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"""
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
a_ = "tiny-wmt19-en-ru"
# Build
# borrowed from ... | 719 |
"""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 | 0 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
a_ = get_logger(__name__)
a_ = R"\n Args:\n input_ids (`jnp.ndarray` of s... | 720 |
"""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 | 0 |
"""simple docstring"""
from __future__ import annotations
a_ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class snake_case :
def __init__( sel... | 721 |
"""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 | 0 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class snake_case :
def __init__( self : Union[str, Any] , a__ : List[str] , a__ : int , a__ : int ) -> List[s... | 700 |
"""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 | 0 |
"""simple docstring"""
class snake_case :
def __init__( self : Tuple , a__ : int ) -> None:
'''simple docstring'''
_A = size
_A = [0] * size
_A = [0] * size
... | 701 |
"""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 | 0 |
"""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... | 702 |
"""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 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {}
class snake_case ( _UpperCamelCase):
__UpperCamelCase = 'llama'
__UpperCamelCase = ['past_key_values... | 703 |
"""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 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See... | 704 |
"""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 | 0 |
"""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... | 705 |
"""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 | 0 |
"""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... | 706 |
"""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 | 0 |
"""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 = []
emb... | 707 |
"""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 | 0 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 708 |
"""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 | 0 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase=1024 ) -> Any:
_A , _A = [], []
_A ... | 709 |
"""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 | 0 |
"""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... | 710 |
"""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 | 0 |
"""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... | 711 |
"""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 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,... | 712 |
"""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 | 0 |
"""simple docstring"""
a_ : List[str] = "\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.... | 713 |
"""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 | 0 |
"""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,
... | 714 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 0 |
"""simple docstring"""
def a__ ( __lowercase ) -> tuple[int, int]:
try:
_A = float(__lowercase )
except ValueError:
raise ValueError("Please enter a valid number" )
_A = decimal - int(__lowercase )
if fractional_part == 0:
return ... | 715 |
"""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 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...... | 716 |
"""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 | 0 |
"""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_ = "h... | 717 |
"""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 | 0 |
"""simple docstring"""
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
a_ = importlib.util.find_spec("s3fs") is not None
if _has_safs:
... | 718 |
"""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 | 0 |
"""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"... | 719 |
"""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 | 0 |
"""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... | 720 |
"""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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not i... | 721 |
"""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 | 0 |
"""simple docstring"""
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 snake_case ( _UpperCamelCase):
__Uppe... | 700 |
"""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 | 0 |
"""simple docstring"""
def a__ ( __lowercase = 1000 ) -> int:
return sum(e for e in range(3 , __lowercase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 701 |
"""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 | 0 |
"""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... | 702 |
"""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 | 0 |
"""simple docstring"""
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_... | 703 |
"""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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
... | 704 |
"""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 | 0 |
"""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... | 705 |
"""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 | 0 |
"""simple docstring"""
from typing import Any
class snake_case :
def __init__( self : Optional[int] , a__ : Any ) -> str:
'''simple docstring'''
_A = data
_A = None
class ... | 706 |
"""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 | 0 |
"""simple docstring"""
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ = ... | 707 |
"""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 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
c... | 708 |
"""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 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case ( _UpperCamelCase):
__UpperCamelCase = 'WhisperFeatureExtractor'
__UpperCamelCase = 'WhisperTokenizer'
def __init__( self : List[Any] , a__ : ... | 709 |
"""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 | 0 |
"""simple docstring"""
a_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def a__ ( ) -> None:
_A = input("Enter message: " )
_A = input("Enter key [alphanumeric]: " )
_A = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e"... | 710 |
"""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 | 0 |
"""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__ : ... | 711 |
"""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 | 0 |
"""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"... | 712 |
"""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 | 0 |
"""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... | 713 |
"""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 | 0 |
"""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
... | 714 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 0 |
"""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 ... | 715 |
"""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 | 0 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class snake_case ( _UpperCamelCase):
def __init__( self : int , a__ : Tuple , a__ : Tuple ) -> Optional[int]:
'''simple docstring'... | 716 |
"""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 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from trans... | 717 |
"""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 | 0 |
"""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:
... | 718 |
"""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 | 0 |
"""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_ ... | 719 |
"""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 | 0 |
"""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 ... | 720 |
"""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 | 0 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
a_ = "src/transformers"
# Matches is_xxx_available()
a_ = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
a_ = re.compile... | 721 |
"""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 | 0 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
# TODO Update this
a_ = {
"facebook/esm-1b": "http... | 700 |
"""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 | 0 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def a__ ( __lowercase , __lowercase , __lowercase ) -> List[Any]:
_A = 0
if start < end:
_A = randint(__lowercase , __lowerca... | 701 |
"""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 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a_ = "\\n@misc{chen2021evaluating,\n t... | 702 |
"""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 | 0 |
"""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 content_categorie... | 703 |
"""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 | 0 |
"""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
... | 704 |
"""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 | 0 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..... | 705 |
"""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 | 0 |
"""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... | 706 |
"""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 | 0 |
"""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 , __lowercase ) -> boo... | 707 |
"""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 | 0 |
"""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,
... | 708 |
"""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 | 0 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> int:
if index == r:
for j in range(__lowercase ):
print(data[j] , end=" " )
