
__lowerCAmelCase = conv_bias
__lowerCAmelCase = num_conv_pos_embeddings
__lowerCAmelCase = num_conv_pos_embedding_groups
__lowerCAmelCase = len(self.conv_dim )
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = hidden_dropout
__lowerCAmelCase = attention_dropout
__lowerCAmelCase = activation_dropout
__lowerCAmelCase = feat_proj_dropout
__lowerCAmelCase = final_dropout
__lowerCAmelCase = layerdrop
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = initializer_range
__lowerCAmelCase = vocab_size
__lowerCAmelCase = do_stable_layer_norm
__lowerCAmelCase = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCAmelCase = apply_spec_augment
__lowerCAmelCase = mask_time_prob
__lowerCAmelCase = mask_time_length
__lowerCAmelCase = mask_time_min_masks
__lowerCAmelCase = mask_feature_prob
__lowerCAmelCase = mask_feature_length
__lowerCAmelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__lowerCAmelCase = num_codevectors_per_group
__lowerCAmelCase = num_codevector_groups
__lowerCAmelCase = contrastive_logits_temperature
__lowerCAmelCase = feat_quantizer_dropout
__lowerCAmelCase = num_negatives
__lowerCAmelCase = codevector_dim
__lowerCAmelCase = proj_codevector_dim
__lowerCAmelCase = diversity_loss_weight
# ctc loss
__lowerCAmelCase = ctc_loss_reduction
__lowerCAmelCase = ctc_zero_infinity
# adapter
__lowerCAmelCase = add_adapter
__lowerCAmelCase = adapter_kernel_size
__lowerCAmelCase = adapter_stride
__lowerCAmelCase = num_adapter_layers
__lowerCAmelCase = output_hidden_size or hidden_size
__lowerCAmelCase = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowerCAmelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowerCAmelCase = list(__lowercase )
__lowerCAmelCase = list(__lowercase )
__lowerCAmelCase = list(__lowercase )
__lowerCAmelCase = xvector_output_dim
@property
def _snake_case (self ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 9 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __magic_name__( lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = old_name
if "patch_embed" in old_name:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = old_name.split('''.''')
if layer == "0":
__lowerCAmelCase = old_name.replace('''0''', '''convolution1''')
elif layer == "1":
__lowerCAmelCase = old_name.replace('''1''', '''batchnorm_before''')
elif layer == "3":
__lowerCAmelCase = old_name.replace('''3''', '''convolution2''')
else:
__lowerCAmelCase = old_name.replace('''4''', '''batchnorm_after''')
if "network" in old_name and re.search(r'''\d\.\d''', lowerCamelCase):
__lowerCAmelCase = r'''\b\d{2}\b'''
if bool(re.search(lowerCamelCase, lowerCamelCase)):
__lowerCAmelCase = re.search(r'''\d\.\d\d.''', lowerCamelCase).group()
else:
__lowerCAmelCase = re.search(r'''\d\.\d.''', lowerCamelCase).group()
if int(match[0]) < 6:
__lowerCAmelCase = old_name.replace(lowerCamelCase, '''''')
__lowerCAmelCase = trimmed_name.replace('''network''', match[0] + '''.meta4D_layers.blocks.''' + match[2:-1])
__lowerCAmelCase = '''intermediate_stages.''' + trimmed_name
else:
__lowerCAmelCase = old_name.replace(lowerCamelCase, '''''')
if int(match[2]) < num_meta4D_last_stage:
__lowerCAmelCase = trimmed_name.replace('''network''', '''meta4D_layers.blocks.''' + match[2])
else:
__lowerCAmelCase = str(int(match[2]) - num_meta4D_last_stage)
__lowerCAmelCase = trimmed_name.replace('''network''', '''meta3D_layers.blocks.''' + layer_index)
if "norm1" in old_name:
__lowerCAmelCase = trimmed_name.replace('''norm1''', '''layernorm1''')
elif "norm2" in old_name:
__lowerCAmelCase = trimmed_name.replace('''norm2''', '''layernorm2''')
elif "fc1" in old_name:
__lowerCAmelCase = trimmed_name.replace('''fc1''', '''linear_in''')
elif "fc2" in old_name:
__lowerCAmelCase = trimmed_name.replace('''fc2''', '''linear_out''')
__lowerCAmelCase = '''last_stage.''' + trimmed_name
elif "network" in old_name and re.search(r'''.\d.''', lowerCamelCase):
__lowerCAmelCase = old_name.replace('''network''', '''intermediate_stages''')
if "fc" in new_name:
__lowerCAmelCase = new_name.replace('''fc''', '''convolution''')
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
__lowerCAmelCase = new_name.replace('''norm1''', '''batchnorm_before''')
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
__lowerCAmelCase = new_name.replace('''norm2''', '''batchnorm_after''')
if "proj" in new_name:
__lowerCAmelCase = new_name.replace('''proj''', '''projection''')
if "dist_head" in new_name:
__lowerCAmelCase = new_name.replace('''dist_head''', '''distillation_classifier''')
elif "head" in new_name:
__lowerCAmelCase = new_name.replace('''head''', '''classifier''')
elif "patch_embed" in new_name:
__lowerCAmelCase = '''efficientformer.''' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
__lowerCAmelCase = new_name.replace('''norm''', '''layernorm''')
__lowerCAmelCase = '''efficientformer.''' + new_name
else:
__lowerCAmelCase = '''efficientformer.encoder.''' + new_name
return new_name
def __magic_name__( lowerCamelCase, lowerCamelCase):
for key in checkpoint.copy().keys():
__lowerCAmelCase = checkpoint.pop(lowerCamelCase)
__lowerCAmelCase = val
return checkpoint
def __magic_name__( ):
__lowerCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__lowerCAmelCase = Image.open(requests.get(lowerCamelCase, stream=lowerCamelCase).raw)
return image
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = torch.load(lowerCamelCase, map_location='''cpu''')['''model''']
__lowerCAmelCase = EfficientFormerConfig.from_json_file(lowerCamelCase)
__lowerCAmelCase = EfficientFormerForImageClassificationWithTeacher(lowerCamelCase)
__lowerCAmelCase = '''_'''.join(checkpoint_path.split('''/''')[-1].split('''.''')[0].split('''_''')[:-1])
__lowerCAmelCase = config.depths[-1] - config.num_metaad_blocks + 1
__lowerCAmelCase = convert_torch_checkpoint(lowerCamelCase, lowerCamelCase)
model.load_state_dict(lowerCamelCase)
model.eval()
__lowerCAmelCase = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
# prepare image
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = 2_5_6
__lowerCAmelCase = 2_2_4
__lowerCAmelCase = EfficientFormerImageProcessor(
size={'''shortest_edge''': image_size}, crop_size={'''height''': crop_size, '''width''': crop_size}, resample=pillow_resamplings['''bicubic'''], )
__lowerCAmelCase = processor(images=lowerCamelCase, return_tensors='''pt''').pixel_values
# original processing pipeline
__lowerCAmelCase = Compose(
[
Resize(lowerCamelCase, interpolation=pillow_resamplings['''bicubic''']),
CenterCrop(lowerCamelCase),
ToTensor(),
Normalize(lowerCamelCase, lowerCamelCase),
])
__lowerCAmelCase = image_transforms(lowerCamelCase).unsqueeze(0)
assert torch.allclose(lowerCamelCase, lowerCamelCase)
__lowerCAmelCase = model(lowerCamelCase)
__lowerCAmelCase = outputs.logits
__lowerCAmelCase = (1, 1_0_0_0)
if "l1" in model_name:
__lowerCAmelCase = torch.Tensor(
[-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28])
assert torch.allclose(logits[0, :1_0], lowerCamelCase, atol=1E-3)
assert logits.shape == expected_shape
elif "l3" in model_name:
__lowerCAmelCase = torch.Tensor(
[-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27])
assert torch.allclose(logits[0, :1_0], lowerCamelCase, atol=1E-3)
assert logits.shape == expected_shape
elif "l7" in model_name:
__lowerCAmelCase = torch.Tensor(
[-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78])
assert logits.shape == expected_shape
else:
raise ValueError(
F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7""")
# Save Checkpoints
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
model.save_pretrained(lowerCamelCase)
print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""")
processor.save_pretrained(lowerCamelCase)
print(F"""Processor successfuly saved at {pytorch_dump_path}""")
if push_to_hub:
print('''Pushing model to the hub...''')
model.push_to_hub(
repo_id=F"""Bearnardd/{pytorch_dump_path}""", commit_message='''Add model''', use_temp_dir=lowerCamelCase, )
processor.push_to_hub(
repo_id=F"""Bearnardd/{pytorch_dump_path}""", commit_message='''Add image processor''', use_temp_dir=lowerCamelCase, )
if __name__ == "__main__":
_UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--pytorch_model_path""",
default=None,
type=str,
required=True,
help="""Path to EfficientFormer pytorch checkpoint.""",
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The json file for EfficientFormer model config.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
parser.add_argument(
"""--no-push_to_hub""",
dest="""push_to_hub""",
action="""store_false""",
help="""Do not push model and image processor to the hub""",
)
parser.set_defaults(push_to_hub=True)
_UpperCAmelCase : List[str] = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 9 | 1 |
'''simple docstring'''
from torch import nn
def __UpperCamelCase ( UpperCAmelCase ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {act_fn}""" )
| 198 | '''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__a: Optional[int] = 4
__a: Optional[Any] = 3
class UpperCAmelCase ( a__ ):
'''simple docstring'''
pass
def __UpperCamelCase ( UpperCAmelCase ):
for shard in shards:
for i in range(UpperCAmelCase ):
yield {"i": i, "shard": shard}
def __UpperCamelCase ( ):
lowercase__ : Tuple = int(os.environ['''RANK'''] )
lowercase__ : List[str] = int(os.environ['''WORLD_SIZE'''] )
lowercase__ : Optional[Any] = ArgumentParser()
parser.add_argument('''--streaming''' , type=UpperCAmelCase )
parser.add_argument('''--local_rank''' , type=UpperCAmelCase )
parser.add_argument('''--num_workers''' , type=UpperCAmelCase , default=0 )
lowercase__ : List[Any] = parser.parse_args()
lowercase__ : List[str] = args.streaming
lowercase__ : str = args.num_workers
lowercase__ : Optional[int] = {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(UpperCAmelCase )]}
lowercase__ : Tuple = IterableDataset.from_generator(UpperCAmelCase , gen_kwargs=UpperCAmelCase )
if not streaming:
lowercase__ : int = Dataset.from_list(list(UpperCAmelCase ) )
lowercase__ : str = split_dataset_by_node(UpperCAmelCase , rank=UpperCAmelCase , world_size=UpperCAmelCase )
lowercase__ : Optional[int] = torch.utils.data.DataLoader(UpperCAmelCase , num_workers=UpperCAmelCase )
lowercase__ : Dict = NUM_SHARDS * NUM_ITEMS_PER_SHARD
lowercase__ : Optional[Any] = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
lowercase__ : Union[str, Any] = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}""" )
if __name__ == "__main__":
main()
| 198 | 1 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowercase__ = 299792458
# Symbols
lowercase__ , lowercase__ , lowercase__ , lowercase__ = symbols("""ct x y z""")
def __lowerCamelCase ( __UpperCamelCase ) -> float:
"""simple docstring"""
if velocity > c:
raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("Speed must be greater than or equal to 1!" )
return velocity / c
def __lowerCamelCase ( __UpperCamelCase ) -> float:
"""simple docstring"""
return 1 / sqrt(1 - beta(__UpperCamelCase ) ** 2 )
def __lowerCamelCase ( __UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
return np.array(
[
[gamma(__UpperCamelCase ), -gamma(__UpperCamelCase ) * beta(__UpperCamelCase ), 0, 0],
[-gamma(__UpperCamelCase ) * beta(__UpperCamelCase ), gamma(__UpperCamelCase ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase = None ) -> np.ndarray:
"""simple docstring"""
if event is None:
lowerCAmelCase_ : int = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(__UpperCamelCase ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
lowercase__ = transform(29979245)
print("""Example of four vector: """)
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
lowercase__ = {ct: c, x: 1, y: 1, z: 1}
lowercase__ = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""")
| 161 |
"""simple docstring"""
from __future__ import annotations
import math
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Dict , a_ : int ):
lowerCAmelCase_ : Union[str, Any] = size
# approximate the overall size of segment tree with given value
lowerCAmelCase_ : Union[str, Any] = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
lowerCAmelCase_ : int = [0 for i in range(0 , 4 * size )]
lowerCAmelCase_ : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowerCamelCase ( self : List[Any] , a_ : int ):
return idx * 2
def lowerCamelCase ( self : Tuple , a_ : int ):
return idx * 2 + 1
def lowerCamelCase ( self : Tuple , a_ : int , a_ : int , a_ : int , a_ : list[int] ):
if left_element == right_element:
lowerCAmelCase_ : Tuple = a[left_element - 1]
else:
lowerCAmelCase_ : Tuple = (left_element + right_element) // 2
self.build(self.left(a_ ) , a_ , a_ , a_ )
self.build(self.right(a_ ) , mid + 1 , a_ , a_ )
lowerCAmelCase_ : int = max(
self.segment_tree[self.left(a_ )] , self.segment_tree[self.right(a_ )] )
def lowerCamelCase ( self : Union[str, Any] , a_ : int , a_ : int , a_ : int , a_ : int , a_ : int , a_ : int ):
if self.flag[idx] is True:
lowerCAmelCase_ : Dict = self.lazy[idx]
lowerCAmelCase_ : Optional[Any] = False
if left_element != right_element:
lowerCAmelCase_ : str = self.lazy[idx]
lowerCAmelCase_ : Dict = self.lazy[idx]
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : Union[str, Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
lowerCAmelCase_ : Dict = val
if left_element != right_element:
lowerCAmelCase_ : Union[str, Any] = val
lowerCAmelCase_ : Dict = val
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : List[str] = True
return True
lowerCAmelCase_ : Optional[Any] = (left_element + right_element) // 2
self.update(self.left(a_ ) , a_ , a_ , a_ , a_ , a_ )
self.update(self.right(a_ ) , mid + 1 , a_ , a_ , a_ , a_ )
lowerCAmelCase_ : int = max(
self.segment_tree[self.left(a_ )] , self.segment_tree[self.right(a_ )] )
return True
def lowerCamelCase ( self : int , a_ : int , a_ : int , a_ : int , a_ : int , a_ : int ):
if self.flag[idx] is True:
lowerCAmelCase_ : Union[str, Any] = self.lazy[idx]
lowerCAmelCase_ : Optional[int] = False
if left_element != right_element:
lowerCAmelCase_ : int = self.lazy[idx]
lowerCAmelCase_ : int = self.lazy[idx]
lowerCAmelCase_ : Optional[Any] = True
lowerCAmelCase_ : Dict = 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_ : Union[str, Any] = self.query(self.left(a_ ) , a_ , a_ , a_ , a_ )
lowerCAmelCase_ : List[str] = self.query(self.right(a_ ) , mid + 1 , a_ , a_ , a_ )
return max(a_ , a_ )
def __str__( self : str ):
return str([self.query(1 , 1 , self.size , a_ , a_ ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
lowercase__ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
lowercase__ = 15
lowercase__ = 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)
| 161 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowercase__ = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
lowercase__ = """\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""
lowercase__ = """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
'google_bleu': google_bleu score
Examples:
Example 1:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.44
Example 2:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.61
Example 3:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results[\"google_bleu\"], 2))
0.53
Example 4:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results[\"google_bleu\"], 2))
0.4
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCamelCase ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase ( self : Union[str, Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , )
def lowerCamelCase ( self : int , a_ : List[List[List[str]]] , a_ : List[List[str]] , a_ : int = 1 , a_ : int = 4 , ):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=a_ , hypotheses=a_ , min_len=a_ , max_len=a_ )
}
| 241 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
lowercase__ = TypeVar("""_T""")
class __lowerCamelCase ( Generic[_T] ):
'''simple docstring'''
def __init__( self : Optional[int] , a_ : Iterable[_T] | None = None ):
lowerCAmelCase_ : list[_T] = list(iterable or [] )
lowerCAmelCase_ : list[_T] = []
def __len__( self : str ):
return len(self._stacka ) + len(self._stacka )
def __repr__( self : List[Any] ):
return f'''Queue({tuple(self._stacka[::-1] + self._stacka )})'''
def lowerCamelCase ( self : List[str] , a_ : _T ):
self._stacka.append(a_ )
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : int = self._stacka.pop
lowerCAmelCase_ : Any = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("Queue is empty" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 241 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 183 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 183 | 1 |
from __future__ import annotations
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = str(_SCREAMING_SNAKE_CASE )
return len(_SCREAMING_SNAKE_CASE ) == 9 and set(_SCREAMING_SNAKE_CASE ) == set('''123456789''' )
def _lowercase ( ):
for base_num in range(9_9_9_9 , 4_9_9_9 , -1 ):
__lowerCAmelCase : Any = 1_0_0_0_0_2 * base_num
if is_9_pandigital(_SCREAMING_SNAKE_CASE ):
return candidate
for base_num in range(3_3_3 , 9_9 , -1 ):
__lowerCAmelCase : Dict = 1_0_0_2_0_0_3 * base_num
if is_9_pandigital(_SCREAMING_SNAKE_CASE ):
return candidate
return None
if __name__ == "__main__":
print(F"{solution() = }")
| 275 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
SCREAMING_SNAKE_CASE_ = random.Random()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : Optional[Any]=400 ,lowerCamelCase__ : List[str]=2000 ,lowerCamelCase__ : List[str]=2048 ,lowerCamelCase__ : Any=128 ,lowerCamelCase__ : List[str]=1 ,lowerCamelCase__ : str=512 ,lowerCamelCase__ : Optional[Any]=30 ,lowerCamelCase__ : Tuple=44100 ,) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = spectrogram_length
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = num_audio_channels
SCREAMING_SNAKE_CASE = hop_length
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = sampling_rate
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Union[str, Any]=False ) -> str:
'''simple docstring'''
def _flatten(lowerCamelCase__ : List[Any] ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
SCREAMING_SNAKE_CASE = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = TvltFeatureExtractor
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TvltFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase__ ,"""spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""sampling_rate""" ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
check_json_file_has_correct_format(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE = feature_extractor(np_speech_inputs[0] ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
SCREAMING_SNAKE_CASE = feature_extractor(
lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ,mask_audio=lowerCamelCase__ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape ,(1, 1, 192, 128) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = ['''image_processor''']
__lowerCAmelCase = '''SamImageProcessor'''
def __init__(self : Optional[Any] , _lowerCAmelCase : Dict ):
super().__init__(_lowerCAmelCase )
A = self.image_processor
A = -10
A = self.image_processor.size["""longest_edge"""]
def __call__(self : Optional[Any] , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Any=None , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Optional[Union[str, TensorType]] = None , **_lowerCAmelCase : Union[str, Any] , ):
A = self.image_processor(
_lowerCAmelCase , return_tensors=_lowerCAmelCase , **_lowerCAmelCase , )
# pop arguments that are not used in the foward but used nevertheless
A = encoding_image_processor["""original_sizes"""]
if hasattr(_lowerCAmelCase , """numpy""" ): # Checks if Torch or TF tensor
A = original_sizes.numpy()
A , A , A = self._check_and_preprocess_points(
input_points=_lowerCAmelCase , input_labels=_lowerCAmelCase , input_boxes=_lowerCAmelCase , )
A = self._normalize_and_convert(
_lowerCAmelCase , _lowerCAmelCase , input_points=_lowerCAmelCase , input_labels=_lowerCAmelCase , input_boxes=_lowerCAmelCase , return_tensors=_lowerCAmelCase , )
return encoding_image_processor
def A (self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] , _lowerCAmelCase : str=None , _lowerCAmelCase : str=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : List[str]="pt" , ):
if input_points is not None:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
A = [
self._normalize_coordinates(self.target_size , _lowerCAmelCase , original_sizes[0] ) for point in input_points
]
else:
A = [
self._normalize_coordinates(self.target_size , _lowerCAmelCase , _lowerCAmelCase )
for point, original_size in zip(_lowerCAmelCase , _lowerCAmelCase )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
A , A = self._pad_points_and_labels(_lowerCAmelCase , _lowerCAmelCase )
A = np.array(_lowerCAmelCase )
if input_labels is not None:
A = np.array(_lowerCAmelCase )
if input_boxes is not None:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
A = [
self._normalize_coordinates(self.target_size , _lowerCAmelCase , original_sizes[0] , is_bounding_box=_lowerCAmelCase )
for box in input_boxes
]
else:
A = [
self._normalize_coordinates(self.target_size , _lowerCAmelCase , _lowerCAmelCase , is_bounding_box=_lowerCAmelCase )
for box, original_size in zip(_lowerCAmelCase , _lowerCAmelCase )
]
A = np.array(_lowerCAmelCase )
if input_boxes is not None:
if return_tensors == "pt":
A = torch.from_numpy(_lowerCAmelCase )
# boxes batch size of 1 by default
A = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
A = tf.convert_to_tensor(_lowerCAmelCase )
# boxes batch size of 1 by default
A = tf.expand_dims(_lowerCAmelCase , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"""input_boxes""": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
A = torch.from_numpy(_lowerCAmelCase )
# point batch size of 1 by default
A = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
A = tf.convert_to_tensor(_lowerCAmelCase )
# point batch size of 1 by default
A = tf.expand_dims(_lowerCAmelCase , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"""input_points""": input_points} )
if input_labels is not None:
if return_tensors == "pt":
A = torch.from_numpy(_lowerCAmelCase )
# point batch size of 1 by default
A = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
A = tf.convert_to_tensor(_lowerCAmelCase )
# point batch size of 1 by default
A = tf.expand_dims(_lowerCAmelCase , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"""input_labels""": input_labels} )
return encoding_image_processor
def A (self : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] ):
A = max([point.shape[0] for point in input_points] )
A = []
for i, point in enumerate(_lowerCAmelCase ):
if point.shape[0] != expected_nb_points:
A = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
A = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(_lowerCAmelCase )
A = processed_input_points
return input_points, input_labels
def A (self : Dict , _lowerCAmelCase : int , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : Tuple , _lowerCAmelCase : Dict=False ):
