code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self : str , lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : Tuple , lowercase_ : Dic... | 151 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase__ = {"processing_layoutxlm": ["Layou... | 151 | 1 |
from __future__ import annotations
from collections import namedtuple
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> tuple:
"""simple docstring"""
UpperCamelCase : Union[str, Any] = namedtuple("result" , "nam... | 353 |
import math
def A_ ( _lowerCAmelCase ) -> list:
UpperCamelCase : str = [True] * n
UpperCamelCase : Optional[int] = False
UpperCamelCase : str = False
UpperCamelCase : List[Any] = True
for i in range(3 , int(n**0.5... | 140 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.util... | 326 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowerCAmelCase__( lowercase : Dict ) -> str: # picklable for multip... | 326 | 1 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = ... | 370 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
fr... | 226 | 0 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_UpperCamelCase : Optional[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets ... | 77 | """simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : int = args.pruning_method
... | 77 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...tes... | 281 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
raise... | 281 | 1 |
from __future__ import annotations
import numpy as np
def _UpperCAmelCase (UpperCamelCase__ : list[float] ):
return np.maximum(0 , UpperCamelCase__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 11 |
"""simple docstring"""
import os
from collections.abc import Iterator
def __SCREAMING_SNAKE_CASE ( A_ = "." ):
for dir_path, dir_names, filenames in os.walk(A_ ):
lowerCAmelCase__ : str = [d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
for filename in filenames:
i... | 106 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformer... | 355 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils ... | 91 | 0 |
from importlib import import_module
from .logging import get_logger
UpperCAmelCase__ = get_logger(__name__)
class lowercase_ :
'''simple docstring'''
def __init__( self : int , __UpperCAmelCase : Optional[int]... | 0 | import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase = version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
from nltk import word_tokenize
_UpperCAmelCa... | 140 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
),
# See all Tr... | 167 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image... | 167 | 1 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _A ( lowercase , lowercase=() , lowercase=None , lo... | 81 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
P... | 226 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def a ( lowerCamelCase__ ):
'''simple docstring'''
if "img_encoder.pos_embed" in name:
A_ : List[str] = ... | 370 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _lowerCAmelCase :
@property
def _a (self... | 135 | 0 |
def lowerCAmelCase_ ( _snake_case : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
__magic_name__ : list = []
for temp in range(int(_snake_case ) ):
series.append(F'''1/{temp + 1}''' if series else "1" )
return series
if __name__ == "__main__"... | 281 |
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
snake_case : int = logging.get_logger(__name__)
snake_case : List[st... | 281 | 1 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 355 | """simple docstring"""
def _lowerCamelCase( a , a , a , a ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
__a = mf_knapsack(i - 1 , a , a , a )
else:
__a = ... | 268 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : Tuple = logging.get_logger(__nam... | 47 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, ... | 91 | 0 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
lowerCAmelCase__ : List[str] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to ... | 37 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
... | 37 | 1 |
"""simple docstring"""
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowercase_ ( ... | 167 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import ... | 167 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_=None ) -> List[str]:
"""simple docstring"""
a = ... | 330 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCamelCase__ : Optional[int] = pd.read_csv("""sample_data.csv""", header... | 330 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0'''))... | 105 | """simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 135 | 0 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer... | 359 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 281 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ... | 173 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = lo... | 268 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( __A : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(__A , (list, tuple) ) or not all(
isinstance(__A , __A ) for number in numbers ):
raise ValueError("numbers must be an iterable of int... | 111 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowerCamelCase_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5,... | 111 | 1 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if not nums:
raise ValueError("""List is empty""" )
return sum(UpperCamelCase ) / len(UpperCamelCase )
if __name__ == "__main__":
impor... | 37 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_lowerCAmelCase = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems... | 37 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Tuple =... | 355 |
_lowerCamelCase : List[Any] = tuple[float, float, float]
_lowerCamelCase : Tuple = tuple[float, float, float]
def _UpperCAmelCase (UpperCamelCase_ : Pointad , UpperCamelCase_ : Pointad ):
'''simple docstring'''
_lowerCAmelCase : Tuple = end_pointa[0] - en... | 159 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig""",
"""Blip2VisionConfig"""... | 330 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel""", """weight"""),
... | 330 | 1 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
A__ : List[str] = HfApi()
A__ : List[str] = {}
# fmt: off
A__ : Optional[int] = torch.tensor([
-0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.3... | 0 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
