code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
... | 352 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_lowerCamelCase : List[str] = 0
_lowerCamelCase : Tuple = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0,... | 352 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase : List[Any] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig... | 364 |
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,
)
_lowerCAmelCase : Union[str, Any] = {
"configuration_ber... | 364 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_t... | 453 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ) -> list:
lowerCAmelCase__ : List[str] = len(lowercase__ )
lowerCAmelCase__ : Dict = []
for i in range(len(lowercase__ ) - pat_len + 1 ):
lowerCAmelCase__ : Union[str, Any] ... | 453 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
# See all Cvt models at https://huggingface... | 720 | '''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a :
"""simple docstring"""
def __init__( self , snake_case_ = None ):
'''simple docstring'''
if componen... | 466 | 0 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_uti... | 228 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __a ( A__ ):
_lowerCAmelCase : ... | 228 | 1 |
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, is_staging_test
class lowerC... | 707 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__snake_case : Optional[int] =(3, 9, -1_1, 0, 7, 5, 1, -1)
__snake_case : str =(4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class lowerCamelCase__ :
'''simpl... | 90 | 0 |
"""simple docstring"""
from __future__ import annotations
def snake_case_ ( A_ : float, A_ : float, A_ : float, ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot su... | 83 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE :Union[str, Any] = parse(importlib.metadata.version('''torch'''))
def _lowerCAmelCase ( lowerCAmelCase_ :Union... | 283 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCa... | 709 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__A ):
'''simple docstring'''
snake_case_ = ['flax']
def __init__( self : List[Any] , *UpperCamelCase_ : str , **Upper... | 411 | 0 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
lowercase = 4
lowercase = (1 << p) - 1
for _ in range(p - 2 ):
lowercase = ((s * s) - 2) % m
return s == 0
if __name__ ... | 84 |
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
A__ : Tuple =1
for i in range(1, num + 1 ):
fact *= i
return fact
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
A__ : Optional[Any] =0
while number >... | 416 | 0 |
lowercase_: Optional[Any] = {str(digit): digit**5 for digit in range(10)}
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_lowerCamelCase))
def _lowercase ( ):
"""simple docstring"""
... | 716 |
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
snake_case__ : Any = 1
snake_case__ : Dict = 2
while i * i <= n:
snake_case__ : Dict = 0
while n % i == 0:
n //= i
multiplicity += 1
n_div... | 127 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTes... | 35 | '''simple docstring'''
# 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
... | 309 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_imag... | 414 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __magic_name__ ( lowercase_ = "isbn/0140328726" ) -> dict:
'''simple docstring'''
UpperCamelCase = olid.strip().strip("/" ) # Remove le... | 414 | 1 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils impor... | 351 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case_ ( __UpperCamelCase ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase__ (__UpperCAmelCase: ArgumentParser ) -> Tuple:
... | 351 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
'''simple docstring'''
UpperCAmelCase : int
UpperCAmelCase : int
class lowercase :
''... | 700 |
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 PreTrainedTokenizerBase
from ...utils... | 308 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ ={
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"... | 616 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import log... | 616 | 1 |
'''simple docstring'''
def __snake_case ( lowercase : float ):
return 10 - x * x
def __snake_case ( lowercase : float , lowercase : float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(lower... | 710 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowercase : list ):
if len(lowercase ) == 0:
return []
snake_case_ , snake_case_ = min(lowercase ), max(lowercase )
snake_case_ = int(max_value - min_value ... | 420 | 0 |
def __A(lowerCAmelCase ) -> list[int]:
"""simple docstring"""
_UpperCamelCase = len(lowerCAmelCase )
for i in range(lowerCAmelCase ):
for j in range(i + 1 , lowerCAmelCase ):
if numbers[j] < numbers[i]:
_UpperCamelCase , _UpperCamelCase =... | 612 |
# 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 app... | 612 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/xmod-ba... | 624 |
'''simple docstring'''
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, rando... | 624 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Config... | 103 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Optional[int] = int(_lowerCAmelCase )
# Initialize Result
A : int = []
# Traverse through all denomination
for denomination in reversed(... | 662 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCAmelCase ( __UpperCamelCase : list[float] , __UpperCamelCase : Any ):
'''simple docstring'''
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(__UpperCamelCa... | 719 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_ava... | 21 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
f... | 5 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_trans... | 315 | 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
a ="""src/transformers"""
# This is to make sure the transformers module imported is the one in the r... | 702 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a ={
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tokenization_canine""": ["""CanineTokenizer"""],
}
... | 337 | 0 |
"""simple docstring"""
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
lowerCAmelCase ... | 543 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def a__ ( snake_case__ ) -> Dict[str, torch.Tensor]:
lowerCamelCase = []
lowerCamelCase = [... | 543 | 1 |
'''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,
)
lowerCAmelCase : Dict = {'''configuration... | 39 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCAmelCase__ ( UpperCamelCase__ ):
def __init__( self ) -> List[str]:
# test for the above condition
self.test()
def UpperCAmelCase_ ( self ) ... | 39 | 1 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __lowerCAmelCase ( nn.Module):
'''simple docstring'''
__magic_name__ : in... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Any = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONF... | 656 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testin... | 274 |
"""simple docstring"""
def UpperCAmelCase__ ( ) -> int:
"""simple docstring"""
return 1
def UpperCAmelCase__ ( A__ ) -> int:
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCAmelCase__ ( A__ ) -> ... | 274 | 1 |
'''simple docstring'''
import pytest
lowerCAmelCase_ : Optional[Any] = """__dummy_dataset1__"""
lowerCAmelCase_ : List[str] = """\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nU... | 435 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-b... | 695 | 0 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead."""