A , A = original_size
A , A = self.image_processor._get_preprocess_shape(_lowerCAmelCase , longest_edge=_lowerCAmelCase )
A = deepcopy(_lowerCAmelCase ).astype(_lowerCAmelCase )
if is_bounding_box:
A = coords.reshape(-1 , 2 , 2 )
A = coords[..., 0] * (new_w / old_w)
A = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
A = coords.reshape(-1 , 4 )
return coords
def A (self : Any , _lowerCAmelCase : Optional[Any]=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Dict=None , ):
if input_points is not None:
if hasattr(_lowerCAmelCase , """numpy""" ): # Checks for TF or Torch tensor
A = input_points.numpy().tolist()
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not isinstance(input_points[0] , _lowerCAmelCase ):
raise ValueError("""Input points must be a list of list of floating points.""" )
A = [np.array(_lowerCAmelCase ) for input_point in input_points]
else:
A = None
if input_labels is not None:
if hasattr(_lowerCAmelCase , """numpy""" ):
A = input_labels.numpy().tolist()
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not isinstance(input_labels[0] , _lowerCAmelCase ):
raise ValueError("""Input labels must be a list of list integers.""" )
A = [np.array(_lowerCAmelCase ) for label in input_labels]
else:
A = None
if input_boxes is not None:
if hasattr(_lowerCAmelCase , """numpy""" ):
A = input_boxes.numpy().tolist()
if (
not isinstance(_lowerCAmelCase , _lowerCAmelCase )
or not isinstance(input_boxes[0] , _lowerCAmelCase )
or not isinstance(input_boxes[0][0] , _lowerCAmelCase )
):
raise ValueError("""Input boxes must be a list of list of list of floating points.""" )
A = [np.array(_lowerCAmelCase ).astype(np.floataa ) for box in input_boxes]
else:
A = None
return input_points, input_labels, input_boxes
@property
def A (self : Tuple ):
A = self.image_processor.model_input_names
return list(dict.fromkeys(_lowerCAmelCase ) )
def A (self : Tuple , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Any ):
return self.image_processor.post_process_masks(*_lowerCAmelCase , **_lowerCAmelCase )
| 337 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_lowerCamelCase : Any = {
# 1536-bit
5: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
base=16,
),
'generator': 2,
},
# 2048-bit
14: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
base=16,
),
'generator': 2,
},
# 3072-bit
15: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
base=16,
),
'generator': 2,
},
# 4096-bit
16: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
+ '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
+ '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
+ '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
+ '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
+ '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
+ 'FFFFFFFFFFFFFFFF',
base=16,
),
'generator': 2,
},
# 6144-bit
17: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
+ '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
+ '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
+ 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
+ '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
+ 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
+ '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
+ '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
+ '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
+ 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
+ '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
+ 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
+ '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
+ '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
+ '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
+ 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
+ '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
+ 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
+ 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
+ '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
+ 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
+ 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
+ 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
+ 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
+ '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
+ '6DCC4024FFFFFFFFFFFFFFFF',
base=16,
),
'generator': 2,
},
# 8192-bit
18: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
+ '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
+ '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
+ '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
+ '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
+ '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
+ '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
+ 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
+ '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
+ 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
+ '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
+ 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
+ '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
+ 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
+ '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
+ 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
+ '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
+ '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
+ '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
+ '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
+ '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
+ '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
+ '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
+ '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
+ 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
+ '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
+ '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
+ '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
base=16,
),
'generator': 2,
},
}
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : int , _lowerCAmelCase : int = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
A = primes[group]["""prime"""]
A = primes[group]["""generator"""]
A = int(hexlify(urandom(32 ) ) , base=16 )
def A (self : Optional[Any] ):
return hex(self.__private_key )[2:]
def A (self : Union[str, Any] ):
A = pow(self.generator , self.__private_key , self.prime )
return hex(_lowerCAmelCase )[2:]
def A (self : Any , _lowerCAmelCase : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(_lowerCAmelCase , (self.prime - 1) // 2 , self.prime ) == 1
)
def A (self : List[str] , _lowerCAmelCase : str ):
A = int(_lowerCAmelCase , base=16 )
if not self.is_valid_public_key(_lowerCAmelCase ):
raise ValueError("""Invalid public key""" )
A = pow(_lowerCAmelCase , self.__private_key , self.prime )
return shaaaa(str(_lowerCAmelCase ).encode() ).hexdigest()
@staticmethod
def A (_lowerCAmelCase : int , _lowerCAmelCase : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(_lowerCAmelCase , (prime - 1) // 2 , _lowerCAmelCase ) == 1
)
@staticmethod
def A (_lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : int = 14 ):
A = int(_lowerCAmelCase , base=16 )
A = int(_lowerCAmelCase , base=16 )
A = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""Invalid public key""" )
A = pow(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return shaaaa(str(_lowerCAmelCase ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 337 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] =logging.get_logger(__name__)
__lowerCAmelCase : List[str] ={
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = '''wav2vec2'''
def __init__( self :Any , lowerCAmelCase__ :Optional[Any]=32 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :List[str]=12 , lowerCAmelCase__ :Any=3_072 , lowerCAmelCase__ :Dict="gelu" , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :List[str]=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=0.0 , lowerCAmelCase__ :Union[str, Any]=0.0 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :List[Any]=0.02 , lowerCAmelCase__ :Optional[int]=1E-5 , lowerCAmelCase__ :Tuple="group" , lowerCAmelCase__ :Optional[int]="gelu" , lowerCAmelCase__ :Dict=(512, 512, 512, 512, 512, 512, 512) , lowerCAmelCase__ :Any=(5, 2, 2, 2, 2, 2, 2) , lowerCAmelCase__ :Optional[int]=(10, 3, 3, 3, 3, 2, 2) , lowerCAmelCase__ :List[Any]=False , lowerCAmelCase__ :Dict=128 , lowerCAmelCase__ :List[Any]=16 , lowerCAmelCase__ :int=False , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=0.05 , lowerCAmelCase__ :Any=10 , lowerCAmelCase__ :Dict=2 , lowerCAmelCase__ :Any=0.0 , lowerCAmelCase__ :Optional[Any]=10 , lowerCAmelCase__ :List[Any]=0 , lowerCAmelCase__ :List[str]=320 , lowerCAmelCase__ :Union[str, Any]=2 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Union[str, Any]=100 , lowerCAmelCase__ :str=256 , lowerCAmelCase__ :List[str]=256 , lowerCAmelCase__ :Any=0.1 , lowerCAmelCase__ :Tuple="sum" , lowerCAmelCase__ :Dict=False , lowerCAmelCase__ :Dict=False , lowerCAmelCase__ :Union[str, Any]=256 , lowerCAmelCase__ :Optional[Any]=(512, 512, 512, 512, 1_500) , lowerCAmelCase__ :Optional[int]=(5, 3, 3, 1, 1) , lowerCAmelCase__ :Any=(1, 2, 3, 1, 1) , lowerCAmelCase__ :Union[str, Any]=512 , lowerCAmelCase__ :Any=0 , lowerCAmelCase__ :Optional[Any]=1 , lowerCAmelCase__ :Any=2 , lowerCAmelCase__ :int=False , lowerCAmelCase__ :str=3 , lowerCAmelCase__ :Tuple=2 , lowerCAmelCase__ :Dict=3 , lowerCAmelCase__ :int=None , lowerCAmelCase__ :Optional[int]=None , **lowerCAmelCase__ :Any , ) -> List[str]:
super().__init__(**lowerCAmelCase__ , pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = feat_extract_norm
__SCREAMING_SNAKE_CASE : List[str] = feat_extract_activation
__SCREAMING_SNAKE_CASE : Optional[int] = list(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = list(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = list(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = conv_bias
__SCREAMING_SNAKE_CASE : Any = num_conv_pos_embeddings
__SCREAMING_SNAKE_CASE : Tuple = num_conv_pos_embedding_groups
__SCREAMING_SNAKE_CASE : Optional[int] = len(self.conv_dim )
__SCREAMING_SNAKE_CASE : Tuple = num_hidden_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
__SCREAMING_SNAKE_CASE : Any = hidden_act
__SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
__SCREAMING_SNAKE_CASE : int = hidden_dropout
__SCREAMING_SNAKE_CASE : str = attention_dropout
__SCREAMING_SNAKE_CASE : Dict = activation_dropout
__SCREAMING_SNAKE_CASE : str = feat_proj_dropout
__SCREAMING_SNAKE_CASE : Dict = final_dropout
__SCREAMING_SNAKE_CASE : Optional[int] = layerdrop
__SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
__SCREAMING_SNAKE_CASE : str = initializer_range
__SCREAMING_SNAKE_CASE : Tuple = vocab_size
__SCREAMING_SNAKE_CASE : int = do_stable_layer_norm
__SCREAMING_SNAKE_CASE : Tuple = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__SCREAMING_SNAKE_CASE : Optional[Any] = apply_spec_augment
__SCREAMING_SNAKE_CASE : List[Any] = mask_time_prob
__SCREAMING_SNAKE_CASE : str = mask_time_length
__SCREAMING_SNAKE_CASE : Any = mask_time_min_masks
__SCREAMING_SNAKE_CASE : Optional[int] = mask_feature_prob
__SCREAMING_SNAKE_CASE : int = mask_feature_length
__SCREAMING_SNAKE_CASE : int = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__SCREAMING_SNAKE_CASE : Dict = num_codevectors_per_group
__SCREAMING_SNAKE_CASE : Optional[Any] = num_codevector_groups
__SCREAMING_SNAKE_CASE : Optional[int] = contrastive_logits_temperature
__SCREAMING_SNAKE_CASE : Any = feat_quantizer_dropout
__SCREAMING_SNAKE_CASE : List[Any] = num_negatives
__SCREAMING_SNAKE_CASE : Tuple = codevector_dim
__SCREAMING_SNAKE_CASE : List[str] = proj_codevector_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = diversity_loss_weight
# ctc loss
__SCREAMING_SNAKE_CASE : Tuple = ctc_loss_reduction
__SCREAMING_SNAKE_CASE : Optional[int] = ctc_zero_infinity
# adapter
__SCREAMING_SNAKE_CASE : str = add_adapter
__SCREAMING_SNAKE_CASE : Union[str, Any] = adapter_kernel_size
__SCREAMING_SNAKE_CASE : Any = adapter_stride
__SCREAMING_SNAKE_CASE : List[str] = num_adapter_layers
__SCREAMING_SNAKE_CASE : List[str] = output_hidden_size or hidden_size
__SCREAMING_SNAKE_CASE : int = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__SCREAMING_SNAKE_CASE : Union[str, Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__SCREAMING_SNAKE_CASE : Optional[int] = list(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = list(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = list(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = xvector_output_dim
@property
def __magic_name__( self :List[Any] ) -> Any:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 9 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self :Any , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tuple=7 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :Any=10 , lowerCAmelCase__ :Optional[int]=18 , lowerCAmelCase__ :Dict=30 , lowerCAmelCase__ :Tuple=400 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :List[str]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :List[str]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[Any]=None , ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE : Dict = size if size is not None else {'''shortest_edge''': 18}
__SCREAMING_SNAKE_CASE : Optional[int] = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
__SCREAMING_SNAKE_CASE : Tuple = parent
__SCREAMING_SNAKE_CASE : List[Any] = batch_size
__SCREAMING_SNAKE_CASE : List[str] = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = num_frames
__SCREAMING_SNAKE_CASE : Tuple = image_size
__SCREAMING_SNAKE_CASE : Optional[Any] = min_resolution
__SCREAMING_SNAKE_CASE : Any = max_resolution
__SCREAMING_SNAKE_CASE : List[Any] = do_resize
__SCREAMING_SNAKE_CASE : Optional[Any] = size
__SCREAMING_SNAKE_CASE : Optional[int] = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : List[str] = image_std
__SCREAMING_SNAKE_CASE : str = crop_size
def __magic_name__( self :Tuple ) -> Any:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = VivitImageProcessor if is_vision_available() else None
def __magic_name__( self :List[str] ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : str = VivitImageProcessingTester(self )
@property
def __magic_name__( self :int ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def __magic_name__( self :List[str] ) -> Optional[int]:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , '''image_mean''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''image_std''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''do_normalize''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''do_resize''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''size''' ) )
def __magic_name__( self :Optional[Any] ) -> Optional[int]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def __magic_name__( self :List[Any] ) -> Union[str, Any]:
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
__SCREAMING_SNAKE_CASE : List[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def __magic_name__( self :str ) -> int:
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : List[str] = prepare_video_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Any = image_processing(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def __magic_name__( self :Any ) -> List[str]:
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_video_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : Tuple = image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 9 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __magic_name__ :
__A : int
__A : Node | None
class __magic_name__ :
def __init__( self : Union[str, Any] , snake_case__ : Iterable[int] ):
'''simple docstring'''
lowercase :Node | None = None
for i in sorted(snake_case__ , reverse=snake_case__ ):
lowercase :List[str] = Node(snake_case__ , self.head )
def __iter__( self : Dict ):
'''simple docstring'''
lowercase :List[str] = self.head
while node:
yield node.data
lowercase :Optional[int] = node.next_node
def __len__( self : Dict ):
'''simple docstring'''
return sum(1 for _ in self )
def __str__( self : Any ):
'''simple docstring'''
return " -> ".join([str(snake_case__ ) for node in self] )
def lowerCamelCase (a_ :SortedLinkedList , a_ :SortedLinkedList) -> SortedLinkedList:
return SortedLinkedList(list(a_) + list(a_))
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 172 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = '''▁'''
UpperCAmelCase = {'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}
UpperCAmelCase = {
'''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
'''vocab_file''': '''vocab.txt''',
}
UpperCAmelCase = {
'''vocab_file''': {
'''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
'''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
},
'''sentencepiece_model_file''': {
'''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
'''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
},
}
UpperCAmelCase = {
'''ernie-m-base''': 514,
'''ernie-m-large''': 514,
}
UpperCAmelCase = {
'''ernie-m-base''': {'''do_lower_case''': False},
'''ernie-m-large''': {'''do_lower_case''': False},
}
class __magic_name__ ( __UpperCAmelCase ):
__A : List[str] = ["input_ids"]
__A : Optional[Any] = VOCAB_FILES_NAMES
__A : str = PRETRAINED_INIT_CONFIGURATION
__A : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : List[str] = PRETRAINED_VOCAB_FILES_MAP
__A : List[str] = RESOURCE_FILES_NAMES
def __init__( self : Dict , snake_case__ : List[Any] , snake_case__ : List[Any]=None , snake_case__ : int=False , snake_case__ : Optional[int]="utf8" , snake_case__ : List[str]="[UNK]" , snake_case__ : Tuple="[SEP]" , snake_case__ : List[Any]="[PAD]" , snake_case__ : Dict="[CLS]" , snake_case__ : Dict="[MASK]" , snake_case__ : Optional[Dict[str, Any]] = None , **snake_case__ : str , ):
'''simple docstring'''
lowercase :Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , vocab_file=snake_case__ , encoding=snake_case__ , sp_model_kwargs=self.sp_model_kwargs , **snake_case__ , )
lowercase :Dict = do_lower_case
lowercase :str = sentencepiece_model_ckpt
lowercase :Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case__ )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowercase :Tuple = self.load_vocab(filepath=snake_case__ )
else:
lowercase :str = {self.sp_model.id_to_piece(snake_case__ ): id for id in range(self.sp_model.get_piece_size() )}
lowercase :Any = {v: k for k, v in self.vocab.items()}
def __snake_case ( self : List[str] , snake_case__ : str ):
'''simple docstring'''
if text is None:
return None
lowercase :List[Any] = self.tokenize(snake_case__ )
lowercase , lowercase :List[str] = '''''', []
for i, ch in enumerate(snake_case__ ):
if ch in self.SP_CHAR_MAPPING:
lowercase :Optional[int] = self.SP_CHAR_MAPPING.get(snake_case__ )
else:
lowercase :Optional[int] = unicodedata.normalize('''NFKC''' , snake_case__ )
if self.is_whitespace(snake_case__ ):
continue
normalized_text += ch
char_mapping.extend([i] * len(snake_case__ ) )
lowercase , lowercase , lowercase :int = normalized_text, [], 0
if self.do_lower_case:
lowercase :Any = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowercase :Tuple = token[1:]
lowercase :List[str] = text[offset:].index(snake_case__ ) + offset
lowercase :Tuple = start + len(snake_case__ )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowercase :int = end
return token_mapping
@property
def __snake_case ( self : List[Any] ):
'''simple docstring'''
return len(self.vocab )
def __snake_case ( self : Optional[Any] ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self : Optional[int] ):
'''simple docstring'''
lowercase :Any = self.__dict__.copy()
lowercase :Optional[int] = None
return state
def __setstate__( self : Tuple , snake_case__ : Dict ):
'''simple docstring'''
lowercase :Optional[int] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase :Dict = {}
lowercase :List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def __snake_case ( self : int , snake_case__ : List[Any] ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(snake_case__ , snake_case__ ) for c in text) )
def __snake_case ( self : List[str] , snake_case__ : Optional[int] , snake_case__ : int=False , snake_case__ : Dict=6_4 , snake_case__ : Any=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get('''enable_sampling''' ) is True:
lowercase :Any = True
if self.sp_model_kwargs.get('''alpha''' ) is not None:
lowercase :Any = self.sp_model_kwargs.get('''alpha''' )
if self.sp_model_kwargs.get('''nbest_size''' ) is not None:
lowercase :Optional[Any] = self.sp_model_kwargs.get('''nbest_size''' )
if not enable_sampling:
lowercase :Any = self.sp_model.EncodeAsPieces(snake_case__ )
else:
lowercase :List[Any] = self.sp_model.SampleEncodeAsPieces(snake_case__ , snake_case__ , snake_case__ )
lowercase :str = []
for pi, piece in enumerate(snake_case__ ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(snake_case__ ) and pi != 0:
new_pieces.append(snake_case__ )
continue
else:
continue
lowercase :int = 0
for i, chunk in enumerate(snake_case__ ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(snake_case__ ) or self.is_punct(snake_case__ ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(snake_case__ )
lowercase :Optional[int] = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase :str = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase :Dict = i
if len(snake_case__ ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def __snake_case ( self : Dict , snake_case__ : str ):
'''simple docstring'''
lowercase :int = ''''''.join(snake_case__ ).replace(snake_case__ , ''' ''' ).strip()
return out_string
def __snake_case ( self : int , snake_case__ : str ):
'''simple docstring'''
lowercase :Tuple = self.convert_ids_to_tokens(snake_case__ )
lowercase :Any = ''''''.join(snake_case__ ).replace(snake_case__ , ''' ''' ).strip()
return out_string
def __snake_case ( self : int , snake_case__ : Union[str, Any] ):
'''simple docstring'''
return self.vocab.get(snake_case__ , self.vocab.get(self.unk_token ) )
def __snake_case ( self : List[Any] , snake_case__ : List[str] ):
'''simple docstring'''
return self.reverse_vocab.get(snake_case__ , self.unk_token )
def __snake_case ( self : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : Any=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase :int = [self.cls_token_id]
lowercase :str = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def __snake_case ( self : Any , snake_case__ : Dict , snake_case__ : str=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def __snake_case ( self : List[Any] , snake_case__ : Optional[int] , snake_case__ : Any=None , snake_case__ : Optional[int]=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(snake_case__ )) + [1, 1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1]