... | 0 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE... | 24 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin impor... | 281 | 0 |
import functools
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# Validation
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or not all(isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for day in days ):
raise ... | 224 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowercase__ = n - k
# Calculate C(n,k)
for i in range(SCREAMING_SNAKE_CASE_ ):
result *=... | 224 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__UpperCAmelCase : Dict = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel ... | 111 |
def A__ ( SCREAMING_SNAKE_CASE__ = 200) -> int:
__snake_case: Optional[int] = [1, 2, 5, 10, 20, 50, 100, 200]
__snake_case: List[Any] = [0] * (pence + 1)
__snake_case: int = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(SC... | 111 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_bloc... | 352 |
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
UpperCAmelCase__ ... | 26 | 0 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerat... | 2 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fro... | 159 | 0 |
"""simple docstring"""
import torch
from torch import nn
class SCREAMING_SNAKE_CASE_ ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=1 , ... | 255 |
"""simple docstring"""
import inspect
import unittest
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self):
try:
import diffusers # noqa: F401
except ImportError:
assert False
def snake_case_ ... | 255 | 1 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCAmelCase__ = HfApi()
UpperCAmelCase__ = {}
# fmt: off
UpperCAmelCase__ = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1... | 0 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6... | 0 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase ) -> list[int]:
return [ord(__lowercase ) - 96 for elem in plain]
def a__ ( __lowercase ) -> str:
return "".join(chr(elem + 96 ) for elem in encoded )
... | 163 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> float:
_validate_point(__lowercase )
_validate_point(__lowercase )
if len(__lowercase ) != len(__lowercase ):
raise ValueError("Both points must be in the same n-dimensional space" )... | 163 | 1 |
"""simple docstring"""
import math
class UpperCamelCase__ :
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self : int , SCREAMING_SNAKE_CASE_ : list[list[float]] , SCREAMING_SNAKE_CASE_ : list[in... | 224 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : ... | 224 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import... | 161 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, 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 .tokeni... | 161 | 1 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hu... | 277 |
from math import asin, atan, cos, radians, sin, sqrt, tan
_snake_case = 6_3_7_8_1_3_7.0
_snake_case = 6_3_5_6_7_5_2.3_1_4_2_4_5
_snake_case = 6378137
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ):
_A : Any ... | 26 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase__ ( ):
snake_case_ : str = ArgumentParser(
description=(
"PyTorch... | 359 |
import argparse
import copy
def lowerCAmelCase__ ( _a : List[Any] ):
snake_case_ : List[Any] = {}
with open(_a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case_ : int = []
_list.append([line.spl... | 36 | 0 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = False ) -> bool:
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): #... | 255 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers... | 255 | 1 |
"""simple docstring"""
UpperCAmelCase : Optional[Any] = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter... | 320 |
"""simple docstring"""
UpperCAmelCase : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase ( _UpperCamelCase : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ... | 320 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKE... | 163 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
... | 163 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""funnel-transfo... | 192 | import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase ( __A ):
'''simple docstring'''
def __init__( self , *lowercase , **lowe... | 192 | 1 |
'''simple docstring'''
import os
import string
import sys
a__ : int = 1 << 8
a__ : Optional[Any] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 2_7,
"up": 6_5 + ARROW_KEY_FLAG,
"down": 6_6 + ARROW_KEY_FLAG,
"right": 6_7 + ARR... | 161 |
'''simple docstring'''
from __future__ import annotations
def snake_case ( UpperCAmelCase )-> list[int]:
"""simple docstring"""
__A = 2
__A = []
while i * i <= n:
if n % i:
i += 1
els... | 161 | 1 |
"""simple docstring"""
import os
from distutils.util import strtobool
def _lowerCAmelCase ( UpperCAmelCase__ : List[Any], UpperCAmelCase__ : Optional[Any] ) ->List[str]:
for e in env_keys:
A__ : List[Any] = int(os.environ.get(UpperCAme... | 296 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
A_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel... | 296 | 1 |
import math
def _a ( UpperCamelCase_ : Tuple , UpperCamelCase_ : List[str] ) -> List[Any]:
"""simple docstring"""
if (
not isinstance(_lowerCamelCase , (int, float) )
or power_factor < -1
or power_factor > 1
... | 340 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import... | 36 | 0 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils imp... | 135 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCamelCase :Union[str, An... | 135 | 1 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_... | 320 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 320 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.sche... | 368 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, 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 .tokeni... | 161 | 0 |
def UpperCamelCase (lowercase_: str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
A__ : str = set()
# Replace all the whitespace in our sentence
A__ : List[str] = input_str.replace(""" """ , """""" )
for alpha in input_str:
... | 192 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""... | 192 | 1 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
... | 360 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_... | 119 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils ... | 296 |
from collections import defaultdict
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 2