)
| 711 |
"""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""")):
raise OptionalDep... | 87 | 0 |
'''simple docstring'''
import numpy as np
import qiskit
def _a ( _lowerCamelCase = 8 , _lowerCamelCase = None ) -> str:
"""simple docstring"""
__snake_case : Any = np.random.default_rng(seed=_lowerCamelCase )
... | 26 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface... | 514 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 295 |
"""simple docstring"""
from __future__ import annotations
lowercase_ : List[str] = '''#'''
class UpperCamelCase :
def __init__( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : dict = {}
def __SCREAMING_SNAKE_CASE... | 295 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_tor... | 209 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 279 | 0 |
from math import factorial
def UpperCamelCase ( _a = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 714 |
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... | 441 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
a_ = """<<<<<<< This should probably be modified because it mentions: """
a_ = """=======
>>>>>>>
"""
a_ = ... | 221 | from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCAmelCase__ :
"""simple docstring"""
lowerCAmelCase__ : int
lowerCAmelCase__ : TreeNode | None = None
lowerCAmelCase__ : TreeNode | None ... | 221 | 1 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowercase__( __UpperCamelCase: Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = Fil... | 714 |
'''simple docstring'''
from __future__ import annotations
import math
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ,__UpperCamelCase: bool ,__UpperCamelCase: list[int] ,__UpperCamelCase: float ):
"""simple docstring"""
... | 508 | 0 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase_ ( lowerCAmelCase__ : Callable , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float ) -> np.array:
'''simple docstring'''
A ... | 106 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
... | 145 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ = 100 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = n * (n + 1) * (2 * n + 1) / 6
__SCREAMING_SNAKE_CASE = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __... | 715 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_visio... | 553 | 0 |
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(__snake_case ) * abs(__snake_case )
if __name__ == "__main__":
import doctest
docte... | 108 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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, pre... | 108 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
Reques... | 712 |
def _A ( __snake_case :int ) -> bool:
"""simple docstring"""
if not isinstance(__snake_case , __snake_case ):
raise ValueError("check_bouncy() accepts only integer arguments" )
__SCREAMING_SNAKE_CASE = str(__snake_case )
__SCRE... | 214 | 0 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
UpperCAmelCase__ = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
UpperCAmelCase__ ... | 224 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('check_bouncy() accepts only integer arguments' )
_A = str(_S... | 27 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
A : List[str] = datasets.utils.logging.get_logger(__name__)
@dataclass
class _UpperCamelCase ( ... | 717 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A : Optional[int] = logging.get_logger(__name__)
A : List[str] = {
"goog... | 282 | 0 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 395 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.pa... | 395 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MOD... | 682 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ = None ) -> None:
if components is None:
__U... | 682 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import loa... | 591 |
'''simple docstring'''
class _a :
"""simple docstring"""
def __init__( self , A__ ) -> List[Any]:
# we need a list not a string, so do something to change the type
_SCREAMING_SNAKE_CASE = arr.split(""",""" )
def Upp... | 591 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if... | 704 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class a ( A_... | 173 | 0 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
SCREA... | 34 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( se... | 34 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils... | 554 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ = None ):
A__ = word_bank or []
# create a table
A__ = len(lowerCAmelCase__ ) + 1
A__ = ... | 554 | 1 |
'''simple docstring'''
from math import sqrt
def _UpperCAmelCase ( __A : List[Any] ):
a_ : List[str] = 0
for i in range(1 , int(sqrt(lowerCAmelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCAmelCase_ ):
total +=... | 466 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_ut... | 310 | 0 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ... | 603 | '''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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 603 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _lowercase ( UpperCamelCase_ , Upper... | 472 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __versi... | 472 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def A(__a: str = "" ):
lowerCAmelCase_ = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowerCAmelCase_ = BeautifulSoup(requests.get(__a ).text , "html.parser" )
lo... | 719 |
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, is_staging_test
class __magi... | 226 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from tra... | 55 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
... | 55 | 1 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
@datac... | 557 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_en... | 557 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'fac... | 61 | '''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must... | 244 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()... | 711 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHead... | 344 | 0 |
'''simple docstring'''
a : Union[str, Any] = "\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.g... | 640 |