def __snake_case ( self : List[Any] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(snake_case__ ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(snake_case__ ) + 1) + [1] * (len(snake_case__ ) + 3)
def __snake_case ( self : List[Any] , snake_case__ : Any ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def __snake_case ( self : List[str] , snake_case__ : Any ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def __snake_case ( self : List[str] , snake_case__ : Union[str, Any] ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def __snake_case ( self : Optional[int] , snake_case__ : List[str] ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(snake_case__ ) == 1:
lowercase :str = unicodedata.category(snake_case__ )
if cat == "Zs":
return True
return False
def __snake_case ( self : str , snake_case__ : Any ):
'''simple docstring'''
lowercase :Dict = {}
with io.open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
for index, line in enumerate(snake_case__ ):
lowercase :Dict = line.rstrip('''\n''' )
lowercase :str = int(snake_case__ )
return token_to_idx
def __snake_case ( self : Dict , snake_case__ : str , snake_case__ : Optional[str] = None ):
'''simple docstring'''
lowercase :Optional[int] = 0
if os.path.isdir(snake_case__ ):
lowercase :str = os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
lowercase :Any = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda snake_case__ : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
''' Please check that the vocabulary is not corrupted!''' )
lowercase :Optional[int] = token_index
writer.write(token + '''\n''' )
index += 1
lowercase :int = os.path.join(snake_case__ , '''sentencepiece.bpe.model''' )
with open(snake_case__ , '''wb''' ) as fi:
lowercase :Tuple = self.sp_model.serialized_model_proto()
fi.write(snake_case__ )
return (vocab_file,)
| 172 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a__ : Tuple = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : str = [
"GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : int = [
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxGPTNeoPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
a__ : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 161 |
'''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
a__ : Tuple = logging.get_logger(__name__)
# General docstring
a__ : List[Any] = "RegNetConfig"
# Base docstring
a__ : Dict = "facebook/regnet-y-040"
a__ : Optional[int] = [1, 1_0_8_8, 7, 7]
# Image classification docstring
a__ : Union[str, Any] = "facebook/regnet-y-040"
a__ : Union[str, Any] = "tabby, tabby cat"
a__ : int = [
"facebook/regnet-y-040",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCamelCase__ ( nn.Module):
def __init__( self :Union[str, Any] , _A :int , _A :int , _A :int = 3 , _A :int = 1 , _A :int = 1 , _A :Optional[str] = "relu" , ) -> int:
'''simple docstring'''
super().__init__()
__A = nn.Convad(
_A , _A , kernel_size=_A , stride=_A , padding=kernel_size // 2 , groups=_A , bias=_A , )
__A = nn.BatchNormad(_A )
__A = ACTaFN[activation] if activation is not None else nn.Identity()
def lowercase_ ( self :Tuple , _A :Union[str, Any] ) -> int:
'''simple docstring'''
__A = self.convolution(_A )
__A = self.normalization(_A )
__A = self.activation(_A )
return hidden_state
class UpperCamelCase__ ( nn.Module):
def __init__( self :Optional[int] , _A :RegNetConfig ) -> List[str]:
'''simple docstring'''
super().__init__()
__A = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
__A = config.num_channels
def lowercase_ ( self :Any , _A :Optional[int] ) -> Optional[int]:
'''simple docstring'''
__A = 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.' )
__A = self.embedder(_A )
return hidden_state
class UpperCamelCase__ ( nn.Module):
def __init__( self :Optional[int] , _A :int , _A :int , _A :int = 2 ) -> Any:
'''simple docstring'''
super().__init__()
__A = nn.Convad(_A , _A , kernel_size=1 , stride=_A , bias=_A )
__A = nn.BatchNormad(_A )
def lowercase_ ( self :Optional[int] , _A :Tensor ) -> Tensor:
'''simple docstring'''
__A = self.convolution(_A )
__A = self.normalization(_A )
return hidden_state
class UpperCamelCase__ ( nn.Module):
def __init__( self :Optional[Any] , _A :int , _A :int ) -> List[str]:
'''simple docstring'''
super().__init__()
__A = nn.AdaptiveAvgPoolad((1, 1) )
__A = nn.Sequential(
nn.Convad(_A , _A , kernel_size=1 ) , nn.ReLU() , nn.Convad(_A , _A , kernel_size=1 ) , nn.Sigmoid() , )
def lowercase_ ( self :Any , _A :str ) -> int:
'''simple docstring'''
__A = self.pooler(_A )
__A = self.attention(_A )
__A = hidden_state * attention
return hidden_state
class UpperCamelCase__ ( nn.Module):
def __init__( self :int , _A :RegNetConfig , _A :int , _A :int , _A :int = 1 ) -> List[Any]:
'''simple docstring'''
super().__init__()
__A = in_channels != out_channels or stride != 1
__A = max(1 , out_channels // config.groups_width )
__A = (
RegNetShortCut(_A , _A , stride=_A ) if should_apply_shortcut else nn.Identity()
)
__A = nn.Sequential(
RegNetConvLayer(_A , _A , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(_A , _A , stride=_A , groups=_A , activation=config.hidden_act ) , RegNetConvLayer(_A , _A , kernel_size=1 , activation=_A ) , )
__A = ACTaFN[config.hidden_act]
def lowercase_ ( self :Optional[Any] , _A :int ) -> int:
'''simple docstring'''
__A = hidden_state
__A = self.layer(_A )
__A = self.shortcut(_A )
hidden_state += residual
__A = self.activation(_A )
return hidden_state
class UpperCamelCase__ ( nn.Module):
def __init__( self :Optional[int] , _A :RegNetConfig , _A :int , _A :int , _A :int = 1 ) -> Any:
'''simple docstring'''
super().__init__()
__A = in_channels != out_channels or stride != 1
__A = max(1 , out_channels // config.groups_width )
__A = (
RegNetShortCut(_A , _A , stride=_A ) if should_apply_shortcut else nn.Identity()
)
__A = nn.Sequential(
RegNetConvLayer(_A , _A , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(_A , _A , stride=_A , groups=_A , activation=config.hidden_act ) , RegNetSELayer(_A , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(_A , _A , kernel_size=1 , activation=_A ) , )
__A = ACTaFN[config.hidden_act]
def lowercase_ ( self :int , _A :int ) -> int:
'''simple docstring'''
__A = hidden_state
__A = self.layer(_A )
__A = self.shortcut(_A )
hidden_state += residual
__A = self.activation(_A )
return hidden_state
class UpperCamelCase__ ( nn.Module):
def __init__( self :Tuple , _A :RegNetConfig , _A :int , _A :int , _A :int = 2 , _A :int = 2 , ) -> Any:
'''simple docstring'''
super().__init__()
__A = RegNetXLayer if config.layer_type == 'x' else RegNetYLayer
__A = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_A , _A , _A , stride=_A , ) , *[layer(_A , _A , _A ) for _ in range(depth - 1 )] , )
def lowercase_ ( self :List[str] , _A :Optional[int] ) -> Tuple:
'''simple docstring'''
__A = self.layers(_A )
return hidden_state
class UpperCamelCase__ ( nn.Module):
def __init__( self :Union[str, Any] , _A :RegNetConfig ) -> List[str]:
'''simple docstring'''
super().__init__()
__A = 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(
_A , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
__A = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_A , config.depths[1:] ):
self.stages.append(RegNetStage(_A , _A , _A , depth=_A ) )
def lowercase_ ( self :str , _A :Tensor , _A :bool = False , _A :bool = True ) -> BaseModelOutputWithNoAttention:
'''simple docstring'''
__A = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__A = hidden_states + (hidden_state,)
__A = stage_module(_A )
if output_hidden_states:
__A = 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=_A , hidden_states=_A )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE):
UpperCAmelCase__ : int = RegNetConfig
UpperCAmelCase__ : Dict = 'regnet'
UpperCAmelCase__ : int = 'pixel_values'
UpperCAmelCase__ : Optional[int] = True
def lowercase_ ( self :str , _A :Optional[int] ) -> Tuple:
'''simple docstring'''
if isinstance(_A , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode='fan_out' , nonlinearity='relu' )
elif isinstance(_A , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def lowercase_ ( self :int , _A :str , _A :Dict=False ) -> Dict:
'''simple docstring'''
if isinstance(_A , _A ):
__A = value
a__ : Optional[int] = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
a__ : int = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , SCREAMING_SNAKE_CASE , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE):
def __init__( self :List[str] , _A :List[Any] ) -> List[str]:
'''simple docstring'''
super().__init__(_A )
__A = config
__A = RegNetEmbeddings(_A )
__A = RegNetEncoder(_A )
__A = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_A , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowercase_ ( self :List[Any] , _A :Tensor , _A :Optional[bool] = None , _A :Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
'''simple docstring'''
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.embedder(_A )
__A = self.encoder(
_A , output_hidden_states=_A , return_dict=_A )
__A = encoder_outputs[0]
__A = self.pooler(_A )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_A , pooler_output=_A , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , SCREAMING_SNAKE_CASE , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE):
def __init__( self :Optional[int] , _A :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
super().__init__(_A )
__A = config.num_labels
__A = RegNetModel(_A )
# classification head
__A = 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(_A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowercase_ ( self :Optional[int] , _A :Optional[torch.FloatTensor] = None , _A :Optional[torch.LongTensor] = None , _A :Optional[bool] = None , _A :Optional[bool] = None , ) -> ImageClassifierOutputWithNoAttention:
'''simple docstring'''
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(_A , output_hidden_states=_A , return_dict=_A )
__A = outputs.pooler_output if return_dict else outputs[1]
__A = self.classifier(_A )
__A = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__A = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__A = 'single_label_classification'
else:
__A = 'multi_label_classification'
if self.config.problem_type == "regression":
__A = MSELoss()
if self.num_labels == 1:
__A = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__A = loss_fct(_A , _A )
elif self.config.problem_type == "single_label_classification":
__A = CrossEntropyLoss()
__A = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__A = BCEWithLogitsLoss()
__A = loss_fct(_A , _A )
if not return_dict:
__A = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_A , logits=_A , hidden_states=outputs.hidden_states )
| 161 | 1 |
from random import randint, random
def A ( a_ ,a_ ,a_ ,a_ = False ,a_ = False ,a_ = 5 ,) -> list:
__UpperCamelCase : Dict =[[-1] * number_of_cells] # Create a highway without any car
__UpperCamelCase : Union[str, Any] =0
__UpperCamelCase : int =max(a_ ,0 )
while i < number_of_cells:
__UpperCamelCase : str =(
randint(0 ,a_ ) 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 A ( a_ ,a_ ) -> int:
__UpperCamelCase : Dict =0
__UpperCamelCase : Union[str, Any] =highway_now[car_index + 1 :]
for cell in range(len(a_ ) ): # 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(a_ ,-1 )
def A ( a_ ,a_ ,a_ ) -> list:
__UpperCamelCase : Optional[Any] =len(a_ )
# Beforce calculations, the highway is empty
__UpperCamelCase : Dict =[-1] * number_of_cells
for car_index in range(a_ ):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
__UpperCamelCase : List[Any] =min(highway_now[car_index] + 1 ,a_ )
# Number of empty cell before the next car
__UpperCamelCase : Tuple =get_distance(a_ ,a_ ) - 1
# We can't have the car causing an accident
__UpperCamelCase : List[Any] =min(next_highway[car_index] ,a_ )
if random() < probability:
# Randomly, a driver will slow down
__UpperCamelCase : Optional[int] =max(next_highway[car_index] - 1 ,0 )
return next_highway
def A ( a_ ,a_ ,a_ ,a_ ) -> list:
__UpperCamelCase : Tuple =len(highway[0] )
for i in range(a_ ):
__UpperCamelCase : str =update(highway[i] ,a_ ,a_ )
__UpperCamelCase : Optional[Any] =[-1] * number_of_cells
for car_index in range(a_ ):
__UpperCamelCase : Optional[int] =next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
__UpperCamelCase : Dict =(car_index + speed) % number_of_cells
# Commit the change of position
__UpperCamelCase : str =speed
highway.append(a_ )
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 |
def A ( a_ ) -> bool:
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 245 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : Optional[int] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple ) -> Optional[int]:
lowerCamelCase_ = s.rsplit(_lowerCamelCase , _lowerCamelCase )
return new.join(_lowerCamelCase )
def lowerCamelCase__ ( _lowerCamelCase : Any ) -> List[str]:
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def lowerCamelCase__ ( _lowerCamelCase : Any ) -> Tuple:
lowerCamelCase_ = {}
lowerCamelCase_ = ['group_1', 'group_2', 'group_3', 'group_4']
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
lowerCamelCase_ = key.replace(F'''{group_key}.''' , F'''{group_key}.group.''' )
if "res_path" in key:
lowerCamelCase_ = key.replace('res_path.' , 'res_path.path.' )
if key.endswith('.w' ):
lowerCamelCase_ = rreplace(_lowerCamelCase , '.w' , '.weight' , 1 )
if key.endswith('.b' ):
lowerCamelCase_ = rreplace(_lowerCamelCase , '.b' , '.bias' , 1 )
lowerCamelCase_ = value.float()
return upgrade
@torch.no_grad()
def lowerCamelCase__ ( _lowerCamelCase : str , _lowerCamelCase : Tuple , _lowerCamelCase : Optional[int]=None , _lowerCamelCase : Optional[Any]=True ) -> List[Any]:
from dall_e import Encoder
lowerCamelCase_ = Encoder()
if os.path.exists(_lowerCamelCase ):
lowerCamelCase_ = torch.load(_lowerCamelCase )
else:
lowerCamelCase_ = torch.hub.load_state_dict_from_url(_lowerCamelCase )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
lowerCamelCase_ = ckpt.state_dict()
encoder.load_state_dict(_lowerCamelCase )
if config_path is not None:
lowerCamelCase_ = FlavaImageCodebookConfig.from_pretrained(_lowerCamelCase )
else:
lowerCamelCase_ = FlavaImageCodebookConfig()
lowerCamelCase_ = FlavaImageCodebook(_lowerCamelCase ).eval()
lowerCamelCase_ = encoder.state_dict()
lowerCamelCase_ = upgrade_state_dict(_lowerCamelCase )
hf_model.load_state_dict(_lowerCamelCase )
lowerCamelCase_ = hf_model.state_dict()
lowerCamelCase_ = count_parameters(_lowerCamelCase )
lowerCamelCase_ = count_parameters(_lowerCamelCase )
assert torch.allclose(_lowerCamelCase , _lowerCamelCase , atol=1e-3 )
if save_checkpoint:
hf_model.save_pretrained(_lowerCamelCase )
else:
return hf_state_dict
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Tuple = 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 flava checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
_SCREAMING_SNAKE_CASE : Any = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 183 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
_SCREAMING_SNAKE_CASE : Union[str, Any] = '''CompVis/stable-diffusion-v1-1'''
_SCREAMING_SNAKE_CASE : Optional[Any] = '''CompVis/stable-diffusion-v1-2'''
_SCREAMING_SNAKE_CASE : int = '''CompVis/stable-diffusion-v1-3'''
_SCREAMING_SNAKE_CASE : str = '''CompVis/stable-diffusion-v1-4'''
class a ( __snake_case ):
def __init__( self : int , __SCREAMING_SNAKE_CASE : AutoencoderKL , __SCREAMING_SNAKE_CASE : CLIPTextModel , __SCREAMING_SNAKE_CASE : CLIPTokenizer , __SCREAMING_SNAKE_CASE : UNetaDConditionModel , __SCREAMING_SNAKE_CASE : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , __SCREAMING_SNAKE_CASE : StableDiffusionSafetyChecker , __SCREAMING_SNAKE_CASE : CLIPImageProcessor , __SCREAMING_SNAKE_CASE : bool = True , ) -> List[str]:
super()._init_()
lowerCamelCase_ = StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = StableDiffusionPipeline(
vae=__SCREAMING_SNAKE_CASE , text_encoder=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE , safety_checker=__SCREAMING_SNAKE_CASE , feature_extractor=__SCREAMING_SNAKE_CASE , requires_safety_checker=__SCREAMING_SNAKE_CASE , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea )
@property
def UpperCamelCase ( self : List[str] ) -> Dict[str, Any]:
return {k: getattr(self , __SCREAMING_SNAKE_CASE ) for k in self.config.keys() if not k.startswith('_' )}
def UpperCamelCase ( self : int , __SCREAMING_SNAKE_CASE : Optional[Union[str, int]] = "auto" ) -> Any:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase_ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : Any ) -> List[Any]:
self.enable_attention_slicing(__SCREAMING_SNAKE_CASE )
@torch.no_grad()
def UpperCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 50 , __SCREAMING_SNAKE_CASE : float = 7.5 , __SCREAMING_SNAKE_CASE : Optional[Union[str, List[str]]] = None , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : Optional[torch.Generator] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[str] = "pil" , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __SCREAMING_SNAKE_CASE : int = 1 , **__SCREAMING_SNAKE_CASE : int , ) -> Tuple:
return self.pipea(
prompt=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , output_type=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , callback=__SCREAMING_SNAKE_CASE , callback_steps=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def UpperCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 50 , __SCREAMING_SNAKE_CASE : float = 7.5 , __SCREAMING_SNAKE_CASE : Optional[Union[str, List[str]]] = None , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : Optional[torch.Generator] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[str] = "pil" , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __SCREAMING_SNAKE_CASE : int = 1 , **__SCREAMING_SNAKE_CASE : List[str] , ) -> Optional[int]:
return self.pipea(
prompt=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , output_type=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , callback=__SCREAMING_SNAKE_CASE , callback_steps=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def UpperCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 50 , __SCREAMING_SNAKE_CASE : float = 7.5 , __SCREAMING_SNAKE_CASE : Optional[Union[str, List[str]]] = None , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : Optional[torch.Generator] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[str] = "pil" , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __SCREAMING_SNAKE_CASE : int = 1 , **__SCREAMING_SNAKE_CASE : Optional[int] , ) -> Tuple:
return self.pipea(
prompt=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , output_type=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , callback=__SCREAMING_SNAKE_CASE , callback_steps=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def UpperCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 50 , __SCREAMING_SNAKE_CASE : float = 7.5 , __SCREAMING_SNAKE_CASE : Optional[Union[str, List[str]]] = None , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : Optional[torch.Generator] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[str] = "pil" , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __SCREAMING_SNAKE_CASE : int = 1 , **__SCREAMING_SNAKE_CASE : Tuple , ) -> Tuple:
return self.pipea(
prompt=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , output_type=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , callback=__SCREAMING_SNAKE_CASE , callback_steps=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Union[str, List[str]] , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 512 , __SCREAMING_SNAKE_CASE : int = 50 , __SCREAMING_SNAKE_CASE : float = 7.5 , __SCREAMING_SNAKE_CASE : Optional[Union[str, List[str]]] = None , __SCREAMING_SNAKE_CASE : Optional[int] = 1 , __SCREAMING_SNAKE_CASE : float = 0.0 , __SCREAMING_SNAKE_CASE : Optional[torch.Generator] = None , __SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None , __SCREAMING_SNAKE_CASE : Optional[str] = "pil" , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __SCREAMING_SNAKE_CASE : int = 1 , **__SCREAMING_SNAKE_CASE : int , ) -> str:
lowerCamelCase_ = 'cuda' if torch.cuda.is_available() else 'cpu'
self.to(__SCREAMING_SNAKE_CASE )
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` must be divisible by 8 but are {height} and {width}.''' )
# Get first result from Stable Diffusion Checkpoint v1.1
lowerCamelCase_ = self.textaimg_sda_a(
prompt=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , output_type=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , callback=__SCREAMING_SNAKE_CASE , callback_steps=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
# Get first result from Stable Diffusion Checkpoint v1.2
lowerCamelCase_ = self.textaimg_sda_a(
prompt=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , output_type=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , callback=__SCREAMING_SNAKE_CASE , callback_steps=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
# Get first result from Stable Diffusion Checkpoint v1.3
lowerCamelCase_ = self.textaimg_sda_a(
prompt=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , output_type=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , callback=__SCREAMING_SNAKE_CASE , callback_steps=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
# Get first result from Stable Diffusion Checkpoint v1.4
lowerCamelCase_ = self.textaimg_sda_a(
prompt=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , negative_prompt=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , eta=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , latents=__SCREAMING_SNAKE_CASE , output_type=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE , callback=__SCREAMING_SNAKE_CASE , callback_steps=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]] )
| 183 | 1 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self , lowerCAmelCase , lowerCAmelCase=2 , lowerCAmelCase=3 , lowerCAmelCase=4 , lowerCAmelCase=2 , lowerCAmelCase=7 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=99 , lowerCAmelCase=36 , lowerCAmelCase=3 , lowerCAmelCase=4 , lowerCAmelCase=37 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=5_12 , lowerCAmelCase=16 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=6 , lowerCAmelCase=6 , lowerCAmelCase=3 , lowerCAmelCase=4 , lowerCAmelCase=None , lowerCAmelCase=10_00 , ):
"""simple docstring"""
snake_case = parent
snake_case = batch_size
snake_case = num_channels
snake_case = image_size
snake_case = patch_size
snake_case = text_seq_length
snake_case = is_training
snake_case = use_input_mask
snake_case = use_token_type_ids
snake_case = use_labels
snake_case = vocab_size
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_vocab_size
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = coordinate_size
snake_case = shape_size
snake_case = num_labels
snake_case = num_choices
snake_case = scope
snake_case = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case = text_seq_length