while 2 * euc... | 296 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consider... | 357 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A(__a: Any , __a: Union[str, Any] , __a: List[str] ):
lowerCAmelCase_ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelle... | 22 | 0 |
"""simple docstring"""
import numpy as np
def lowercase_ ( _lowerCamelCase: np.array ) -> np.array:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase_ ( _lowerCamelCase: np.array ) -> np.array:
''... | 135 | """simple docstring"""
__A = [0, 2, 4, 6, 8]
__A = [1, 3, 5, 7, 9]
def lowercase_ ( _lowerCamelCase: int , _lowerCamelCase: int , _lowerCamelCase: list[int] , _lowerCamelCase: int ) -> int:
'''simple docstring'''
if remaining_length ==... | 135 | 1 |
from __future__ import annotations
from collections import deque
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ ):
lowercase_ :list[dict] = []
self.adlist.append(
{... | 369 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional ... | 252 | 0 |
import re
def UpperCamelCase( __UpperCamelCase : str ):
lowerCAmelCase_ : str = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(__UpperCamelCase ,__UpperCamelCase ):
return match.string == phone
return False
if __name__ == "__main__":
pr... | 103 |
'''simple docstring'''
from __future__ import annotations
def snake_case ( UpperCAmelCase )-> list[int]:
"""simple docstring"""
__A = 2
__A = []
while i * i <= n:
if n % i:
i += 1
els... | 161 | 0 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
snake_case__ : int = {
# 1536-bit
5: {
... | 314 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
class snake_case_( a__ ):
... | 314 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetP... | 28 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import loggi... | 119 | 0 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase = 0.0 , UpperCAmelCase = 1.0 ) ->int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
... | 303 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common i... | 303 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( lowercase__ : float, lowercase__ : float, lowercase__ : float ):
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > ... | 141 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import T... | 22 | 0 |
'''simple docstring'''
def __a ( UpperCAmelCase ) ->int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
A = f"""Input value of [number={number}] must be an integer"""
raise TypeError(UpperCAmelCase )
if number < 1:
A ... | 337 |
'''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 __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
... | 337 | 1 |
'''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_ : Optional[int] = "\\n@misc{chen2021evaluating,\n... | 55 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : int = {
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AutoformerConfig",
],
}... | 252 | 0 |
# Lint as: python3
import itertools
import os
import re
A_ : Dict = re.compile(r'([A-Z]+)([A-Z][a-z])')
A_ : List[str] = re.compile(r'([a-z\d])([A-Z])')
A_ : Optional[int] = re.compile(r'(?<!_)_(?!_)')
A_ : Tuple = re.compile(r'(_{2,})')
A_ : Tuple = r'^\w+(\... | 361 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can a... | 292 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNeta... | 299 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 0 |
import argparse
import os
import re
UpperCAmelCase__ = 'src/transformers'
# Pattern that looks at the indentation in a line.
UpperCAmelCase__ = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase__ = re.compile(r'^\s*\"([^\"]+)\":')
#... | 361 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
... | 40 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCamelCase :
"""simple docstring"""
def UpperCAmelCase__ ( self : Any , _A : Any ):
"""simple... | 303 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a__ ( snake_case , snake_case=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = OmegaConf.load(snake_case )
if display:
print(yaml.dump(Om... | 303 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = len(_A )
# We need to create solution object to save path.
lowerCAmelCase_ = [[0 for _ in range(_A )] for _ in range(_A )]
lowerCAmelCase_ = run_maze(_A , 0 , ... | 167 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host... | 167 | 1 |
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
if not isinstance(_UpperCamelCase, _UpperCamelCase ):
lowercase : List[str] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(_UpperCamelCase )
... | 337 |
from typing import List
from .keymap import KEYMAP, get_character
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
def decorator(_UpperCamelCase ):
lowercase : str = getattr(_UpperCamelCase, '''handle_key''', [] )
han... | 337 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedTo... | 25 |
'''simple docstring'''
def lowercase ():
"""simple docstring"""
_lowerCAmelCase : Optional[int] = [3_1, 2_8, 3_1, 3_0, 3_1, 3_0, 3_1, 3_1, 3_0, 3_1, 3_0, 3_1]
_lowerCAmelCase : int = 6
_... | 25 | 1 |
import baseaa
def __snake_case ( _UpperCAmelCase ):
return baseaa.aaaencode(string.encode('''utf-8''' ) )
def __snake_case ( _UpperCAmelCase ):
return baseaa.aaadecode(_UpperCAmelCase ).decode('''utf-8''' )
if __name__ == "__main__":
import doctest
doctest.test... | 49 |
"""simple docstring"""
from __future__ import annotations
_snake_case : str = []
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
for i in range(len(UpperCamelCase ) ):
if board[row][i] == 1:
retur... | 292 | 0 |
# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thu... | 41 |
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Option... | 41 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__A = get_tests_dir() + "/test_... | 10 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def lowercase ( A_ , A_ , A_ , A_ )-> Union[str, Any]:
'''simple docstring'''
a : Any = sorted(zip(A_ , A_ ) , key=lambda A_ ... | 40 | 0 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatc... | 360 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str ) -> List[Any]:
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def UpperCamelCase_ ... | 289 | 0 |
"""simple docstring"""
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if index == number_of_items:
return 0
A_ : Tuple = 0
A_ ... | 167 |
"""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
... | 167 | 1 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any] ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCamelCase_ (UpperCamelCase__ : str , UpperCamelCase__ : str=0 ):
