code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 621 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetect... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase ) -> list[int]:
_A = 2
_A = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__lowercase )
if... | 621 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( _UpperCamelCase):
__UpperCamelCase = (IPNDMScheduler,)
__UpperCamelCase = (('num_inference_steps', 50),)
... | 621 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
... | 621 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> bool:
if len(__lowercase ) == 0:
return False
_A = len(__lowercase ) // 2
if a_list[midpoint] == item:
return True
if item < a_list... | 621 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Infor... | 621 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 |
"""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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 | 1 |
"""simple docstring"""
class snake_case : # Public class to implement a graph
def __init__( self : int , a__ : int , a__ : int , a__ : list[list[bool]] ) -> None:
'''simple docstring'''
... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case ( _UpperCamelCase):
__UpperCamelCase = 'WhisperFeatureExtractor'
__UpperCamelCase = 'WhisperTokenizer'
def __init__( self : List[Any] , a__ : ... | 621 |
"""simple docstring"""
# Copyright 2021 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/LICE... | 621 | 1 |
"""simple docstring"""
import math
def a__ ( __lowercase = 100 ) -> int:
_A = sum(i * i for i in range(1 , n + 1 ) )
_A = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == ... | 621 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
if len(__lowercase ) != len(__lowercase ):
raise ValueError("String lengths must match!" )
_A = 0
for chara, chara in zip(__lowercase , __lowercase ):
if chara != ch... | 621 |
"""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_ = {
"configuration_roformer": ["ROFORME... | 621 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
a_ = versi... | 621 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 | 1 |
"""simple docstring"""
import math
def a__ ( __lowercase ) -> bool:
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
... | 621 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
a_ = "http:... | 621 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common i... | 621 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impor... | 621 | 1 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( __lowercase ) -> List[Any]:
_A = os.path.join(args.tf_model_dir , "parameters.json" )
_A ... | 621 | 1 |
"""simple docstring"""
import math
def a__ ( __lowercase , __lowercase ) -> int:
_A = len(__lowercase )
_A = int(math.floor(math.sqrt(__lowercase ) ) )
_A = 0
while arr[min(__lowercase , __lowercase ) - 1] < x:
... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
import requests
def a__ ( __lowercase ) -> dict:
_A = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(__lowercase ).json()
def a__ ( ... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 621 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers impo... | 621 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 | 1 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_sim... | 621 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 | 1 |
"""simple docstring"""
import operator as op
def a__ ( __lowercase ) -> str:
_A = []
_A = lambda __lowercase , __lowercase : int(x / y ) # noqa: E731 integer division operation
_A = {
"^": op.pow,
"*": op.mul,
... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import... | 621 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetect... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> list:
_A = word.split()
def justify(__lowercase , __lowercase , __lowercase ) -> str:
_A = max_width - width
_A = len(__lowercase )
if len(__lowercase... | 621 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 1 |
"""simple docstring"""
class snake_case :
def __init__( self : Tuple , a__ : Any , a__ : Tuple ) -> str:
'''simple docstring'''
_A = name
_A = val
def... | 621 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> None:
_A = len(__lowercase )
print("The following activities are selected:" )
# The first activity is always selected
_A = 0
print(__lowercase , end="," )
# Consider r... | 621 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 | 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... | 621 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 1 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class snake_case ( _UpperCamelCase):
def __ini... | 621 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a__ ( __lowercase ) -> int:
_A... | 621 |
"""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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ ... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
a_ = logging.get_logger(__name__)
class snake_case :
... | 621 |
"""simple docstring"""
# Copyright 2021 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/LICE... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
import requests
a_ = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked conten... | 621 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 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.te... | 621 |
"""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_ = {
"configuration_roformer": ["ROFORME... | 621 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( __lowercase ) -> Optional[Any]:
def wrapper(*__lowercase , **__lowercase ):
_A ... | 621 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
fro... | 621 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase , __lowercase , ) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
... | 621 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...... | 621 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impor... | 621 | 1 |
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a_ = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < ver... | 621 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( __lowercase ) -> List[Any]:
_A = os.path.join(args.tf_model_dir , "parameters.json" )
_A ... | 621 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"huggingface/time-series-transformer-tourism-monthly": (
"https:/... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
"""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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase ) -> int:
def count_of_possible_combinations(__lowercase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combina... | 621 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 621 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
a_ = input("Enter image url: ").strip()
print(f'''Downloading image from {url} ...''')