snake_case = (image_size // patch_size) ** 2 + 1
snake_case = self.text_seq_length + self.image_seq_length
def snake_case ( self ):
"""simple docstring"""
snake_case = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
snake_case = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case = bbox[i, j, 3]
snake_case = bbox[i, j, 1]
snake_case = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case = bbox[i, j, 2]
snake_case = bbox[i, j, 0]
snake_case = t
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = None
if self.use_input_mask:
snake_case = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case = None
if self.use_token_type_ids:
snake_case = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
snake_case = None
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
snake_case = LayoutLMvaConfig(
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 , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = LayoutLMvaModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
# text + image
snake_case = model(lowerCAmelCase , pixel_values=lowerCAmelCase )
snake_case = model(
lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase )
snake_case = model(lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , token_type_ids=lowerCAmelCase )
snake_case = model(lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case = model(lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case = model(pixel_values=lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = self.num_labels
snake_case = LayoutLMvaForSequenceClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
snake_case = model(
lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = self.num_labels
snake_case = LayoutLMvaForTokenClassification(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
snake_case = model(
lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
snake_case = model(
lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , start_positions=lowerCAmelCase , end_positions=lowerCAmelCase , )
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 snake_case ( self ):
"""simple docstring"""
snake_case = self.prepare_config_and_inputs()
(
(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,
) = config_and_inputs
snake_case = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : Optional[Any] = False
_lowerCAmelCase : Any = False
_lowerCAmelCase : str = False
_lowerCAmelCase : List[Any] = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
_lowerCAmelCase : str = (
{"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
if is_torch_available()
else {}
)
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
return True
def snake_case ( self ):
"""simple docstring"""
snake_case = LayoutLMvaModelTester(self )
snake_case = ConfigTester(self , config_class=lowerCAmelCase , hidden_size=37 )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=False ):
"""simple docstring"""
snake_case = copy.deepcopy(lowerCAmelCase )
if model_class in get_values(lowerCAmelCase ):
snake_case = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase ):
snake_case = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase )
elif model_class in get_values(lowerCAmelCase ):
snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase )
snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase )
elif model_class in [
*get_values(lowerCAmelCase ),
]:
snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase )
elif model_class in [
*get_values(lowerCAmelCase ),
]:
snake_case = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase , )
return inputs_dict
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case = type
self.model_tester.create_and_check_model(*lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case = LayoutLMvaModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def lowerCAmelCase__ ( ) -> List[Any]:
"""simple docstring"""
snake_case = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case ( self ):
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase ) if is_vision_available() else None
@slow
def snake_case ( self ):
"""simple docstring"""
snake_case = LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' ).to(lowerCAmelCase )
snake_case = self.default_image_processor
snake_case = prepare_img()
snake_case = image_processor(images=lowerCAmelCase , return_tensors='pt' ).pixel_values.to(lowerCAmelCase )
snake_case = torch.tensor([[1, 2]] )
snake_case = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
snake_case = model(
input_ids=input_ids.to(lowerCAmelCase ) , bbox=bbox.to(lowerCAmelCase ) , pixel_values=pixel_values.to(lowerCAmelCase ) , )
# verify the logits
snake_case = torch.Size((1, 1_99, 7_68) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase )
snake_case = torch.tensor(
[[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]] ).to(lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase , atol=1E-4 ) )
| 149 | """simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ = random.Random()
def lowerCAmelCase__ ( _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[Any]=1.0 , _UpperCamelCase : Optional[int]=None , _UpperCamelCase : Optional[int]=None ) -> Optional[int]:
"""simple docstring"""
if rng is None:
snake_case = global_rng
snake_case = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=4_00 , lowerCAmelCase=20_00 , lowerCAmelCase=20_48 , lowerCAmelCase=1_28 , lowerCAmelCase=1 , lowerCAmelCase=5_12 , lowerCAmelCase=30 , lowerCAmelCase=4_41_00 , ):
"""simple docstring"""
snake_case = parent
snake_case = batch_size
snake_case = min_seq_length
snake_case = max_seq_length
snake_case = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
snake_case = spectrogram_length
snake_case = feature_size
snake_case = num_audio_channels
snake_case = hop_length
snake_case = chunk_length
snake_case = sampling_rate
def snake_case ( self ):
"""simple docstring"""
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def snake_case ( self , lowerCAmelCase=False , lowerCAmelCase=False ):
"""simple docstring"""
def _flatten(lowerCAmelCase ):
return list(itertools.chain(*lowerCAmelCase ) )
if equal_length:
snake_case = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
snake_case = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
snake_case = [np.asarray(lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowerCAmelCase_ ( lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : Union[str, Any] = TvltFeatureExtractor
def snake_case ( self ):
"""simple docstring"""
snake_case = TvltFeatureExtractionTester(self )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCAmelCase , 'spectrogram_length' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'feature_size' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'num_audio_channels' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'hop_length' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'chunk_length' ) )
self.assertTrue(hasattr(lowerCAmelCase , 'sampling_rate' ) )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case = feat_extract_first.save_pretrained(lowerCAmelCase )[0]
check_json_file_has_correct_format(lowerCAmelCase )
snake_case = self.feature_extraction_class.from_pretrained(lowerCAmelCase )
snake_case = feat_extract_first.to_dict()
snake_case = feat_extract_second.to_dict()
snake_case = dict_first.pop('mel_filters' )
snake_case = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase ) )
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case = os.path.join(lowerCAmelCase , 'feat_extract.json' )
feat_extract_first.to_json_file(lowerCAmelCase )
snake_case = self.feature_extraction_class.from_json_file(lowerCAmelCase )
snake_case = feat_extract_first.to_dict()
snake_case = feat_extract_second.to_dict()
snake_case = dict_first.pop('mel_filters' )
snake_case = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase ) )
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
snake_case = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
snake_case = [np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]
# Test not batched input
snake_case = feature_extractor(np_speech_inputs[0] , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
snake_case = feature_extractor(lowerCAmelCase , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
snake_case = feature_extractor(
lowerCAmelCase , return_tensors='np' , sampling_rate=4_41_00 , mask_audio=lowerCAmelCase ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
snake_case = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
snake_case = np.asarray(lowerCAmelCase )
snake_case = feature_extractor(lowerCAmelCase , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
snake_case = ds.sort('id' ).select(range(lowerCAmelCase ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def snake_case ( self ):
"""simple docstring"""
snake_case = self._load_datasamples(1 )
snake_case = TvltFeatureExtractor()
snake_case = feature_extractor(lowerCAmelCase , return_tensors='pt' ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_92, 1_28) )
snake_case = torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , lowerCAmelCase , atol=1E-4 ) )
| 149 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[str, Any] = ['image_processor']
A : Optional[Any] = 'SamImageProcessor'
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super().__init__(SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = self.image_processor
lowercase : List[Any] = -10
lowercase : Dict = self.image_processor.size['''longest_edge''']
def __call__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
lowercase : Optional[Any] = self.image_processor(
SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
# pop arguments that are not used in the foward but used nevertheless
lowercase : List[Any] = encoding_image_processor['''original_sizes''']
if hasattr(SCREAMING_SNAKE_CASE__ , '''numpy''' ): # Checks if Torch or TF tensor
lowercase : List[str] = original_sizes.numpy()
lowercase , lowercase , lowercase : List[Any] = self._check_and_preprocess_points(
input_points=SCREAMING_SNAKE_CASE__ , input_labels=SCREAMING_SNAKE_CASE__ , input_boxes=SCREAMING_SNAKE_CASE__ , )
lowercase : str = self._normalize_and_convert(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , input_points=SCREAMING_SNAKE_CASE__ , input_labels=SCREAMING_SNAKE_CASE__ , input_boxes=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , )
return encoding_image_processor
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="pt" , ):
if input_points is not None:
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[Any] = [
self._normalize_coordinates(self.target_size , SCREAMING_SNAKE_CASE__ , original_sizes[0] ) for point in input_points
]
else:
lowercase : Optional[int] = [
self._normalize_coordinates(self.target_size , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
for point, original_size in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
lowercase , lowercase : Any = self._pad_points_and_labels(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Any = np.array(SCREAMING_SNAKE_CASE__ )
if input_labels is not None:
lowercase : Union[str, Any] = np.array(SCREAMING_SNAKE_CASE__ )
if input_boxes is not None:
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
lowercase : int = [
self._normalize_coordinates(self.target_size , SCREAMING_SNAKE_CASE__ , original_sizes[0] , is_bounding_box=SCREAMING_SNAKE_CASE__ )
for box in input_boxes
]
else:
lowercase : int = [
self._normalize_coordinates(self.target_size , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , is_bounding_box=SCREAMING_SNAKE_CASE__ )
for box, original_size in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
]
lowercase : Any = np.array(SCREAMING_SNAKE_CASE__ )
if input_boxes is not None:
if return_tensors == "pt":
lowercase : int = torch.from_numpy(SCREAMING_SNAKE_CASE__ )
# boxes batch size of 1 by default
lowercase : List[str] = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
lowercase : Any = tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ )
# boxes batch size of 1 by default
lowercase : Optional[int] = tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'''input_boxes''': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
lowercase : Union[str, Any] = torch.from_numpy(SCREAMING_SNAKE_CASE__ )
# point batch size of 1 by default
lowercase : Union[str, Any] = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
lowercase : Any = tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ )
# point batch size of 1 by default
lowercase : Dict = tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'''input_points''': input_points} )
if input_labels is not None:
if return_tensors == "pt":
lowercase : Union[str, Any] = torch.from_numpy(SCREAMING_SNAKE_CASE__ )
# point batch size of 1 by default
lowercase : Dict = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
lowercase : str = tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ )
# point batch size of 1 by default
lowercase : Tuple = tf.expand_dims(SCREAMING_SNAKE_CASE__ , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'''input_labels''': input_labels} )
return encoding_image_processor
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = max([point.shape[0] for point in input_points] )
lowercase : str = []
for i, point in enumerate(SCREAMING_SNAKE_CASE__ ):
if point.shape[0] != expected_nb_points:
lowercase : Optional[int] = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
lowercase : Union[str, Any] = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(SCREAMING_SNAKE_CASE__ )
lowercase : Dict = processed_input_points
return input_points, input_labels
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ):
lowercase , lowercase : Union[str, Any] = original_size
lowercase , lowercase : List[Any] = self.image_processor._get_preprocess_shape(SCREAMING_SNAKE_CASE__ , longest_edge=SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = deepcopy(SCREAMING_SNAKE_CASE__ ).astype(SCREAMING_SNAKE_CASE__ )
if is_bounding_box:
lowercase : Any = coords.reshape(-1 , 2 , 2 )
lowercase : Any = coords[..., 0] * (new_w / old_w)
lowercase : List[Any] = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
lowercase : Union[str, Any] = coords.reshape(-1 , 4 )
return coords
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , ):
if input_points is not None:
if hasattr(SCREAMING_SNAKE_CASE__ , '''numpy''' ): # Checks for TF or Torch tensor
lowercase : Tuple = input_points.numpy().tolist()
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or not isinstance(input_points[0] , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''Input points must be a list of list of floating points.''' )
lowercase : Optional[Any] = [np.array(SCREAMING_SNAKE_CASE__ ) for input_point in input_points]
else:
lowercase : Dict = None
if input_labels is not None:
if hasattr(SCREAMING_SNAKE_CASE__ , '''numpy''' ):
lowercase : str = input_labels.numpy().tolist()
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or not isinstance(input_labels[0] , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''Input labels must be a list of list integers.''' )
lowercase : Tuple = [np.array(SCREAMING_SNAKE_CASE__ ) for label in input_labels]
else:
lowercase : Any = None
if input_boxes is not None:
if hasattr(SCREAMING_SNAKE_CASE__ , '''numpy''' ):
lowercase : Tuple = input_boxes.numpy().tolist()
if (
not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
or not isinstance(input_boxes[0] , SCREAMING_SNAKE_CASE__ )
or not isinstance(input_boxes[0][0] , SCREAMING_SNAKE_CASE__ )
):
raise ValueError('''Input boxes must be a list of list of list of floating points.''' )
lowercase : List[str] = [np.array(SCREAMING_SNAKE_CASE__ ).astype(np.floataa ) for box in input_boxes]
else:
lowercase : List[Any] = None
return input_points, input_labels, input_boxes
@property
def __lowerCamelCase ( self ):
lowercase : List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(SCREAMING_SNAKE_CASE__ ) )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
return self.image_processor.post_process_masks(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 337 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 337 | 1 |
'''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_funnel import FunnelTokenizer
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_lowerCAmelCase = [
"small",
"small-base",
"medium",
"medium-base",
"intermediate",
"intermediate-base",
"large",
"large-base",
"xlarge",
"xlarge-base",
]
_lowerCAmelCase = {
"vocab_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
"funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
"funnel-transformer/medium-base": (
"https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
),
"funnel-transformer/intermediate": (
"https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
),
"funnel-transformer/intermediate-base": (
"https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
),
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
"funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
"funnel-transformer/xlarge-base": (
"https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
"funnel-transformer/small-base": (
"https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
),
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
"funnel-transformer/medium-base": (
"https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
),
"funnel-transformer/intermediate": (
"https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
),
"funnel-transformer/intermediate-base": (
"https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
),
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
"funnel-transformer/large-base": (
"https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
),
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
"funnel-transformer/xlarge-base": (
"https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
),
},
}
_lowerCAmelCase = {F'''funnel-transformer/{name}''': 512 for name in _model_names}
_lowerCAmelCase = {F'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Union[str, Any] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE :Any = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE :List[Any] = PRETRAINED_INIT_CONFIGURATION
__SCREAMING_SNAKE_CASE :str = FunnelTokenizer
__SCREAMING_SNAKE_CASE :Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE :int = 2
def __init__( self : List[str] , a__ : List[str]=None , a__ : str=None , a__ : Tuple=True , a__ : Any="<unk>" , a__ : List[str]="<sep>" , a__ : Optional[int]="<pad>" , a__ : Dict="<cls>" , a__ : Union[str, Any]="<mask>" , a__ : Optional[Any]="<s>" , a__ : List[str]="</s>" , a__ : Union[str, Any]=True , a__ : str=True , a__ : int=None , a__ : Tuple="##" , **a__ : str , ):
super().__init__(
a__ , tokenizer_file=a__ , do_lower_case=a__ , unk_token=a__ , sep_token=a__ , pad_token=a__ , cls_token=a__ , mask_token=a__ , bos_token=a__ , eos_token=a__ , clean_text=a__ , tokenize_chinese_chars=a__ , strip_accents=a__ , wordpieces_prefix=a__ , **a__ , )
__magic_name__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , a__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , a__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , a__ ) != tokenize_chinese_chars
):
__magic_name__ = getattr(a__ , normalizer_state.pop('''type''' ) )
__magic_name__ = do_lower_case
__magic_name__ = strip_accents
__magic_name__ = tokenize_chinese_chars
__magic_name__ = normalizer_class(**a__ )
__magic_name__ = do_lower_case
def snake_case__ ( self : List[str] , a__ : Any , a__ : List[str]=None ):
__magic_name__ = [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 snake_case__ ( self : List[str] , a__ : List[int] , a__ : Optional[List[int]] = None ):
__magic_name__ = [self.sep_token_id]
__magic_name__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case__ ( self : List[Any] , a__ : str , a__ : Optional[str] = None ):
__magic_name__ = self._tokenizer.model.save(a__ , name=a__ )
return tuple(a__ )
| 367 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = ["GLPNFeatureExtractor"]
_lowerCAmelCase = ["GLPNImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
"GLPNForDepthEstimation",
"GLPNLayer",
"GLPNModel",
"GLPNPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 98 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class UpperCamelCase ( unittest.TestCase ):
def __init__(self : Any , _A : int , _A : Dict=13 , _A : Tuple=7 , _A : int=True , _A : str=True , _A : List[Any]=True , _A : Union[str, Any]=True , _A : str=99 , _A : Union[str, Any]=32 , _A : Tuple=5 , _A : str=4 , _A : int=37 , _A : Tuple="gelu" , _A : List[str]=0.1 , _A : str=0.1 , _A : Union[str, Any]=5_12 , _A : Optional[Any]=16 , _A : Any=2 , _A : Optional[int]=0.02 , _A : Dict=4 , ) -> Tuple:
__snake_case : Tuple = parent
__snake_case : List[str] = batch_size
__snake_case : List[Any] = seq_length
__snake_case : List[str] = is_training
__snake_case : List[str] = use_attention_mask
__snake_case : Optional[Any] = use_token_type_ids
__snake_case : int = use_labels
__snake_case : int = vocab_size
__snake_case : Union[str, Any] = hidden_size
__snake_case : str = num_hidden_layers
__snake_case : int = num_attention_heads
__snake_case : Any = intermediate_size
__snake_case : int = hidden_act
__snake_case : List[Any] = hidden_dropout_prob
__snake_case : int = attention_probs_dropout_prob
__snake_case : Optional[Any] = max_position_embeddings
__snake_case : Dict = type_vocab_size
__snake_case : Tuple = type_sequence_label_size
__snake_case : str = initializer_range
__snake_case : List[Any] = num_choices
def _lowercase (self : List[Any]) -> Optional[int]:
__snake_case : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__snake_case : Optional[int] = None
if self.use_attention_mask:
__snake_case : Tuple = random_attention_mask([self.batch_size, self.seq_length])
__snake_case : Any = None
if self.use_token_type_ids:
__snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
__snake_case : Union[str, Any] = BertConfig(
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=_A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowercase (self : Optional[int]) -> Optional[int]:
__snake_case : Tuple = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case : List[Any] = config_and_inputs
__snake_case : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def _lowercase (self : Optional[Any]) -> Any:
__snake_case : Union[str, Any] = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case : List[Any] = config_and_inputs
__snake_case : Tuple = True
__snake_case : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
__snake_case : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class UpperCamelCase ( lowercase , unittest.TestCase ):
UpperCAmelCase : int = True
UpperCAmelCase : List[str] = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowercase (self : List[Any]) -> int:
__snake_case : Union[str, Any] = FlaxBertModelTester(self)
@slow
def _lowercase (self : Optional[int]) -> Optional[Any]:
# Only check this for base model, not necessary for all model classes.
# This will also help speed-up tests.
__snake_case : Dict = FlaxBertModel.from_pretrained('bert-base-cased')
__snake_case : str = model(np.ones((1, 1)))
self.assertIsNotNone(_A)
| 172 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : int= logging.get_logger(__name__)
_a : Optional[Any]= {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class UpperCamelCase ( lowercase ):
UpperCAmelCase : List[Any] = """lilt"""
def __init__(self : Dict , _A : Any=3_05_22 , _A : Union[str, Any]=7_68 , _A : Any=12 , _A : Tuple=12 , _A : Optional[int]=30_72 , _A : Tuple="gelu" , _A : str=0.1 , _A : List[Any]=0.1 , _A : Union[str, Any]=5_12 , _A : Any=2 , _A : Tuple=0.02 , _A : List[str]=1E-12 , _A : Optional[int]=0 , _A : Optional[Any]="absolute" , _A : Any=None , _A : List[Any]=4 , _A : Optional[int]=10_24 , **_A : Union[str, Any] , ) -> Tuple:
super().__init__(pad_token_id=_A , **_A)
__snake_case : Optional[int] = vocab_size
__snake_case : List[Any] = hidden_size
__snake_case : Any = num_hidden_layers
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[int] = hidden_act
__snake_case : List[str] = intermediate_size
__snake_case : Union[str, Any] = hidden_dropout_prob
__snake_case : Dict = attention_probs_dropout_prob
__snake_case : List[Any] = max_position_embeddings
__snake_case : Dict = type_vocab_size
__snake_case : List[Any] = initializer_range
__snake_case : Optional[Any] = layer_norm_eps
__snake_case : Optional[int] = position_embedding_type
__snake_case : Any = classifier_dropout
__snake_case : Optional[int] = channel_shrink_ratio
__snake_case : Tuple = max_ad_position_embeddings
| 172 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
_lowercase: Dict = get_tests_dir("fixtures")
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
a = mock.Mock()
a = 500
a = {}
a = HTTPError
a = {}
# Download this model to make sure it's in the cache.
a = WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=lowerCamelCase_ ) as mock_head:
a = WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase_ (self ):
"""simple docstring"""
a = WavaVecaFeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json" )
@is_staging_test
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def UpperCamelCase_ (cls ):
"""simple docstring"""
a = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def UpperCamelCase_ (cls ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-feature-extractor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-feature-extractor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-feature-extractor" )
except HTTPError:
pass
def UpperCamelCase_ (self ):
"""simple docstring"""
a = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_ )
feature_extractor.push_to_hub("test-feature-extractor" , use_auth_token=self._token )
a = WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-feature-extractor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
lowerCamelCase_ , repo_id="test-feature-extractor" , push_to_hub=lowerCamelCase_ , use_auth_token=self._token )
a = WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
def UpperCamelCase_ (self ):
"""simple docstring"""
a = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_ )
feature_extractor.push_to_hub("valid_org/test-feature-extractor" , use_auth_token=self._token )
a = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-feature-extractor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
lowerCamelCase_ , repo_id="valid_org/test-feature-extractor-org" , push_to_hub=lowerCamelCase_ , use_auth_token=self._token )
a = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowerCamelCase_ , getattr(lowerCamelCase_ , lowerCamelCase_ ) )
def UpperCamelCase_ (self ):
"""simple docstring"""
CustomFeatureExtractor.register_for_auto_class()
a = CustomFeatureExtractor.from_pretrained(lowerCamelCase_ )
feature_extractor.push_to_hub("test-dynamic-feature-extractor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"} , )
a = AutoFeatureExtractor.from_pretrained(
F'''{USER}/test-dynamic-feature-extractor''' , trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , "CustomFeatureExtractor" )
| 71 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__(self , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ ):
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , lowerCamelCase_ , )
a = kwargs.pop("feature_extractor" )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(lowerCamelCase_ , lowerCamelCase_ )
def __call__(self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ ):
"""simple docstring"""
if text is None and visual_prompt is None and images is None:
raise ValueError("You have to specify either text, visual prompt or images." )
if text is not None and visual_prompt is not None:
raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt." )
if text is not None:
a = self.tokenizer(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if visual_prompt is not None:
a = self.image_processor(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if images is not None:
a = self.image_processor(lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
if visual_prompt is not None and images is not None:
a = {
"pixel_values": image_features.pixel_values,
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
a = {
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowerCamelCase_ ) , tensor_type=lowerCamelCase_ )
def UpperCamelCase_ (self , *lowerCamelCase_ , **lowerCamelCase_ ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCamelCase_ , **lowerCamelCase_ )
def UpperCamelCase_ (self , *lowerCamelCase_ , **lowerCamelCase_ ):
"""simple docstring"""
return self.tokenizer.decode(*lowerCamelCase_ , **lowerCamelCase_ )
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , lowerCamelCase_ , )
return self.image_processor_class
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , lowerCamelCase_ , )
return self.image_processor
| 71 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class a_ ( snake_case_ ):
'''simple docstring'''
UpperCamelCase = """nllb-moe"""
UpperCamelCase = ["""past_key_values"""]
UpperCamelCase = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , A=12_8112 , A=1024 , A=12 , A=4096 , A=16 , A=12 , A=4096 , A=16 , A=0.05 , A=0.05 , A=True , A=True , A="relu" , A=1024 , A=0.1 , A=0.1 , A=0.0 , A=0.02 , A=2 , A=True , A=False , A="float32" , A=False , A=128 , A=64 , A=4 , A=4 , A=0.001 , A=0.001 , A="all" , A=False , A=False , A=1.0 , A=0.2 , A=1 , A=0 , A=2 , A=False , **A , ) -> int:
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = d_model
_SCREAMING_SNAKE_CASE = encoder_ffn_dim
_SCREAMING_SNAKE_CASE = encoder_layers
_SCREAMING_SNAKE_CASE = encoder_attention_heads
_SCREAMING_SNAKE_CASE = decoder_ffn_dim
_SCREAMING_SNAKE_CASE = decoder_layers
_SCREAMING_SNAKE_CASE = decoder_attention_heads
_SCREAMING_SNAKE_CASE = dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = activation_dropout
_SCREAMING_SNAKE_CASE = activation_function
_SCREAMING_SNAKE_CASE = init_std
_SCREAMING_SNAKE_CASE = encoder_layerdrop
_SCREAMING_SNAKE_CASE = decoder_layerdrop
_SCREAMING_SNAKE_CASE = use_cache
_SCREAMING_SNAKE_CASE = encoder_layers
_SCREAMING_SNAKE_CASE = scale_embedding # scale factor will be sqrt(d_model) if True
_SCREAMING_SNAKE_CASE = router_z_loss_coef
_SCREAMING_SNAKE_CASE = router_aux_loss_coef
_SCREAMING_SNAKE_CASE = decoder_sparse_step
_SCREAMING_SNAKE_CASE = encoder_sparse_step
_SCREAMING_SNAKE_CASE = num_experts
_SCREAMING_SNAKE_CASE = expert_capacity
_SCREAMING_SNAKE_CASE = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
_SCREAMING_SNAKE_CASE = router_dtype
_SCREAMING_SNAKE_CASE = router_ignore_padding_tokens
_SCREAMING_SNAKE_CASE = batch_prioritized_routing
_SCREAMING_SNAKE_CASE = second_expert_policy
_SCREAMING_SNAKE_CASE = normalize_router_prob_before_dropping
_SCREAMING_SNAKE_CASE = moe_eval_capacity_token_fraction
_SCREAMING_SNAKE_CASE = moe_token_dropout
_SCREAMING_SNAKE_CASE = output_router_logits
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , is_encoder_decoder=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 58 |
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ : Tuple = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __lowercase ( _A ) -> str:
re.sub("""<n>""" , """""" , _A ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_A ) )
| 245 | 0 |
"""simple docstring"""
__UpperCamelCase : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __SCREAMING_SNAKE_CASE ( A_ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(A_ , A_ ):
lowerCAmelCase__ : Dict = f'a bytes-like object is required, not \'{data.__class__.__name__}\''
raise TypeError(A_ )
lowerCAmelCase__ : Any = ''''''.join(bin(A_ )[2:].zfill(8 ) for byte in data )
lowerCAmelCase__ : List[str] = len(A_ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowerCAmelCase__ : List[str] = b'''=''' * ((6 - len(A_ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(A_ ) % 6)
else:
lowerCAmelCase__ : Tuple = b''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(A_ ) , 6 ) ).encode()
+ padding
)
def __SCREAMING_SNAKE_CASE ( A_ ):
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(A_ , A_ ) and not isinstance(A_ , A_ ):
lowerCAmelCase__ : str = (
'''argument should be a bytes-like object or ASCII string, '''
f'not \'{encoded_data.__class__.__name__}\''
)
raise TypeError(A_ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(A_ , A_ ):
try:
lowerCAmelCase__ : Union[str, Any] = encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
lowerCAmelCase__ : Union[str, Any] = encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(A_ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowerCAmelCase__ : List[str] = encoded_data[:-padding]
lowerCAmelCase__ : Tuple = ''''''.join(
bin(B64_CHARSET.index(A_ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowerCAmelCase__ : Any = ''''''.join(
bin(B64_CHARSET.index(A_ ) )[2:].zfill(6 ) for char in encoded_data )
lowerCAmelCase__ : List[Any] = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(A_ ) , 8 )
]
return bytes(A_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 74 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__UpperCamelCase : Union[str, Any] = namedtuple(
'''_TestCommandArgs''',
[
'''dataset''',
'''name''',
'''cache_dir''',
'''data_dir''',
'''all_configs''',
'''save_infos''',
'''ignore_verifications''',
'''force_redownload''',
'''clear_cache''',
],
defaults=[None, None, None, False, False, False, False, False],
)
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Dict = _TestCommandArgs(dataset=A_ , all_configs=A_ , save_infos=A_ )
lowerCAmelCase__ : Optional[int] = TestCommand(*A_ )
test_command.run()
lowerCAmelCase__ : int = os.path.join(A_ , '''README.md''' )
assert os.path.exists(A_ )
lowerCAmelCase__ : List[Any] = DatasetInfosDict.from_directory(A_ )
lowerCAmelCase__ : List[str] = DatasetInfosDict(
{
'''default''': DatasetInfo(
features=Features(
{
'''tokens''': Sequence(Value('''string''' ) ),
'''ner_tags''': Sequence(
ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC'''] ) ),
'''langs''': Sequence(Value('''string''' ) ),
'''spans''': Sequence(Value('''string''' ) ),
} ) , splits=[
{
'''name''': '''train''',
'''num_bytes''': 2_35_15_63,
'''num_examples''': 1_00_00,
},
{
'''name''': '''validation''',
'''num_bytes''': 23_84_18,
'''num_examples''': 10_00,
},
] , download_size=3_94_06_80 , dataset_size=2_58_99_81 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = getattr(dataset_infos['''default'''] , A_ ), getattr(expected_dataset_infos['''default'''] , A_ )
if key == "num_bytes":
assert is_apercent_close(A_ , A_ )
elif key == "splits":
assert list(A_ ) == list(A_ )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected
| 74 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A__: int = 16
A__: str = 32
def lowerCAmelCase_ ( A_ ,A_ = 16):
UpperCamelCase__: Dict = AutoTokenizer.from_pretrained("bert-base-cased")
UpperCamelCase__: Optional[int] = load_dataset("glue" ,"mrpc")
def tokenize_function(A_):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase__: str = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=A_ ,max_length=A_)
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase__: Optional[Any] = datasets.map(
A_ ,batched=A_ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase__: Any = tokenized_datasets.rename_column("label" ,"labels")
def collate_fn(A_):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase__: str = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase__: Tuple = 16
elif accelerator.mixed_precision != "no":
UpperCamelCase__: Union[str, Any] = 8
else:
UpperCamelCase__: Union[str, Any] = None
return tokenizer.pad(
A_ ,padding="longest" ,max_length=A_ ,pad_to_multiple_of=A_ ,return_tensors="pt" ,)
# Instantiate dataloaders.
UpperCamelCase__: Optional[Any] = DataLoader(
tokenized_datasets["train"] ,shuffle=A_ ,collate_fn=A_ ,batch_size=A_)
UpperCamelCase__: int = DataLoader(
tokenized_datasets["validation"] ,shuffle=A_ ,collate_fn=A_ ,batch_size=A_)
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
A__: Optional[Any] = mocked_dataloaders # noqa: F811
def lowerCAmelCase_ ( A_ ,A_):
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,A_) == "1":
UpperCamelCase__: Tuple = 2
# Initialize accelerator
UpperCamelCase__: Optional[Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision)
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase__: Optional[int] = config["lr"]
UpperCamelCase__: Optional[int] = int(config["num_epochs"])
UpperCamelCase__: Optional[Any] = int(config["seed"])
UpperCamelCase__: Union[str, Any] = int(config["batch_size"])
UpperCamelCase__: int = evaluate.load("glue" ,"mrpc")
# If the batch size is too big we use gradient accumulation
UpperCamelCase__: Tuple = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
UpperCamelCase__: Tuple = batch_size // MAX_GPU_BATCH_SIZE
UpperCamelCase__: Optional[int] = MAX_GPU_BATCH_SIZE
set_seed(A_)
UpperCamelCase__ , UpperCamelCase__: Tuple = get_dataloaders(A_ ,A_)
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase__: Any = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=A_)
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase__: List[Any] = model.to(accelerator.device)
# Instantiate optimizer
UpperCamelCase__: Optional[Any] = AdamW(params=model.parameters() ,lr=A_)
# Instantiate scheduler
UpperCamelCase__: Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=A_ ,num_warmup_steps=1_00 ,num_training_steps=(len(A_) * num_epochs) // gradient_accumulation_steps ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__: str = accelerator.prepare(
A_ ,A_ ,A_ ,A_ ,A_)
# Now we train the model
for epoch in range(A_):
model.train()
for step, batch in enumerate(A_):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
UpperCamelCase__: str = model(**A_)
UpperCamelCase__: Tuple = outputs.loss
UpperCamelCase__: Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(A_)
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
UpperCamelCase__: Optional[Any] = 0
for step, batch in enumerate(A_):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
with torch.no_grad():
UpperCamelCase__: str = model(**A_)
UpperCamelCase__: Any = outputs.logits.argmax(dim=-1)
UpperCamelCase__ , UpperCamelCase__: List[str] = accelerator.gather((predictions, batch["labels"]))
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(A_) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
UpperCamelCase__: Any = predictions[: len(eval_dataloader.dataset) - samples_seen]
UpperCamelCase__: Optional[int] = references[: len(eval_dataloader.dataset) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=A_ ,references=A_ ,)
UpperCamelCase__: int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" ,A_)
def lowerCAmelCase_ ( ):
UpperCamelCase__: List[str] = argparse.ArgumentParser(description="Simple example of training script.")
parser.add_argument(
"--mixed_precision" ,type=A_ ,default=A_ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU.")
UpperCamelCase__: Any = parser.parse_args()
UpperCamelCase__: Optional[int] = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(A_ ,A_)
if __name__ == "__main__":
main()
| 149 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _a :
"""simple docstring"""
def __init__( self: str , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: str=sys.maxsize ):
'''simple docstring'''
UpperCamelCase__: List[Any] = "bilinear"
UpperCamelCase__: Optional[int] = max_size
UpperCamelCase__: Optional[int] = short_edge_length
def __call__( self: Optional[Any] , __lowerCamelCase: str ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = []
for img in imgs:
UpperCamelCase__ , UpperCamelCase__: Any = img.shape[:2]
# later: provide list and randomly choose index for resize
UpperCamelCase__: Optional[int] = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
UpperCamelCase__: Dict = size * 1.0 / min(__lowerCamelCase , __lowerCamelCase )
if h < w:
UpperCamelCase__ , UpperCamelCase__: Optional[Any] = size, scale * w
else:
UpperCamelCase__ , UpperCamelCase__: Dict = scale * h, size
if max(__lowerCamelCase , __lowerCamelCase ) > self.max_size:
UpperCamelCase__: str = self.max_size * 1.0 / max(__lowerCamelCase , __lowerCamelCase )
UpperCamelCase__: List[str] = newh * scale
UpperCamelCase__: Any = neww * scale
UpperCamelCase__: List[str] = int(neww + 0.5 )
UpperCamelCase__: List[Any] = int(newh + 0.5 )
if img.dtype == np.uinta:
UpperCamelCase__: Dict = Image.fromarray(__lowerCamelCase )
UpperCamelCase__: Any = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
UpperCamelCase__: str = np.asarray(__lowerCamelCase )
else:
UpperCamelCase__: Dict = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
UpperCamelCase__: Optional[Any] = nn.functional.interpolate(
__lowerCamelCase , (newh, neww) , mode=self.interp_method , align_corners=__lowerCamelCase ).squeeze(0 )
img_augs.append(__lowerCamelCase )
return img_augs
class _a :
"""simple docstring"""
def __init__( self: Dict , __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
UpperCamelCase__: List[Any] = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
UpperCamelCase__: Union[str, Any] = cfg.INPUT.FORMAT
UpperCamelCase__: Union[str, Any] = cfg.SIZE_DIVISIBILITY
UpperCamelCase__: Tuple = cfg.PAD_VALUE
UpperCamelCase__: str = cfg.INPUT.MAX_SIZE_TEST
UpperCamelCase__: int = cfg.MODEL.DEVICE
UpperCamelCase__: str = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
UpperCamelCase__: int = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
UpperCamelCase__: List[Any] = lambda __lowerCamelCase : (x - self.pixel_mean) / self.pixel_std
def UpperCAmelCase_ ( self: List[str] , __lowerCamelCase: List[Any] ):
'''simple docstring'''
UpperCamelCase__: Dict = tuple(max(__lowerCamelCase ) for s in zip(*[img.shape for img in images] ) )
UpperCamelCase__: Tuple = [im.shape[-2:] for im in images]
UpperCamelCase__: Optional[int] = [
nn.functional.pad(
__lowerCamelCase , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(__lowerCamelCase , __lowerCamelCase )
]
return torch.stack(__lowerCamelCase ), torch.tensor(__lowerCamelCase )
def __call__( self: str , __lowerCamelCase: Dict , __lowerCamelCase: Any=False ):
'''simple docstring'''
with torch.no_grad():
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
UpperCamelCase__: int = [images]
if single_image:
assert len(__lowerCamelCase ) == 1
for i in range(len(__lowerCamelCase ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(__lowerCamelCase , images.pop(__lowerCamelCase ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
__lowerCamelCase , torch.as_tensor(img_tensorize(images.pop(__lowerCamelCase ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
UpperCamelCase__: int = torch.tensor([im.shape[:2] for im in images] )
UpperCamelCase__: int = self.aug(__lowerCamelCase )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
UpperCamelCase__: Any = [self.normalizer(__lowerCamelCase ) for x in images]
# now pad them to do the following operations
UpperCamelCase__ , UpperCamelCase__: Any = self.pad(__lowerCamelCase )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
UpperCamelCase__: Optional[int] = torch.true_divide(__lowerCamelCase , __lowerCamelCase )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def lowerCAmelCase_ ( A_ ,A_):
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def lowerCAmelCase_ ( A_ ,A_):
assert torch.isfinite(A_).all(), "Box tensor contains infinite or NaN!"