... | 68 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_UpperCAmelCase : str = {}
def __lo... | 68 | 1 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCAmelCase_ :
"""simple docstring"""
def __init__(self , SCREAMIN... | 25 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowercase_ ( ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = ArgumentParser(
... | 25 | 1 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_A : List[Any] =argparse.ArgumentParser()
pars... | 129 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSc... | 129 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
i... | 41 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixi... | 41 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepE... | 361 |
'''simple docstring'''
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] ) -> None:
'''simple docstring'''
_a = len(lowerCAmelCase__ )
print('The following activities are selected:' )
# The first a... | 104 | 0 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeniz... | 25 | """simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 289 | 0 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCamelCase = '''src/transformers'''
# This is to make sure the transformers m... | 125 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __lowerCamelCase ( snake_case__ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = prime_factors(snake_case__ )
if is_square_free(... | 125 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> None:
'''simple docstring'''
if (direction == 1 and array[in... | 68 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
ge... | 68 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 364 | """simple docstring"""
from itertools import product
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = sides_number
_UpperCAmelCase = max_face_number * dice_number
_UpperCAmelCase = [0] * (max_total + 1)
_UpperCAmelCase = 1
_U... | 30 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_to... | 129 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
__sn... | 129 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( UpperCamelCase__ : bytes ) -> str:
"""simple docstring"""
return "".join([hex(lowercase__ )[2:].zfill(2 ).upper() for byte in list(lowercase__ )] )
def lowerCamelCase_ ( UpperCam... | 356 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 348 | 0 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> Tuple:
if not isinstance(A__ , A__ ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
snake_case_ = str(A__ )
snake_case_ = """""".join(sorted(A__ ) )
ret... | 347 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tr... | 104 | 0 |
# 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/LICENSE-2.0
#
# Unless required by applicabl... | 105 | 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 transformers.models.mbart.modeling_mbart import... | 105 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
snake_case_ : Union[str, Any] = 50_00_00
snake_case_ ,snake_case_ : Optional[int] = os.path.split(__file__)
snake_case_ : O... | 125 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
snake_case_ : List[str] = logging... | 125 | 1 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCamelCase__ ( _lowercase , _lowercase=0 ):
'''simple docstring'''
return sorted(_lowercase ... | 235 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 235 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCamelCase ( __A , __A , __A , __A , __A ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
... | 80 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 30 | 0 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
... | 72 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data imp... | 72 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"ut/deta": "http... | 28 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__snake_case = 4
__snake_case = 3
class __snake_case ( lowerCamelCase__ ):
pass
d... | 348 | 0 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __snake_case( _lowerCAmelCase ) -> Any:
snake_cas... | 43 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 43 | 1 |
"""simple docstring"""
class __A :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict ) ->str:
"""simple docstring"""
... | 347 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase__ = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _A ( ... | 104 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'co... | 354 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : s... | 1 | 0 |
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 imp... | 235 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = (DDPMScheduler,)
def __lowercase ( sel... | 235 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = 1
__lowerCAmelCase = 2
while i * i <= n:
__lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity +=... | 364 |
'''simple docstring'''
import numpy as np
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase = 1E-12, lowerCamelCase = 1_0_0, ):
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[1]
# Ensure proper dimensionality.
assert np.shape(lowerCamelCa... | 9 | 0 |
"""simple docstring"""
lowerCAmelCase__ = [0, 2, 4, 6, 8]
lowerCAmelCase__ = [1, 3, 5, 7, 9]
def snake_case_ ( A_ : int, A_ : int, A_ : list[int], A_ : int ):
'''simple docstring'''
if remaining_length == 0:
... | 72 |
"""simple docstring"""
def snake_case_ ( A_ : list[list[float]] ):
'''simple docstring'''
_lowerCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(A_ ):
if len(A_ ) < i... | 72 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 362 | def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
while b:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = b, a % b
return a
def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ ... | 206 | 0 |
import re
from filelock import FileLock
try:
import nltk
__lowercase = True
except (ImportError, ModuleNotFoundError):
__lowercase = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def lowerCamelCase ( SCREAMING... | 43 | import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 43 | 1 |
import functools
from typing import Any
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or len(_UpperCamelCase ) == 0:
raise ValueError('''the string sho... | 356 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 279 | 0 |
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