a_ = BeautifulSoup(requests.get(url).content, "h... | 621 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def a__ ( __lowercase = "AAPL" ) -> str:
_A = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_A = BeautifulSoup(requests.get(__lowercase ).text , "html.parser" )
... | 621 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@re... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 1 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class snake_case ( _UpperCamelCase):
def __init__( self : int , a__ : Tuple , a__ : Tuple ) -> Optional[int]:
'''simple docstring'... | 621 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetect... | 621 | 1 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case ( unittest.TestCase):
__UpperCamelCase = JukeboxTokenizer
__UpperCamelCase = {
'artist': 'Zac Brown Band',
... | 621 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 1 |
"""simple docstring"""
import copy
import re
class snake_case :
__UpperCamelCase = 'hp'
__UpperCamelCase = {}
__UpperCamelCase = None
@classmethod
def a_ ( cls : Any , a__ : str , a__ : Any... | 621 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"distilbert-base-un... | 621 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 1 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( __lowercase ) -> Tuple:
# A local function to see if a dot lands in the circle.
def is_in_circle(__lowercase , __l... | 621 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 621 |
"""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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ ( __lowercase , __lowercase ,... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from d... | 621 |
"""simple docstring"""
# Copyright 2021 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/LICE... | 621 | 1 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHT... | 621 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at h... | 621 |
"""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_ = {
"configuration_roformer": ["ROFORME... | 621 | 1 |
"""simple docstring"""
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
... | 621 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 | 1 |
"""simple docstring"""
a_ = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
... | 621 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 621 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 1 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from tr... | 621 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impor... | 621 | 1 |
"""simple docstring"""
a_ = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
a_ ... | 621 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( __lowercase ) -> List[Any]:
_A = os.path.join(args.tf_model_dir , "parameters.json" )
_A ... | 621 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def a__ ( __lowercase ) -> int:
# encoder.embeddings are d... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
"""simple docstring"""
import math
def a__ ( __lowercase , __lowercase ) -> float:
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > 3... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 1 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( __lowercase ) -> List[Any]:
_A = os.path.join(args.tf_model_dir , "parameters.json" )
_A ... | 621 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 621 | 1 |
"""simple docstring"""
import argparse
import os
# New Code #
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_war... | 621 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 | 1 |
"""simple docstring"""
# Function to print upper half of diamond (pyramid)
def a__ ( __lowercase ) -> str:
for i in range(0 , __lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in rang... | 621 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
i... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case ... | 621 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetect... | 621 | 1 |
"""simple docstring"""
import numpy as np
import datasets
a_ = "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\n... | 621 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration... | 621 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> int:
if index == r:
for j in range(__lowercase ):
print(data[j] , end=" " )
print(" " )
retu... | 621 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 | 1 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a_ = "sshleif... | 621 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 1 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 621 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
# See a... | 621 |
"""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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def a__ ( ) -> Tuple:
_A = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"path"... | 621 |
"""simple docstring"""
# Copyright 2021 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/LICE... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...sched... | 621 |
"""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_ = {
"configuration_roformer": ["ROFORME... | 621 | 1 |
"""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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDCondit... | 621 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...t... | 621 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 | 1 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
... | 621 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class snake_case ( _UpperCamelCase):
def __lt__( self : List[Any] , a__ : Any ) ... | 621 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impor... | 621 | 1 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
a_ = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding System... | 621 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( __lowercase ) -> List[Any]:
_A = os.path.join(args.tf_model_dir , "parameters.json" )
_A ... | 621 | 1 |
"""simple docstring"""
from typing import Any
class snake_case :
def __init__( self : Optional[int] , a__ : Any ) -> str:
'''simple docstring'''
_A = data
_A = None
class ... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
"""simple docstring"""
a_ = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
a_ = frozen... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 1 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
... | 621 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 621 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT M... | 621 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetect... | 621 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 | 1 |
"""simple docstring"""
import os
import sys
import unittest
a_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 1 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.pro... | 621 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetect... | 621 | 1 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> np.array:
_A = int(np.ceil((x_end - xa) / step_size ) )
_A = np.zer... | 621 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class snake_case ( unittest.TestCase):
def a_ ( self : ... | 621 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTran... | 621 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def a__ ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
... | 621 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 1 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : int , a__ : str=None , a__ : Dict=None ) -> int:
'''simple docstring'''
_A = data... | 621 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class snake_case ( unittest.TestCase... | 621 |
"""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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 | 1 |
"""simple docstring"""
from functools import lru_cache
def a__ ( __lowercase ) -> set:
_A = 2
_A = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lowercase )
if n > 1:
... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 |
"""simple docstring"""
# Copyright 2021 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/LICE... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCH... | 621 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 1 |
"""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_ ... | 621 |
"""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_ = {
"configuration_roformer": ["ROFORME... | 621 | 1 |
"""simple docstring"""
from math import pi, sqrt, tan
def a__ ( __lowercase ) -> float:
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __lowercase , __lowe... | 621 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 | 1 |
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