UpperCamelCase__ , UpperCamelCase__: int = box_size
tensor[:, 0].clamp_(min=0 ,max=A_)
tensor[:, 1].clamp_(min=0 ,max=A_)
tensor[:, 2].clamp_(min=0 ,max=A_)
tensor[:, 3].clamp_(min=0 ,max=A_)
| 149 | 1 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def a_ ( _lowercase , _lowercase ):
_UpperCamelCase : Tuple = []
for part_id in partition_order:
_UpperCamelCase : int = df.where(F"""SPARK_PARTITION_ID() = {part_id}""" ).collect()
for row_idx, row in enumerate(_lowercase ):
expected_row_ids_and_row_dicts.append((F"""{part_id}_{row_idx}""", row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def a_ ( ):
_UpperCamelCase : Any = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
_UpperCamelCase : Union[str, Any] = spark.range(100 ).repartition(1 )
_UpperCamelCase : Dict = Spark(_lowercase )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def a_ ( ):
_UpperCamelCase : Union[str, Any] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
_UpperCamelCase : str = spark.range(10 ).repartition(2 )
_UpperCamelCase : List[str] = [1, 0]
_UpperCamelCase : str = _generate_iterable_examples(_lowercase , _lowercase ) # Reverse the partitions.
_UpperCamelCase : str = _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase , _lowercase )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
_UpperCamelCase , _UpperCamelCase : Union[str, Any] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def a_ ( ):
_UpperCamelCase : Optional[Any] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
_UpperCamelCase : List[str] = spark.range(10 ).repartition(1 )
_UpperCamelCase : Optional[int] = SparkExamplesIterable(_lowercase )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_lowercase ):
assert row_id == F"""0_{i}"""
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def a_ ( ):
_UpperCamelCase : List[Any] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
_UpperCamelCase : int = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch('''numpy.random.Generator''' ) as generator_mock:
_UpperCamelCase : Optional[int] = lambda _lowercase : x.reverse()
_UpperCamelCase : Optional[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase , [2, 1, 0] )
_UpperCamelCase : Tuple = SparkExamplesIterable(_lowercase ).shuffle_data_sources(_lowercase )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_lowercase ):
_UpperCamelCase , _UpperCamelCase : List[Any] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def a_ ( ):
_UpperCamelCase : List[str] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
_UpperCamelCase : Optional[Any] = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
_UpperCamelCase : Union[str, Any] = SparkExamplesIterable(_lowercase ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
_UpperCamelCase : Any = _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase , [0, 2] )
for i, (row_id, row_dict) in enumerate(_lowercase ):
_UpperCamelCase , _UpperCamelCase : List[Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
_UpperCamelCase : List[Any] = SparkExamplesIterable(_lowercase ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
_UpperCamelCase : List[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase , [1, 3] )
for i, (row_id, row_dict) in enumerate(_lowercase ):
_UpperCamelCase , _UpperCamelCase : int = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def a_ ( ):
_UpperCamelCase : Union[str, Any] = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
_UpperCamelCase : Any = spark.range(100 ).repartition(1 )
_UpperCamelCase : Optional[int] = Spark(_lowercase )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
| 128 |
"""simple docstring"""
from __future__ import annotations
import math
def a_ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(_lowercase ) == 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 , _lowercase , _lowercase , _lowercase ) , minimax(depth + 1 , node_index * 2 + 1 , _lowercase , _lowercase , _lowercase ) , )
return min(
minimax(depth + 1 , node_index * 2 , _lowercase , _lowercase , _lowercase ) , minimax(depth + 1 , node_index * 2 + 1 , _lowercase , _lowercase , _lowercase ) , )
def a_ ( ):
_UpperCamelCase : Dict = [90, 23, 6, 33, 21, 65, 123, 3_4423]
_UpperCamelCase : int = math.log(len(_lowercase ) , 2 )
print('''Optimal value : ''' , end='''''' )
print(minimax(0 , 0 , _lowercase , _lowercase , _lowercase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 128 | 1 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ) -> Tuple:
return getitem, k
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] ,_lowerCamelCase : Dict ) -> Optional[Any]:
return setitem, k, v
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> Tuple:
return delitem, k
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : str ,_lowerCamelCase : Optional[int] ,*_lowerCamelCase : str ) -> List[Any]:
try:
return fun(_lowerCamelCase ,*_lowerCamelCase ), None
except Exception as e:
return None, e
_a : Union[str, Any] = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
_a : Optional[Any] = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
_a : Dict = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
_a : Union[str, Any] = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
_a : int = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
_a : Tuple = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"""operations""" ,(
pytest.param(_add_items ,id="""add items""" ),
pytest.param(_overwrite_items ,id="""overwrite items""" ),
pytest.param(_delete_items ,id="""delete items""" ),
pytest.param(_access_absent_items ,id="""access absent items""" ),
pytest.param(_add_with_resize_up ,id="""add with resize up""" ),
pytest.param(_add_with_resize_down ,id="""add with resize down""" ),
) ,)
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] ) -> Optional[int]:
_lowerCAmelCase : Dict = HashMap(initial_block_size=4 )
_lowerCAmelCase : Optional[Any] = {}
for _, (fun, *args) in enumerate(_lowerCamelCase ):
_lowerCAmelCase , _lowerCAmelCase : List[Any] = _run_operation(_lowerCamelCase ,_lowerCamelCase ,*_lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = _run_operation(_lowerCamelCase ,_lowerCamelCase ,*_lowerCamelCase )
assert my_res == py_res
assert str(_lowerCamelCase ) == str(_lowerCamelCase )
assert set(_lowerCamelCase ) == set(_lowerCamelCase )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
assert set(my.items() ) == set(py.items() )
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
def is_public(_lowerCamelCase : List[str] ) -> bool:
return not name.startswith("""_""" )
_lowerCAmelCase : Any = {name for name in dir({} ) if is_public(_lowerCamelCase )}
_lowerCAmelCase : Dict = {name for name in dir(HashMap() ) if is_public(_lowerCamelCase )}
assert dict_public_names > hash_public_names
| 44 | """simple docstring"""
import requests
from bsa import BeautifulSoup
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = BeautifulSoup(requests.get(lowerCamelCase , params=lowerCamelCase ).content , 'html.parser' )
UpperCAmelCase__ = soup.find('div' , attrs={'class': 'gs_ri'} )
UpperCAmelCase__ = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
lowerCAmelCase__ : Optional[int] = {
'title': (
'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
'capacitance, volumetric capacitance, and energy density'
),
'journal': 'Chem. Mater.',
'volume': 30,
'pages': '3979-3990',
'year': 2_018,
'hl': 'en',
}
print(get_citation('https://scholar.google.com/scholar_lookup', params=params))
| 98 | 0 |
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 DeformableDetrImageProcessor
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase=7 , _lowerCamelCase=3 , _lowerCamelCase=30 , _lowerCamelCase=400 , _lowerCamelCase=True , _lowerCamelCase=None , _lowerCamelCase=True , _lowerCamelCase=[0.5, 0.5, 0.5] , _lowerCamelCase=[0.5, 0.5, 0.5] , _lowerCamelCase=True , _lowerCamelCase=1 / 255 , _lowerCamelCase=True , ) ->str:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
SCREAMING_SNAKE_CASE : List[str] = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
SCREAMING_SNAKE_CASE : Any = parent
SCREAMING_SNAKE_CASE : Tuple = batch_size
SCREAMING_SNAKE_CASE : Any = num_channels
SCREAMING_SNAKE_CASE : Any = min_resolution
SCREAMING_SNAKE_CASE : str = max_resolution
SCREAMING_SNAKE_CASE : str = do_resize
SCREAMING_SNAKE_CASE : List[Any] = size
SCREAMING_SNAKE_CASE : List[str] = do_normalize
SCREAMING_SNAKE_CASE : Optional[int] = image_mean
SCREAMING_SNAKE_CASE : Tuple = image_std
SCREAMING_SNAKE_CASE : Optional[int] = do_rescale
SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
SCREAMING_SNAKE_CASE : Optional[Any] = do_pad
def __lowerCAmelCase ( self ) ->Any:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase=False ) ->List[Any]:
if not batched:
SCREAMING_SNAKE_CASE : Union[str, Any] = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = image.size
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
elif w > h:
SCREAMING_SNAKE_CASE : List[Any] = self.size['''shortest_edge''']
SCREAMING_SNAKE_CASE : Union[str, Any] = int(self.size['''shortest_edge'''] * w / h )
else:
SCREAMING_SNAKE_CASE : Dict = self.size['''shortest_edge''']
SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
else:
SCREAMING_SNAKE_CASE : List[Any] = []
for image in image_inputs:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
SCREAMING_SNAKE_CASE : List[str] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a_ ( a__ , unittest.TestCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = DeformableDetrImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self ) ->Any:
SCREAMING_SNAKE_CASE : Dict = DeformableDetrImageProcessingTester(self )
@property
def __lowerCAmelCase ( self ) ->Optional[int]:
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self ) ->Dict:
SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_rescale''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_pad''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def __lowerCAmelCase ( self ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def __lowerCAmelCase ( self ) ->Optional[int]:
pass
def __lowerCAmelCase ( self ) ->Optional[int]:
# Initialize image_processing
SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
SCREAMING_SNAKE_CASE : str = image_processing(_lowerCamelCase , 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 ) ->Tuple:
# Initialize image_processing
SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE : List[str] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self ) ->Dict:
# Initialize image_processing
SCREAMING_SNAKE_CASE : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
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 ) ->Union[str, Any]:
# prepare image and target
SCREAMING_SNAKE_CASE : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
SCREAMING_SNAKE_CASE : Dict = json.loads(f.read() )
SCREAMING_SNAKE_CASE : Tuple = {'''image_id''': 3_9769, '''annotations''': target}
# encode them
SCREAMING_SNAKE_CASE : Union[str, Any] = DeformableDetrImageProcessor()
SCREAMING_SNAKE_CASE : List[str] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
SCREAMING_SNAKE_CASE : Dict = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
SCREAMING_SNAKE_CASE : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE : Tuple = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
SCREAMING_SNAKE_CASE : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
SCREAMING_SNAKE_CASE : Any = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def __lowerCAmelCase ( self ) ->Tuple:
# prepare image, target and masks_path
SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
SCREAMING_SNAKE_CASE : Tuple = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}
SCREAMING_SNAKE_CASE : List[str] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
SCREAMING_SNAKE_CASE : Optional[int] = DeformableDetrImageProcessor(format='''coco_panoptic''' )
SCREAMING_SNAKE_CASE : Tuple = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
SCREAMING_SNAKE_CASE : Tuple = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
SCREAMING_SNAKE_CASE : str = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
SCREAMING_SNAKE_CASE : List[Any] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
SCREAMING_SNAKE_CASE : Tuple = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
SCREAMING_SNAKE_CASE : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
SCREAMING_SNAKE_CASE : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
SCREAMING_SNAKE_CASE : int = 82_2873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
SCREAMING_SNAKE_CASE : Any = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 19 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a__ : Optional[Any] = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class a_ ( a__ , unittest.TestCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = XLMProphetNetTokenizer
__SCREAMING_SNAKE_CASE : List[str] = False
__SCREAMING_SNAKE_CASE : Dict = True
def __lowerCAmelCase ( self ) ->Dict:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE : Optional[Any] = XLMProphetNetTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self ) ->Tuple:
SCREAMING_SNAKE_CASE : List[str] = '''[PAD]'''
SCREAMING_SNAKE_CASE : Tuple = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase ) , _lowerCamelCase )
def __lowerCAmelCase ( self ) ->List[Any]:
SCREAMING_SNAKE_CASE : Optional[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(_lowerCamelCase ) , 1012 )
def __lowerCAmelCase ( self ) ->List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def __lowerCAmelCase ( self ) ->Optional[int]:
SCREAMING_SNAKE_CASE : Union[str, Any] = XLMProphetNetTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_lowerCamelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.convert_tokens_to_ids(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
SCREAMING_SNAKE_CASE : str = tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def __lowerCAmelCase ( self ) ->List[str]:
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def __lowerCAmelCase ( self ) ->Any:
SCREAMING_SNAKE_CASE : Union[str, Any] = '''Hello World!'''
SCREAMING_SNAKE_CASE : int = [3_5389, 6672, 49, 2]
self.assertListEqual(_lowerCamelCase , self.big_tokenizer.encode(_lowerCamelCase ) )
@slow
def __lowerCAmelCase ( self ) ->int:
# fmt: off
SCREAMING_SNAKE_CASE : str = {'''input_ids''': [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCamelCase , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 19 | 1 |
def A ( a_ ,a_ ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def A ( ) -> None:
assert or_gate(0 ,0 ) == 0
assert or_gate(0 ,1 ) == 1
assert or_gate(1 ,0 ) == 1
assert or_gate(1 ,1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 71 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
A_ :Optional[Any] = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def A ( a_ ,a_ ) -> str:
__UpperCamelCase : Any ={
'word_embeddings.weight': 'word_embeddings.weight',
'word_embeddings.norm.weight': 'word_embeddings_layernorm.weight',
'word_embeddings.norm.bias': 'word_embeddings_layernorm.bias',
'weight': 'ln_f.weight',
'bias': 'ln_f.bias',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
__UpperCamelCase : Tuple =int(re.match(r'.*layer_(\d*).*' ,a_ )[1] )
layer_number -= 3
return F'h.{layer_number}.' + key
def A ( a_ ) -> Any:
if dtype == torch.bool:
return 1 / 8
__UpperCamelCase : Dict =re.search(r'[^\d](\d+)$' ,str(a_ ) )
if bit_search is None:
raise ValueError(F'`dtype` is not a valid dtype: {dtype}.' )
__UpperCamelCase : Tuple =int(bit_search.groups()[0] )
return bit_size // 8
def A ( a_ ,a_ ,a_ ,a_ ,a_ ) -> Dict:
# Construct model
if bloom_config_file == "":
__UpperCamelCase : List[Any] =BloomConfig()
else:
__UpperCamelCase : List[str] =BloomConfig.from_json_file(a_ )
if shard_model:
__UpperCamelCase : int =os.listdir(a_ )
__UpperCamelCase : Union[str, Any] =sorted(filter(lambda a_ : s.startswith('layer' ) and "model_00" in s ,a_ ) )
__UpperCamelCase : Optional[Any] ={'weight_map': {}, 'metadata': {}}
__UpperCamelCase : Dict =0
__UpperCamelCase : int =None
__UpperCamelCase : Any =BloomConfig()
for j, file in enumerate(a_ ):
print('Processing file: {}'.format(a_ ) )
__UpperCamelCase : Optional[int] =None
for i in range(a_ ):
# load all TP files
__UpperCamelCase : Dict =file.replace('model_00' ,F'model_0{i}' )
__UpperCamelCase : Optional[Any] =torch.load(os.path.join(a_ ,a_ ) ,map_location='cpu' )
# Rename keys in the transformers names
__UpperCamelCase : int =list(temp.keys() )
for key in keys:
__UpperCamelCase : Dict =temp.pop(a_ )
if tensors is None:
__UpperCamelCase : Any =temp
else:
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
__UpperCamelCase : List[Any] =1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
__UpperCamelCase : Any =torch.cat([tensors[key], temp[key]] ,dim=a_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
__UpperCamelCase : Optional[Any] =tensors[key] / pretraining_tp
torch.save(
a_ ,os.path.join(
a_ ,'pytorch_model_{}-of-{}.bin'.format(str(j + 1 ).zfill(5 ) ,str(len(a_ ) ).zfill(5 ) ) ,) ,)
for key in tensors.keys():
__UpperCamelCase : Union[str, Any] =tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
__UpperCamelCase : int ='pytorch_model_{}-of-{}.bin'.format(
str(j + 1 ).zfill(5 ) ,str(len(a_ ) ).zfill(5 ) )
__UpperCamelCase : Union[str, Any] =BloomConfig()
__UpperCamelCase : Tuple =pytorch_dump_folder_path + '/' + CONFIG_NAME
__UpperCamelCase : Optional[int] =total_size
with open(a_ ,'w' ,encoding='utf-8' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(a_ ,WEIGHTS_NAME + '.index.json' ) ,'w' ,encoding='utf-8' ) as f:
__UpperCamelCase : List[Any] =json.dumps(a_ ,indent=2 ,sort_keys=a_ ) + '\n'
f.write(a_ )
else:
__UpperCamelCase : List[Any] =BloomModel(a_ )
__UpperCamelCase : Optional[Any] =os.listdir(a_ )
__UpperCamelCase : Dict =sorted(filter(lambda a_ : s.startswith('layer' ) and "model_00" in s ,a_ ) )
__UpperCamelCase : Any =None
for i, file in enumerate(a_ ):
__UpperCamelCase : Union[str, Any] =None
for i in range(a_ ):
# load all TP files
__UpperCamelCase : Optional[Any] =file.replace('model_00' ,F'model_0{i}' )
__UpperCamelCase : str =torch.load(os.path.join(a_ ,a_ ) ,map_location='cpu' )
# Rename keys in the transformers names
__UpperCamelCase : List[str] =list(temp.keys() )
for key in keys:
__UpperCamelCase : Union[str, Any] =temp.pop(a_ )
if tensors is None:
__UpperCamelCase : Optional[Any] =temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
__UpperCamelCase : Optional[int] =1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
__UpperCamelCase : int =torch.cat([tensors[key], temp[key]] ,dim=a_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
__UpperCamelCase : Dict =tensors[key] / pretraining_tp
__UpperCamelCase : str =model.load_state_dict(a_ ,strict=a_ )
assert not other_keys.unexpected_keys, F'The keys {other_keys.unexpected_keys} are unexpected'
if missing_keys is None:
__UpperCamelCase : str =set(other_keys.missing_keys )
else:
__UpperCamelCase : int =missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, F'The keys {missing_keys} are missing'
# Save pytorch-model
os.makedirs(a_ ,exist_ok=a_ )
__UpperCamelCase : Optional[int] =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
__UpperCamelCase : Dict =pytorch_dump_folder_path + '/' + CONFIG_NAME
print(F'Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}' )
if config.torch_dtype is not None:
__UpperCamelCase : List[str] =model.to(config.torch_dtype )
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__":
A_ :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM 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(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
A_ :str = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 71 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class SCREAMING_SNAKE_CASE__ ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({"content": datasets.Value("string" )} ) , supervised_keys=__A , )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Dict ):
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_dummy_examples()} )]
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple ):
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__A )
class SCREAMING_SNAKE_CASE__ ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def lowerCamelCase_ ( self : int ):
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) , supervised_keys=__A , )
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] ):
"""simple docstring"""
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_nested_examples()} )
]
def lowerCamelCase_ ( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : Dict ):
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__A )
def __UpperCamelCase ( ):
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"] )]
def __UpperCamelCase ( ):
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"] )]
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
@require_beam
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
__UpperCAmelCase : List[str] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
__UpperCAmelCase : Tuple = DummyBeamDataset(cache_dir=__A , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__A , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
__UpperCAmelCase : str = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , __A )
self.assertEqual(dset["train"].info.splits["train"].num_examples , __A )
self.assertDictEqual(dset["train"][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__A , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
import apache_beam as beam
__UpperCAmelCase : Any = beam.io.parquetio.WriteToParquet
__UpperCAmelCase : Any = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
__UpperCAmelCase : Any = DummyBeamDataset(cache_dir=__A , beam_runner="DirectRunner" )
with patch("apache_beam.io.parquetio.WriteToParquet" ) as write_parquet_mock:
__UpperCAmelCase : Dict = partial(__A , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
__A , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
__A , builder.name , "default" , "0.0.0" , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
__UpperCAmelCase : Dict = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , __A )
self.assertEqual(dset["train"].info.splits["train"].num_examples , __A )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["train"]["content"] ) , sorted(["foo", "bar", "foobar"] ) )
self.assertTrue(
os.path.exists(os.path.join(__A , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_cache_dir:
__UpperCAmelCase : List[str] = DummyBeamDataset(cache_dir=__A )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
__UpperCAmelCase : Dict = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
__UpperCAmelCase : int = NestedBeamDataset(cache_dir=__A , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__A , builder.name , "default" , "0.0.0" , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) )
__UpperCAmelCase : List[str] = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , __A )
self.assertEqual(dset["train"].info.splits["train"].num_examples , __A )
self.assertDictEqual(dset["train"][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__A , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
| 360 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
__UpperCAmelCase : Optional[int] = Github(os.environ["GITHUB_TOKEN"] )
__UpperCAmelCase : Union[str, Any] = g.get_repo("huggingface/transformers" )
__UpperCAmelCase : Union[str, Any] = repo.get_issues(state="open" )
for issue in open_issues:
__UpperCAmelCase : int = sorted([comment for comment in issue.get_comments()], key=lambda _UpperCAmelCase : i.created_at, reverse=_UpperCAmelCase )
__UpperCAmelCase : Any = comments[0] if len(_UpperCAmelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="closed" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 37 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowercase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def _snake_case ( snake_case__ : int ):
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def _snake_case ( snake_case__ : Optional[Any] ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__ )
def _snake_case ( snake_case__ : Optional[int] ):
from transformers.testing_utils import pytest_terminal_summary_main
A = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__ )
def _snake_case ( snake_case__ : int , snake_case__ : List[str] ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