print(" " )
return... | 709 |
"""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 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import... | 710 |
"""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 | 0 |
"""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
fr... | 711 |
"""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 | 0 |
"""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... | 712 |
"""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 | 0 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 713 |
"""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 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
... | 714 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizati... | 715 |
"""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 | 0 |
"""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... | 716 |
"""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 | 0 |
"""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
... | 717 |
"""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 | 0 |
"""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... | 718 |
"""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 | 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_ ... | 719 |
"""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 | 0 |
"""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... | 720 |
"""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 | 0 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
return abs(__lowercase ) if a == 0 else greatest_common_divisor(b % a , __lowercase )
def a__ ( __lowercase , __lowercase ) -> int:
while y: # --> when y=0 then loop will ter... | 721 |
"""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 | 0 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,... | 622 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {"""vocab_file""": """spiece.... | 622 | 1 |
import os
import pytest
from attr import dataclass
a_ = """us-east-1""" # defaults region
@dataclass
class __lowerCAmelCase :
lowerCAmelCase__ = 42
lowerCAmelCase__ = """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
lowerCAmelCase__ = {
"""t... | 622 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 622 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""huggingface/informer-tourism-monthly""": (
"""https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/con... | 622 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDC... | 622 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = CTRLTokenizer
lowe... | 622 |
import torch
from diffusers import StableDiffusionPipeline
a_ = """path-to-your-trained-model"""
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
a_ = """A photo of sks dog in a bucket"""
a_ = pipe(prompt, num_inference_steps=50, guidance_s... | 622 | 1 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self ):
'''simple docstring'''
__lowerCamelCase = []
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return self.node_pos... | 622 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
ne... | 622 | 1 |
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str = " " ):
__lowerCamelCase = []
__lowerCamelCase = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__... | 622 |
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 TokenizerTest... | 622 | 1 |
from pathlib import Path
import fire
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str ,_UpperCamelCase : int ):
__lowerCamelCase = Path(_UpperCamelCase )
__lowerCamelCase = Path(_UpperCamelCase )
dest_dir.mkdir(exist_ok=_UpperCamelCase ... | 622 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module... | 622 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreT... | 622 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""... | 622 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ba... | 622 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest... | 622 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelCas... | 622 |
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 __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@re... | 622 | 1 |
import math
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : List[str] ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_UpperCamelCase )
else:
if x == 0: # 0 raised to any number is 0
return 0
... | 622 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, p... | 622 | 1 |
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... | 622 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 622 | 1 |
from math import sqrt
def a__ ( _UpperCamelCase : int ):
assert isinstance(_UpperCamelCase ,_UpperCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
__lowerCamelCase = True
# 0 and 1 are none primes.
if number <= 1:
__lowerC... | 622 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeads... | 622 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableU... | 622 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser,... | 622 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxCon... | 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
... | 622 | 1 |
import argparse
import os
import re
import packaging.version
a_ = """examples/"""
a_ = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULT... | 622 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def a__ ( ... | 622 | 1 |
def a__ ( _UpperCamelCase : list ):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 ,len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
__lowerCamelCase = grid[0]... | 622 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...te... | 622 | 1 |
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 __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docs... | 622 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _UpperCamelCase : str = "https://www.worldometers.info/coronavirus/" ):
__lowerCamelCase = '''//div[@class = ... | 622 | 1 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Tra... | 622 |
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : str = " " ):
__lowerCamelCase = []
__lowerCamelCase = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__... | 622 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {"""vocab_file""": """spiece.... | 622 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ba... | 622 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def a__ ( _UpperCamelCase : int = 3 ):
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
raise TypeError('''number of qubits must be a integer.''' ... | 622 |
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_ = {
"""sail/poolformer_s12""": """https://huggingface... | 622 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module... | 622 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """https://huggingface.co/ucl... | 622 | 1 |
import string
import numpy
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : int ):
return b if a == 0 else greatest_common_divisor(b % a ,_UpperCamelCase )
class __lowerCAmelCase :
lowerCAmelCase__ = string.ascii_uppercase + string.digits
# This cipher takes alp... | 622 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {"""vocab_file""": """spiece.... | 622 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a__ ( _UpperCamelCase : int ):
if "cls_token" in name:
__lowerCamelCase = name.replace('''cls_token''' ,'''vit.embeddin... | 622 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 622 | 1 |
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : bool = False ):
if not isinstance(_UpperCamelCase ,_UpperCamelCase ):
__lowerCamelCase = F"""Expected string as input, found {type(_UpperCamelCase )}"""
raise ValueError(_UpperCamelCase )
if not isi... | 622 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDC... | 622 | 1 |
def a__ ( _UpperCamelCase : list ,_UpperCamelCase : int = 0 ):
__lowerCamelCase = length or len(_UpperCamelCase )
__lowerCamelCase = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
__lowerCamelCase ,__lower... | 622 |
import torch
from diffusers import StableDiffusionPipeline
a_ = """path-to-your-trained-model"""
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
a_ = """A photo of sks dog in a bucket"""
a_ = pipe(prompt, num_inference_steps=50, guidance_s... | 622 | 1 |
from __future__ import annotations
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : list[str] | None = None ):
__lowerCamelCase = word_bank or []
# create a table
__lowerCamelCase = len(_UpperCamelCase ) + 1
__lowerCamelCase = []
... | 622 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
ne... | 622 | 1 |
from __future__ import annotations
from typing import Any
class __lowerCAmelCase ( lowerCAmelCase__ ):
pass
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = data
__lowerCamelCase ... | 622 |
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 TokenizerTest... | 622 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class __lowerCAmelCase ( lowerCAmelCase__ ):
lowerCAmelC... | 622 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module... | 622 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
a_ = collections.namedtuple("""_Datasets""", ["""train""", """validation""", ... | 622 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""... | 622 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
# See all Data2VecAudio models at ... | 622 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest... | 622 | 1 |
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