'''simple docstring'''
from __future__ import annotations
def _a (lowercase__ : int , lowercase__ : int ) -> list[str]:
"""simple docstring"""
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number... | 56 | 0 |
"""simple docstring"""
from statistics import mean
import numpy as np
def A__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = 0
# Number of... | 706 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from... | 168 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = 4_... | 311 | import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_SCREAMING_SNAKE_CASE = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic... | 537 | 0 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int ) -> str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__lowerCAmelCase : List[Any] = str(bin(SCREAMING_SNAKE_CASE ) )[2:] # remove the le... | 240 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> str:
if not (isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )):
raise ValueError("""longest_common_substrin... | 240 | 1 |
from __future__ import annotations
from random import choice
def A__ ( __A : str ) ->int:
return choice(__lowerCAmelCase )
def A__ ( __A : Optional[int] , __A : Union[str, Any] ) ->int:
__A =random_pivot(__lowerCAmelCase )
... | 184 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase ... | 509 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : str = 6 ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Tupl... | 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE... | 665 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : List[str] = logging.get_logger(__name__)
__magic_name__ : Tuple = {
'facebook/enco... | 281 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __snake_case (lowerCamel... | 281 | 1 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_a : Union[str, Any] = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw ... | 713 | """simple docstring"""
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... | 663 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_deter... | 82 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 119 | 0 |
import pytest
lowerCamelCase :List[Any] = '__dummy_dataset1__'
lowerCamelCase :List[Any] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "vali... | 346 |
from collections.abc import Generator
from math import sin
def __snake_case ( _UpperCamelCase ) -> bytes:
if len(_UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
_a = b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * ... | 346 | 1 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoMo... | 252 |
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 import TextInput
from ...uti... | 252 | 1 |
'''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
from transformers import (
B... | 720 |
'''simple docstring'''
import sys
__snake_case = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6689... | 280 | 0 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__snake_case = {'''UserAgent''': UserAgent().random}
def _A ( _lowercase ) -> dict:
"""simple docstring"""
__UpperCame... | 1 |
'''simple docstring'''
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... | 90 | 0 |
from __future__ import annotations
from math import pow, sqrt
def a__ ( _UpperCamelCase : float ,_UpperCamelCase : float ,_UpperCamelCase : float ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if re... | 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 os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def a__ ( A__, A__=(), A__=None, A__="no", A__="29500" ):
SCREAMING_SNAKE_CASE_ ... | 101 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
__SCREAMING_SNAKE_CASE = logging.get_logger(__n... | 357 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( UpperCamelCase__ :int , UpperCamelCase__ :int ) -> str:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''iterations must be defined as integers''' )
if not isinstance(Up... | 574 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE (lowercase__ ):
A__ = 'ClapFeatureExtractor'
A__ = ('RobertaTokenizer', 'RobertaTokenizerFast')
def _... | 574 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _lowerCamelCase( ... | 230 |
import os
import sys
import unittest
lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
... | 230 | 1 |
from collections import defaultdict
def __lowerCamelCase ( A__ : str , A__ : str ) -> bool:
lowerCamelCase_ : str = first_str.lower().strip()
lowerCamelCase_ : Union[str, Any] = second_str.lower().strip()
# Remove whitespace
lowerCamelCase_ : List[Any] ... | 171 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE_ (a__ ):
... | 171 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any = logging.get_logger(__name__)
_A : Any = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class ... | 427 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 0 |
"""simple docstring"""
import os
_UpperCamelCase : Dict = {'I': 1, 'V': 5, 'X': 1_0, 'L': 5_0, 'C': 1_0_0, 'D': 5_0_0, 'M': 1_0_0_0}
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
'''simple docstring'''
lowercase = 0
lowercase = 0
while i... | 134 |
"""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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils... | 134 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __snake_case ( _lowerCAmelCase : List[str] ) -> int:
@wraps(_lowercase )
def _inner_fn(*_lowerCAmelCase : List[str] , **_lowerCAmelCase : Dict ):
warnings.warn(
(f... | 454 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
return np.maximum(0 , _lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, ... | 331 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
rai... | 665 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from... | 665 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing... | 271 | from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( lowercase: str = "" ) -> dict[str, float]:
'''simple docstring'''
_UpperCamelCase: Tuple = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_UpperCame... | 271 | 1 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tr... | 384 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase = 1 ,__UpperCamelCase = 10_00 ) -> int:
lowerCamelCase_ = 1
lowerCamelCase_ = 0
for divide_by_number in range(__UpperCamelCase ,digit + 1 ):
lowerCamelCase_ = []
... | 384 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTest... | 439 |
# 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 applica... | 439 | 1 |
"""simple docstring"""
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
lowerCamelCase_... | 717 |
"""simple docstring"""
class UpperCamelCase_ :
def __init__( self : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[Any]=None ) -> int:
UpperCAmelCase_ : int = data
UpperCAmelCase_ : Opt... | 463 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 67 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( unittest.TestCas... | 67 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : str = ""
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 ... | 386 |
"""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
if is_torch... | 386 | 1 |
'''simple docstring'''
from functools import reduce
__lowerCAmelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443... | 358 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __l... | 358 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = str(_lowerCAmelCase )
return len(_lowerCAmelCase ) == 9 and set(_lowerCAmelCase ) == set("""123456789""" )
def lowercase ():
for base_num in range(9999 ... | 720 |
"""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_video_inputs
if is_t... | 573 | 0 |
# Algorithm for the pigeonhole sorting
def a_ ( lowerCAmelCase_ : Optional[Any] ):
__lowerCAmelCase = min(lowerCAmelCase_ ) # min() finds the minimum value
__lowerCAmelCase = max(lowerCAmelCase_ ) # max() finds the maximum value
__lowerCA... | 53 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : str = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Tim... | 244 | 0 |
def lowerCamelCase_ ( A : int , A : int ):
"""simple docstring"""
lowerCAmelCase_ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowerCAmelCase_ = n - k
# Calculate C(n,k)
for i in range(A ):
... | 413 |
from __future__ import annotations
from collections.abc import Iterator
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self , _UpperCAmelCase):
lowerCAmelCase_ = value
lowerCAmelCase_ = None
lowerCAme... | 413 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int | float | str ) -> tuple[int, int]:
"""simple docstring"""
try:
UpperCAmelCase_ : int = float(_SCREAMING_SNAKE_CASE )
except ValueError:
raise ValueError("Please enter a valid numbe... | 71 |
'''simple docstring'''
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
UpperCAmelCase_ : Dict = min(_SCREAMING_SNAKE_CASE )
UpperCAmelCase... | 71 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def lowerCamelCase ( ) -> Tuple:
'''simple docstring'''
from torch.utils.cpp_extension import load
__UpperCAmelCase : int = Path(_UpperCamelCase ).resolve().parent.parent.parent / """kernels""" / """deform... | 703 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbon... | 299 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def UpperCAmelCase ( a__ = 1_00_00_00 , a__ = 10 ):
'''simple docstring'''
lowerCAmelCase :defaultdict = defaultdict(a__ )
for outer_width in range(3 , ... | 553 |
"""simple docstring"""
import math
def UpperCAmelCase ( a__ , a__ ):
'''simple docstring'''
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(a__ )
else:
if x == 0: ... | 553 | 1 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__UpperCamelCase : Optional[int] = {'UserAgent': UserAgent().random}
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : List[str] ... | 721 | from __future__ import annotations
from typing import Any
class lowercase__ ( UpperCamelCase_):
pass
class lowercase__ :
def __init__( self : Union[str, Any] , UpperCamelCase__ : Any ):
'''simple docstring'''
... | 34 | 0 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __a(SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ):
''... | 18 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else... | 479 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCondi... | 718 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
... | 353 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : int = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""JukeboxPriorConfig... | 295 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 200 ) -> int:
lowerCamelCase__ : Dict = [1, 2, 5, 10, 20, 50, 100, 200]
lowerCamelCase__ : Union[str, Any] = [0] * (pence + 1)
lowerCamelCase__ : List[str] = 1 # base case: 1 way to make 0 ... | 295 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@mayb... | 721 |
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> bool:
_UpperCAmelCase = len(snake_case ) + 1
_UpperCAmelCase = len(snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i o... | 175 | 0 |
'''simple docstring'''
import random
class _UpperCAmelCase :
"""simple docstring"""
@staticmethod
def _lowerCAmelCase ( lowerCAmelCase_ ):
'''simple docstring'''
a_ : int = [ord(snake_case__ ) for i in text]
a_ : Optional[int]... | 577 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def UpperCamelCase ( __lowe... | 204 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 37 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MA... | 37 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
lowerCamelCase_ = l... | 498 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Dict = logging.get_logger(__name__)
def UpperCamelCase_ ( __a ) -> Union[str, Any]:
a__ : Tuple ... | 37 | 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.utils impo... | 709 | import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
A : Any = logging.getLogger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def ... | 356 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_conf... | 539 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import Aut... | 539 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
... | 716 |
"""simple docstring"""
def __lowercase ( a : str , a : str ) -> str:
__snake_case : int =len(a )
__snake_case : int =len(a )
__snake_case : int =(
first_str_length if first_str_length > second_str_length else sec... | 497 | 0 |
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