A = 0
# Doctest custom flag to ignore output.
_lowercase = doctest.register_optionflag('''IGNORE_RESULT''')
_lowercase = doctest.OutputChecker
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : List[Any] ,A_ : Optional[Any] ,A_ : str ) -> Any:
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,A_ ,A_ ,A_ )
_lowercase = CustomOutputChecker
_lowercase = HfDoctestModule
_lowercase = HfDocTestParser | 74 |
"""simple docstring"""
from string import ascii_uppercase
_lowercase = {char: i for i, char in enumerate(ascii_uppercase)}
_lowercase = dict(enumerate(ascii_uppercase))
def _snake_case ( snake_case__ : str , snake_case__ : str ):
A = len(snake_case__ )
A = 0
while True:
if x == i:
A = 0
if len(snake_case__ ) == len(snake_case__ ):
break
key += key[i]
i += 1
return key
def _snake_case ( snake_case__ : str , snake_case__ : str ):
A = ''
A = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
A = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def _snake_case ( snake_case__ : str , snake_case__ : str ):
A = ''
A = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
A = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def _snake_case ( ):
A = 'THE GERMAN ATTACK'
A = 'SECRET'
A = generate_key(snake_case__ , snake_case__ )
A = cipher_text(snake_case__ , snake_case__ )
print(F'Encrypted Text = {s}' )
print(F'Original Text = {original_text(snake_case__ , snake_case__ )}' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 74 | 1 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 100 ):
"""simple docstring"""
a :Union[str, Any] = n * (n + 1) * (2 * n + 1) / 6
a :List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 358 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case : List[str] = logging.get_logger(__name__)
snake_case : Optional[Any] = {
'''vocab_file''': '''vocab.txt''',
'''merges_file''': '''bpe.codes''',
}
snake_case : str = {
'''vocab_file''': {
'''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
'''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
},
'''merges_file''': {
'''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
'''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
},
}
snake_case : List[Any] = {
'''vinai/phobert-base''': 2_56,
'''vinai/phobert-large''': 2_56,
}
def __lowerCamelCase ( UpperCAmelCase_ : List[str] ):
"""simple docstring"""
a :Union[str, Any] = set()
a :str = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
a :Optional[int] = char
a :Optional[int] = set(UpperCAmelCase_ )
return pairs
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<mask>" , **_lowerCamelCase , ):
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , **_lowerCamelCase , )
a :Optional[Any] = vocab_file
a :Optional[Any] = merges_file
a :Any = {}
a :Any = 0
a :int = 1
a :Union[str, Any] = 2
a :List[Any] = 3
self.add_from_file(_lowerCamelCase )
a :List[str] = {v: k for k, v in self.encoder.items()}
with open(_lowerCamelCase , encoding='''utf-8''' ) as merges_handle:
a :List[str] = merges_handle.read().split('''\n''' )[:-1]
a :Any = [tuple(merge.split()[:-1] ) for merge in merges]
a :str = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
a :str = {}
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
a :Union[str, Any] = [self.cls_token_id]
a :Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(_lowerCamelCase )) + [1]
return [1] + ([0] * len(_lowerCamelCase )) + [1, 1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None ):
a :Optional[int] = [self.sep_token_id]
a :Optional[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 + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE__ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
if token in self.cache:
return self.cache[token]
a :Optional[int] = tuple(_lowerCamelCase )
a :List[str] = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
a :Union[str, Any] = get_pairs(_lowerCamelCase )
if not pairs:
return token
while True:
a :Optional[Any] = min(_lowerCamelCase , key=lambda _lowerCamelCase : self.bpe_ranks.get(_lowerCamelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
a , a :Dict = bigram
a :Union[str, Any] = []
a :int = 0
while i < len(_lowerCamelCase ):
try:
a :Optional[Any] = word.index(_lowerCamelCase , _lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
a :Union[str, Any] = j
if word[i] == first and i < len(_lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
a :Union[str, Any] = tuple(_lowerCamelCase )
a :int = new_word
if len(_lowerCamelCase ) == 1:
break
else:
a :List[str] = get_pairs(_lowerCamelCase )
a :Union[str, Any] = '''@@ '''.join(_lowerCamelCase )
a :Dict = word[:-4]
a :Any = word
return word
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
a :Union[str, Any] = []
a :str = re.findall(R'''\S+\n?''' , _lowerCamelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCamelCase ).split(''' ''' ) ) )
return split_tokens
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
return self.encoder.get(_lowerCamelCase , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
return self.decoder.get(_lowerCamelCase , self.unk_token )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
a :Optional[int] = ''' '''.join(_lowerCamelCase ).replace('''@@ ''' , '''''' ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a :Tuple = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
a :Optional[int] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.vocab_file , _lowerCamelCase )
if os.path.abspath(self.merges_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.merges_file , _lowerCamelCase )
return out_vocab_file, out_merge_file
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
if isinstance(_lowerCamelCase , _lowerCamelCase ):
try:
with open(_lowerCamelCase , '''r''' , encoding='''utf-8''' ) as fd:
self.add_from_file(_lowerCamelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(F'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
a :str = f.readlines()
for lineTmp in lines:
a :Tuple = lineTmp.strip()
a :int = line.rfind(''' ''' )
if idx == -1:
raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' )
a :Tuple = line[:idx]
a :Tuple = len(self.encoder )
| 281 | 0 |
UpperCAmelCase : List[str] ={0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase : Any ={0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
UpperCamelCase_ = True
UpperCamelCase_ = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase)
order.append(_lowerCAmelCase)
return order
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
UpperCamelCase_ = True
UpperCamelCase_ = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase)
return component
def _lowerCAmelCase (_lowerCAmelCase):
UpperCamelCase_ = len(_lowerCAmelCase) * [False]
UpperCamelCase_ = {vert: [] for vert in range(len(_lowerCAmelCase))}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(_lowerCAmelCase)
UpperCamelCase_ = []
for i, was_visited in enumerate(_lowerCAmelCase):
if not was_visited:
order += topology_sort(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase)
UpperCamelCase_ = []
UpperCamelCase_ = len(_lowerCAmelCase) * [False]
for i in range(len(_lowerCAmelCase)):
UpperCamelCase_ = order[len(_lowerCAmelCase) - i - 1]
if not visited[vert]:
UpperCamelCase_ = find_components(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase)
components_list.append(_lowerCAmelCase)
return components_list
| 128 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase : Dict =TypeVar("""T""")
class _lowercase (Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = data
UpperCamelCase_ = None
def __str__( self ):
'''simple docstring'''
return F"""{self.data}"""
class _lowercase (Generic[T] ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
UpperCamelCase_ = None
def __iter__( self ):
'''simple docstring'''
UpperCamelCase_ = self.top
while node:
yield node.data
UpperCamelCase_ = node.next
def __str__( self ):
'''simple docstring'''
return "->".join([str(snake_case__ ) for item in self] )
def __len__( self ):
'''simple docstring'''
return len(tuple(iter(self ) ) )
def _lowerCamelCase ( self ):
'''simple docstring'''
return self.top is None
def _lowerCamelCase ( self , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = Node(snake_case__ )
if not self.is_empty():
UpperCamelCase_ = self.top
UpperCamelCase_ = node
def _lowerCamelCase ( self ):
'''simple docstring'''
if self.is_empty():
raise IndexError("pop from empty stack" )
assert isinstance(self.top , snake_case__ )
UpperCamelCase_ = self.top
UpperCamelCase_ = self.top.next
return pop_node.data
def _lowerCamelCase ( self ):
'''simple docstring'''
if self.is_empty():
raise IndexError("peek from empty stack" )
assert self.top is not None
return self.top.data
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 128 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
snake_case_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __init__(self : List[Any] , a__ : int , a__ : Tuple ):
"""simple docstring"""
super().__init__()
self.register_modules(unet=a__ , scheduler=a__ )
@torch.no_grad()
def __call__(self : Any , a__ : int = 1 , a__ : int = 100 , a__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , a__ : Optional[float] = None , a__ : bool = True , ):
"""simple docstring"""
if audio_length_in_s is None:
__snake_case = self.unet.config.sample_size / self.unet.config.sample_rate
__snake_case = audio_length_in_s * self.unet.config.sample_rate
__snake_case = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )
__snake_case = int(a__ )
if sample_size % down_scale_factor != 0:
__snake_case = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
''' process.''' )
__snake_case = int(a__ )
__snake_case = next(iter(self.unet.parameters() ) ).dtype
__snake_case = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(a__ , a__ ) and len(a__ ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(a__ )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
__snake_case = randn_tensor(a__ , generator=a__ , device=self.device , dtype=a__ )
# set step values
self.scheduler.set_timesteps(a__ , device=audio.device )
__snake_case = self.scheduler.timesteps.to(a__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
__snake_case = self.unet(a__ , a__ ).sample
# 2. compute previous image: x_t -> t_t-1
__snake_case = self.scheduler.step(a__ , a__ , a__ ).prev_sample
__snake_case = audio.clamp(-1 , 1 ).float().cpu().numpy()
__snake_case = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=a__ )
| 238 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class SCREAMING_SNAKE_CASE__ ( TensorFormatter[Mapping, 'torch.Tensor', Mapping] ):
def __init__(self : Optional[int] , a__ : Tuple=None , **a__ : Optional[int] ):
"""simple docstring"""
super().__init__(features=a__ )
__snake_case = torch_tensor_kwargs
import torch # noqa import torch at initialization
def a (self : Union[str, Any] , a__ : Union[str, Any] ):
"""simple docstring"""
import torch
if isinstance(a__ , a__ ) and column:
if all(
isinstance(a__ , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(a__ )
return column
def a (self : Optional[Any] , a__ : str ):
"""simple docstring"""
import torch
if isinstance(a__ , (str, bytes, type(a__ )) ):
return value
elif isinstance(a__ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
__snake_case = {}
if isinstance(a__ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
__snake_case = {'''dtype''': torch.intaa}
elif isinstance(a__ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
__snake_case = {'''dtype''': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a__ , PIL.Image.Image ):
__snake_case = np.asarray(a__ )
return torch.tensor(a__ , **{**default_dtype, **self.torch_tensor_kwargs} )
def a (self : Optional[int] , a__ : str ):
"""simple docstring"""
import torch
# support for torch, tf, jax etc.
if hasattr(a__ , '''__array__''' ) and not isinstance(a__ , torch.Tensor ):
__snake_case = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a__ , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(a__ ) for substruct in data_struct] )
elif isinstance(a__ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(a__ ) for substruct in data_struct] )
return self._tensorize(a__ )
def a (self : Optional[Any] , a__ : dict ):
"""simple docstring"""
return map_nested(self._recursive_tensorize , a__ , map_list=a__ )
def a (self : Optional[int] , a__ : pa.Table ):
"""simple docstring"""
__snake_case = self.numpy_arrow_extractor().extract_row(a__ )
__snake_case = self.python_features_decoder.decode_row(a__ )
return self.recursive_tensorize(a__ )
def a (self : List[Any] , a__ : pa.Table ):
"""simple docstring"""
__snake_case = self.numpy_arrow_extractor().extract_column(a__ )
__snake_case = self.python_features_decoder.decode_column(a__ , pa_table.column_names[0] )
__snake_case = self.recursive_tensorize(a__ )
__snake_case = self._consolidate(a__ )
return column
def a (self : Optional[int] , a__ : pa.Table ):
"""simple docstring"""
__snake_case = self.numpy_arrow_extractor().extract_batch(a__ )
__snake_case = self.python_features_decoder.decode_batch(a__ )
__snake_case = self.recursive_tensorize(a__ )
for column_name in batch:
__snake_case = self._consolidate(batch[column_name] )
return batch
| 238 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__A ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A ={
'''vocab_file''': {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt''',
},
'''tokenizer_file''': {
'''unc-nlp/lxmert-base-uncased''': (
'''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'''
),
},
}
__A ={
'''unc-nlp/lxmert-base-uncased''': 5_1_2,
}
__A ={
'''unc-nlp/lxmert-base-uncased''': {'''do_lower_case''': True},
}
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = LxmertTokenizer
def __init__( self , lowercase=None , lowercase=None , lowercase=True , lowercase="[UNK]" , lowercase="[SEP]" , lowercase="[PAD]" , lowercase="[CLS]" , lowercase="[MASK]" , lowercase=True , lowercase=None , **lowercase , ) -> Dict:
super().__init__(
lowercase , tokenizer_file=lowercase , do_lower_case=lowercase , unk_token=lowercase , sep_token=lowercase , pad_token=lowercase , cls_token=lowercase , mask_token=lowercase , tokenize_chinese_chars=lowercase , strip_accents=lowercase , **lowercase , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , lowercase ) != do_lower_case
or normalizer_state.get("strip_accents" , lowercase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , lowercase ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(lowercase , normalizer_state.pop("type" ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**lowercase )
lowerCamelCase_ = do_lower_case
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=None ) -> Union[str, Any]:
lowerCamelCase_ = [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 SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> List[int]:
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> Tuple[str]:
lowerCamelCase_ = self._tokenizer.model.save(lowercase , name=lowercase )
return tuple(lowercase )
| 19 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WavLMForAudioFrameClassification''',
'''WavLMForCTC''',
'''WavLMForSequenceClassification''',
'''WavLMForXVector''',
'''WavLMModel''',
'''WavLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
__A =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 19 | 1 |
import math
_A = 10
_A = 7
_A = BALLS_PER_COLOUR * NUM_COLOURS
def __UpperCamelCase ( _A = 20 ):
lowerCAmelCase_ = math.comb(_A , _A )
lowerCAmelCase_ = math.comb(NUM_BALLS - BALLS_PER_COLOUR , _A )
lowerCAmelCase_ = NUM_COLOURS * (1 - missing_colour / total)
return f"{result:.9f}"
if __name__ == "__main__":
print(solution(20))
| 167 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
_A = logging.get_logger(__name__)
_A = TypeVar('''DatasetType''', Dataset, IterableDataset)
def __UpperCamelCase ( _A , _A = None , _A = None , _A = None , _A = None , _A = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_A ):
if not isinstance(_A , (Dataset, IterableDataset) ):
if isinstance(_A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
f"Dataset at position {i} has at least one split: {list(_A )}\n"
f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_A ) )}']" )
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_A ).__name__}." )
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ = (
(Dataset, IterableDataset) if isinstance(_A , _A ) else (IterableDataset, Dataset)
)
elif not isinstance(_A , _A ):
raise ValueError(
f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_A , _A , _A , info=_A , split=_A , stopping_strategy=_A )
else:
return _interleave_iterable_datasets(
_A , _A , _A , info=_A , split=_A , stopping_strategy=_A )
def __UpperCamelCase ( _A , _A = None , _A = None , _A = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_A ):
if not isinstance(_A , (Dataset, IterableDataset) ):
if isinstance(_A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
f"Dataset at position {i} has at least one split: {list(_A )}\n"
f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_A ) )}']" )
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_A ).__name__}." )
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ = (
(Dataset, IterableDataset) if isinstance(_A , _A ) else (IterableDataset, Dataset)
)
elif not isinstance(_A , _A ):
raise ValueError(
f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_A , info=_A , split=_A , axis=_A )
else:
return _concatenate_iterable_datasets(_A , info=_A , split=_A , axis=_A )
| 167 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
snake_case__ = MvpTokenizer
snake_case__ = MvpTokenizerFast
snake_case__ = True
snake_case__ = filter_roberta_detectors
def __lowerCAmelCase ( self : Dict ):
super().setUp()
UpperCAmelCase__ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
UpperCAmelCase__ = dict(zip(lowerCamelCase__ ,range(len(lowerCamelCase__ ) ) ) )
UpperCAmelCase__ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
UpperCAmelCase__ = {'unk_token': '<unk>'}
UpperCAmelCase__ = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['vocab_file'] )
UpperCAmelCase__ = 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 : Union[str, Any] ,**lowerCamelCase__ : List[Any] ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCamelCase__ )
def __lowerCAmelCase ( self : str ,**lowerCamelCase__ : Optional[Any] ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCamelCase__ )
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : List[Any] ):
return "lower newer", "lower newer"
@cached_property
def __lowerCAmelCase ( self : Optional[int] ):
return MvpTokenizer.from_pretrained('RUCAIBox/mvp' )
@cached_property
def __lowerCAmelCase ( self : Dict ):
return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp' )
@require_torch
def __lowerCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase__ = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
UpperCAmelCase__ = [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ = tokenizer(lowerCamelCase__ ,max_length=len(lowerCamelCase__ ) ,padding=lowerCamelCase__ ,return_tensors='pt' )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
UpperCAmelCase__ = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
# Test that special tokens are reset
@require_torch
def __lowerCAmelCase ( self : Tuple ):
UpperCAmelCase__ = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ = tokenizer(lowerCamelCase__ ,padding=lowerCamelCase__ ,return_tensors='pt' )
# check if input_ids are returned and no labels
self.assertIn('input_ids' ,lowerCamelCase__ )
self.assertIn('attention_mask' ,lowerCamelCase__ )
self.assertNotIn('labels' ,lowerCamelCase__ )
self.assertNotIn('decoder_attention_mask' ,lowerCamelCase__ )
@require_torch
def __lowerCAmelCase ( self : Union[str, Any] ):
UpperCAmelCase__ = [
'Summary of the text.',
'Another summary.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ = tokenizer(text_target=lowerCamelCase__ ,max_length=32 ,padding='max_length' ,return_tensors='pt' )
self.assertEqual(32 ,targets['input_ids'].shape[1] )
@require_torch
def __lowerCAmelCase ( self : List[str] ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ = tokenizer(
['I am a small frog' * 1_024, 'I am a small frog'] ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ ,return_tensors='pt' )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
self.assertEqual(batch.input_ids.shape ,(2, 1_024) )
@require_torch
def __lowerCAmelCase ( self : Dict ):
UpperCAmelCase__ = ['A long paragraph for summarization.']
UpperCAmelCase__ = [
'Summary of the text.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase__ = tokenizer(lowerCamelCase__ ,text_target=lowerCamelCase__ ,return_tensors='pt' )
UpperCAmelCase__ = inputs['input_ids']
UpperCAmelCase__ = inputs['labels']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def __lowerCAmelCase ( self : Any ):
pass
def __lowerCAmelCase ( self : Tuple ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
UpperCAmelCase__ = self.tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
UpperCAmelCase__ = 'A, <mask> AllenNLP sentence.'
UpperCAmelCase__ = tokenizer_r.encode_plus(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,return_token_type_ids=lowerCamelCase__ )
UpperCAmelCase__ = tokenizer_p.encode_plus(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,return_token_type_ids=lowerCamelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ) ,sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) ,sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) ,)
UpperCAmelCase__ = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
UpperCAmelCase__ = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'] ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowerCamelCase__ ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
lowerCamelCase__ ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
| 98 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_lowerCAmelCase = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
'''
_lowerCAmelCase = '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''
_lowerCAmelCase = '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for \'record\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'prediction_text\': the predicted answer text
- for \'multirc\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question-answer pair as specified by the dataset
- \'prediction\': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for \'record\': list of question-answers dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'answers\': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for \'record\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1\': F1 score
- for \'multirc\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1_m\': Per-question macro-F1 score
- \'f1_a\': Average F1 score over all answers
- for \'axb\':
\'matthews_correlation\': Matthew Correlation
- for \'cb\':
- \'accuracy\': Accuracy
- \'f1\': F1 score
- for all others:
- \'accuracy\': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
>>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
>>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
>>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
return float((preds == labels).mean() )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase="binary" ):
"""simple docstring"""
lowerCAmelCase__ : Any = simple_accuracy(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Tuple = float(fa_score(y_true=UpperCamelCase , y_pred=UpperCamelCase , average=UpperCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = {}
for id_pred, label in zip(UpperCamelCase , UpperCamelCase ):
lowerCAmelCase__ : str = f"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
lowerCAmelCase__ : Dict = id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
lowerCAmelCase__ : Optional[int] = [(pred, label)]
lowerCAmelCase__ , lowerCAmelCase__ : int = [], []
for question, preds_labels in question_map.items():
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = zip(*UpperCamelCase )
lowerCAmelCase__ : List[Any] = fa_score(y_true=UpperCamelCase , y_pred=UpperCamelCase , average="""macro""" )
fas.append(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = int(sum(pred == label for pred, label in preds_labels ) == len(UpperCamelCase ) )
ems.append(UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = float(sum(UpperCamelCase ) / len(UpperCamelCase ) )
lowerCAmelCase__ : List[Any] = sum(UpperCamelCase ) / len(UpperCamelCase )
lowerCAmelCase__ : Dict = float(fa_score(y_true=UpperCamelCase , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> Optional[Any]:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(self._get_feature_types() ) ,codebase_urls=[] ,reference_urls=[] ,format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None ,)
def UpperCAmelCase_ ( self ) -> str:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> Any:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(__UpperCAmelCase ,__UpperCAmelCase )}
elif self.config_name == "cb":
return acc_and_fa(__UpperCAmelCase ,__UpperCAmelCase ,fa_avg="""macro""" )
elif self.config_name == "record":
lowerCAmelCase__ : Optional[Any] = [
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
lowerCAmelCase__ : Union[str, Any] = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(__UpperCAmelCase ,__UpperCAmelCase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(__UpperCAmelCase ,__UpperCAmelCase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(__UpperCAmelCase ,__UpperCAmelCase )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 37 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
A = True
except (ImportError, ModuleNotFoundError):
A = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def __A ( a_ :str) -> str:
re.sub('''<n>''' , '''''' , a_) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(a_)) | 188 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A = '''src/transformers'''
A = '''docs/source/en/tasks'''
def __A ( a_ :List[Any] , a_ :List[Any] , a_ :List[str]) -> List[str]:
with open(a_ , '''r''' , encoding='''utf-8''' , newline='''\n''') as f:
__a : List[str] = f.readlines()
# Find the start prompt.
__a : Optional[Any] = 0
while not lines[start_index].startswith(a_):
start_index += 1
start_index += 1
__a : int = start_index
while not lines[end_index].startswith(a_):
end_index += 1
end_index -= 1
while len(lines[start_index]) <= 1:
start_index += 1
while len(lines[end_index]) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index]), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
A = direct_transformers_import(TRANSFORMERS_PATH)
A = {
'''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A = {
'''summarization.md''': ('''nllb''',),
'''translation.md''': ('''nllb''',),
}
def __A ( a_ :Optional[Any]) -> Any:
__a : List[Any] = TASK_GUIDE_TO_MODELS[task_guide]
__a : List[Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_ , set())
__a : Any = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]) + "\n"
def __A ( a_ :Optional[int] , a_ :Dict=False) -> Any:
__a , __a , __a , __a : Any = _find_text_in_file(
filename=os.path.join(a_ , a_) , start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->''' , end_prompt='''<!--End of the generated tip-->''' , )
__a : Optional[Any] = get_model_list_for_task(a_)
if current_list != new_list:
if overwrite:
with open(os.path.join(a_ , a_) , '''w''' , encoding='''utf-8''' , newline='''\n''') as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
else:
raise ValueError(
F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
''' to fix this.''')
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
A = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite) | 188 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowerCamelCase : int = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : str ) -> Dict:
"""simple docstring"""
inspect_dataset(_snake_case , _snake_case )
SCREAMING_SNAKE_CASE__ = path + ".py"
assert script_name in os.listdir(_snake_case )
assert "__pycache__" not in os.listdir(_snake_case )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : Any ) -> Optional[int]:
"""simple docstring"""
inspect_metric(_snake_case , _snake_case )
SCREAMING_SNAKE_CASE__ = path + ".py"
assert script_name in os.listdir(_snake_case )
assert "__pycache__" not in os.listdir(_snake_case )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : str ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = get_dataset_config_info(_snake_case , config_name=_snake_case )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : int ) -> int:
"""simple docstring"""
with pytest.raises(_snake_case ):
get_dataset_config_info(_snake_case , config_name=_snake_case )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = get_dataset_config_names(_snake_case )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : List[str] , __UpperCamelCase : int ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = get_dataset_infos(_snake_case )
assert list(infos.keys() ) == expected_configs
SCREAMING_SNAKE_CASE__ = expected_configs[0]
assert expected_config in infos
SCREAMING_SNAKE_CASE__ = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : Dict , __UpperCamelCase : int ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = get_dataset_infos(_snake_case )
assert expected_config in infos
SCREAMING_SNAKE_CASE__ = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Any ) -> Tuple:
"""simple docstring"""
with pytest.raises(_snake_case ):
get_dataset_split_names(_snake_case , config_name=_snake_case )
| 219 |
import math
def lowerCAmelCase_ ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
return math.pow(_snake_case , 2 ) - a
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
return 2 * x
def lowerCAmelCase_ ( _snake_case : float ) -> float:
'''simple docstring'''
__magic_name__ : Optional[int] = 2.0
while start <= a:
__magic_name__ : str = math.pow(_snake_case , 2 )
return start
def lowerCAmelCase_ ( _snake_case : float , _snake_case : int = 9999 , _snake_case : float = 0.00_000_000_000_001 ) -> float:
'''simple docstring'''
if a < 0:
raise ValueError("math domain error" )
__magic_name__ : Optional[int] = get_initial_point(_snake_case )
for _ in range(_snake_case ):
__magic_name__ : int = value
__magic_name__ : str = value - fx(_snake_case , _snake_case ) / fx_derivative(_snake_case )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 281 | 0 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
snake_case = False
snake_case = True
snake_case = False
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--repo_path""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the architecture.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
snake_case = parser.parse_args()
snake_case = {
"""image_size""": """sample_size""",
"""num_res_blocks""": """layers_per_block""",
"""block_channels""": """block_out_channels""",
"""down_blocks""": """down_block_types""",
"""up_blocks""": """up_block_types""",
"""downscale_freq_shift""": """freq_shift""",
"""resnet_num_groups""": """norm_num_groups""",
"""resnet_act_fn""": """act_fn""",
"""resnet_eps""": """norm_eps""",
"""num_head_channels""": """attention_head_dim""",
}
snake_case = {
"""time_steps""": """time_proj""",
"""mid""": """mid_block""",
"""downsample_blocks""": """down_blocks""",
"""upsample_blocks""": """up_blocks""",
}
snake_case = """""" if has_file(args.repo_path, """config.json""") else """unet"""
with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
snake_case = reader.read()
snake_case = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, """config.json"""):
snake_case = UNetaDModel(**config)
else:
snake_case = UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
snake_case = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
snake_case = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
snake_case = config[key]
del config[key]
snake_case = [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
snake_case = [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]
if do_only_weights:
snake_case = torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))
snake_case = {}
for param_key, param_value in state_dict.items():
if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
continue
snake_case = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split(""".""")[0] == key:
snake_case = param_value
snake_case = True
if not has_changed:
snake_case = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 319 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
snake_case = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
print("""Torch version:""", None)
try:
import transformers
print("""transformers version:""", transformers.__version__)
except ImportError:
print("""transformers version:""", None)
| 319 | 1 |
"""simple docstring"""
# Imports
import numpy as np
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Any, lowerCamelCase : List[Any]=None, lowerCamelCase : Union[str, Any]=None, lowerCamelCase : Dict=None, lowerCamelCase : Union[str, Any]=None, lowerCamelCase : List[Any]=None )-> Tuple:
self.set_matricies(red=lowerCamelCase, green=lowerCamelCase, blue=lowerCamelCase, red_edge=lowerCamelCase, nir=lowerCamelCase )
def snake_case ( self : Tuple, lowerCamelCase : Dict=None, lowerCamelCase : Optional[Any]=None, lowerCamelCase : Tuple=None, lowerCamelCase : int=None, lowerCamelCase : int=None )-> Optional[Any]:
if red is not None:
lowerCamelCase__ : Union[str, Any] =red
if green is not None:
lowerCamelCase__ : Any =green
if blue is not None:
lowerCamelCase__ : str =blue
if red_edge is not None:
lowerCamelCase__ : List[str] =red_edge
if nir is not None:
lowerCamelCase__ : str =nir
return True
def snake_case ( self : Dict, lowerCamelCase : List[str]="", lowerCamelCase : Optional[int]=None, lowerCamelCase : Dict=None, lowerCamelCase : List[Any]=None, lowerCamelCase : Dict=None, lowerCamelCase : Union[str, Any]=None )-> Union[str, Any]:
self.set_matricies(red=lowerCamelCase, green=lowerCamelCase, blue=lowerCamelCase, red_edge=lowerCamelCase, nir=lowerCamelCase )
lowerCamelCase__ : Dict ={
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''' )
return False
def snake_case ( self : Optional[Any] )-> List[Any]:
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def snake_case ( self : Optional[int] )-> Any:
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def snake_case ( self : List[str] )-> Union[str, Any]:
return self.nir * (self.red / (self.green**2))
def snake_case ( self : Union[str, Any] )-> Optional[Any]:
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def snake_case ( self : Optional[Any] )-> List[str]:
return (self.nir - self.red) / (self.nir + self.red)
def snake_case ( self : Union[str, Any] )-> int:
return (self.nir - self.blue) / (self.nir + self.blue)
def snake_case ( self : List[str] )-> Optional[int]:
return (self.redEdge - self.red) / (self.redEdge + self.red)
def snake_case ( self : List[str] )-> Dict:
return (self.nir - self.green) / (self.nir + self.green)
def snake_case ( self : Tuple )-> Dict:
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def snake_case ( self : int )-> str:
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def snake_case ( self : Tuple )-> str:
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def snake_case ( self : Union[str, Any] )-> str:
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def snake_case ( self : Dict, lowerCamelCase : str=0.08, lowerCamelCase : Dict=1.22, lowerCamelCase : List[Any]=0.03 )-> Optional[int]:
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def snake_case ( self : Optional[Any] )-> Union[str, Any]:
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def snake_case ( self : Optional[Any] )-> Dict:
return (self.nir / self.green) - 1
def snake_case ( self : Any )-> Any:
return (self.nir / self.redEdge) - 1
def snake_case ( self : List[Any] )-> Dict:
return (self.red - self.blue) / self.red
def snake_case ( self : Union[str, Any] )-> Tuple:
lowerCamelCase__ : List[str] =self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def snake_case ( self : Tuple )-> str:
return self.nir - self.green
def snake_case ( self : List[Any] )-> Any:
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def snake_case ( self : Tuple )-> Tuple:
lowerCamelCase__ : Optional[int] =(2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def snake_case ( self : Union[str, Any], lowerCamelCase : int=0.16 )-> str:
return (self.nir - self.green) / (self.nir + self.green + y)
def snake_case ( self : Optional[int], lowerCamelCase : List[str]=0.5 )-> Optional[Any]:
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def snake_case ( self : str )-> Tuple:
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def snake_case ( self : Dict, lowerCamelCase : Dict=None, lowerCamelCase : Optional[int]=None )-> str:
return (self.nir - b) / (a * self.red)
def snake_case ( self : Optional[Any] )-> List[Any]:
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def snake_case ( self : Any )-> List[Any]:
return (self.red + self.green + self.blue) / 30.5
def snake_case ( self : Union[str, Any] )-> List[str]:
return self.nir / self.red
def snake_case ( self : int )-> List[Any]:
return (self.rvi() - 1) / (self.rvi() + 1)
def snake_case ( self : int )-> Tuple:
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def snake_case ( self : int )-> Any:
return self.green / (self.nir + self.red + self.green)
def snake_case ( self : str )-> Optional[int]:
return self.nir / (self.nir + self.red + self.green)
def snake_case ( self : List[str] )-> Optional[Any]:
return self.red / (self.nir + self.red + self.green)
def snake_case ( self : Optional[Any] )-> List[str]:
return (self.green - self.red) / (self.green + self.red)
def snake_case ( self : Optional[Any] )-> List[Any]:
return (self.red - self.green) / (self.red + self.green)
def snake_case ( self : Dict )-> List[str]:
lowerCamelCase__ : List[str] =np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
lowerCamelCase__ : Union[str, Any] =np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def snake_case ( self : List[str] )-> Optional[Any]:
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def snake_case ( self : str )-> Optional[Any]:
return self.nir / self.red
def snake_case ( self : Optional[int] )-> Optional[int]:
return (self.ndvi() + 0.5) ** (1 / 2)
def snake_case ( self : Optional[Any] )-> Tuple:
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 238 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
_lowercase : List[str] = logging.get_logger(__name__)
_lowercase : int = {
"microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
"microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
"microsoft/deberta-v2-xlarge-mnli": (
"https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
),
"microsoft/deberta-v2-xxlarge-mnli": (
"https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
),
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = 'deberta-v2'
def __init__( self : Optional[Any], lowerCamelCase : Optional[int]=12_8100, lowerCamelCase : List[Any]=1536, lowerCamelCase : Dict=24, lowerCamelCase : Any=24, lowerCamelCase : Union[str, Any]=6144, lowerCamelCase : List[Any]="gelu", lowerCamelCase : int=0.1, lowerCamelCase : Union[str, Any]=0.1, lowerCamelCase : Union[str, Any]=512, lowerCamelCase : Optional[Any]=0, lowerCamelCase : Any=0.02, lowerCamelCase : int=1E-7, lowerCamelCase : Union[str, Any]=False, lowerCamelCase : Union[str, Any]=-1, lowerCamelCase : Tuple=0, lowerCamelCase : Union[str, Any]=True, lowerCamelCase : int=None, lowerCamelCase : Dict=0, lowerCamelCase : Tuple="gelu", **lowerCamelCase : Optional[int], )-> Union[str, Any]:
super().__init__(**lowerCamelCase )
lowerCamelCase__ : str =hidden_size
lowerCamelCase__ : Optional[int] =num_hidden_layers
lowerCamelCase__ : Optional[Any] =num_attention_heads
lowerCamelCase__ : List[Any] =intermediate_size
lowerCamelCase__ : int =hidden_act
lowerCamelCase__ : Tuple =hidden_dropout_prob
lowerCamelCase__ : Union[str, Any] =attention_probs_dropout_prob
lowerCamelCase__ : Optional[Any] =max_position_embeddings
lowerCamelCase__ : int =type_vocab_size
lowerCamelCase__ : Tuple =initializer_range
lowerCamelCase__ : Tuple =relative_attention
lowerCamelCase__ : Optional[Any] =max_relative_positions
lowerCamelCase__ : List[Any] =pad_token_id
lowerCamelCase__ : int =position_biased_input
# Backwards compatibility
if type(lowerCamelCase ) == str:
lowerCamelCase__ : Union[str, Any] =[x.strip() for x in pos_att_type.lower().split('''|''' )]
lowerCamelCase__ : Tuple =pos_att_type
lowerCamelCase__ : Union[str, Any] =vocab_size
lowerCamelCase__ : Optional[int] =layer_norm_eps
lowerCamelCase__ : Dict =kwargs.get('''pooler_hidden_size''', lowerCamelCase )
lowerCamelCase__ : Tuple =pooler_dropout
lowerCamelCase__ : List[Any] =pooler_hidden_act
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
@property
def snake_case ( self : List[str] )-> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCamelCase__ : Union[str, Any] ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCamelCase__ : Any ={0: '''batch''', 1: '''sequence'''}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)] )
else:
return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)] )
@property
def snake_case ( self : List[str] )-> int:
return 12
def snake_case ( self : str, lowerCamelCase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], lowerCamelCase : int = -1, lowerCamelCase : int = -1, lowerCamelCase : int = -1, lowerCamelCase : bool = False, lowerCamelCase : Optional["TensorType"] = None, lowerCamelCase : int = 3, lowerCamelCase : int = 40, lowerCamelCase : int = 40, lowerCamelCase : "PreTrainedTokenizerBase" = None, )-> Mapping[str, Any]:
lowerCamelCase__ : List[Any] =super().generate_dummy_inputs(preprocessor=lowerCamelCase, framework=lowerCamelCase )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 238 | 1 |
"""simple docstring"""
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = [False] * len(__lowerCAmelCase )
UpperCAmelCase = [-1] * len(__lowerCAmelCase )
def dfs(_snake_case , _snake_case ):
UpperCAmelCase = True
UpperCAmelCase = c
for u in graph[v]:
if not visited[u]:
dfs(__lowerCAmelCase , 1 - c )
for i in range(len(__lowerCAmelCase ) ):
if not visited[i]:
dfs(__lowerCAmelCase , 0 )
for i in range(len(__lowerCAmelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
_UpperCamelCase = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 362 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case , _snake_case = None , _snake_case = None ):
"""simple docstring"""
if start is None:
UpperCAmelCase = 0
if end is None:
UpperCAmelCase = len(_snake_case ) - 1
if start >= end:
return
UpperCAmelCase = (start + end) // 2
slowsort(_snake_case , _snake_case , _snake_case )
slowsort(_snake_case , mid + 1 , _snake_case )
if sequence[end] < sequence[mid]:
UpperCAmelCase , UpperCAmelCase = sequence[mid], sequence[end]
slowsort(_snake_case , _snake_case , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 234 | 0 |
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