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 |
|---|---|---|---|---|
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : list) -> list:
'''simple docstring'''
if len(lowerCAmelCase__) < 2:
return collection
def circle_sort_util(lowerCAmelCase__ : list , lowerCAmelCase__ : int , l... | 125 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : int) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0) != 0)
def SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstrin... | 125 | 1 |
"""simple docstring"""
import argparse
import os
import re
__lowercase = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__lowercase = re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__lowercase = re.compile(r... | 700 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''YituTech/c... | 296 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 280 |
def lowercase_ ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if num < 0:
return False
snake_case__ : int =num
snake_case__ : int =0
while num > 0:
snake_case__ : int =rev_num * 10 + (num % 10)
num //= 10
return num_copy ==... | 381 | 0 |
"""simple docstring"""
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 141 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetSh... | 141 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def UpperCamelCase_( snake_case__: List[str] ) -> int:
# vision encoder
if "img_encoder.pos_embed" in name:
UpperCAmelCase__ = nam... | 146 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...... | 146 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kand... | 653 |
'''simple docstring'''
import requests
def SCREAMING_SNAKE_CASE ( lowercase_ : str , lowercase_ : str ):
lowercase = {"""Content-Type""": """application/json"""}
lowercase = requests.post(lowercase_ , json={"""text""": message_body} , ... | 653 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Any = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'... | 49 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__SCREAMING_SNAKE_CASE = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value netw... | 553 | 0 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common impo... | 704 |
import torch
from transformers import AutoModel
class _lowerCamelCase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ) -> Optional[Any]:
"""simple docstring"""
... | 462 | 0 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 37 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase_ ( ) -> int:
a__ : Any = HfArgumentParser(__a )
a__ : Any = parser.parse_args_into_dataclasses()[0]
a__ : Optional[int] = TensorFlowBenchmark(args=__a... | 37 | 1 |
'''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 tra... | 701 | import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_co... | 234 | 0 |
"""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_te... | 510 |
"""simple docstring"""
def snake_case ( _a: list , _a: int = 0 )-> list:
'''simple docstring'''
lowerCamelCase__ = length or len(_a )
lowerCamelCase__ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i +... | 510 | 1 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Confi... | 478 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and n... | 478 | 1 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __... | 274 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self , lowercase_ ) -> None:
UpperCAmelCase = value
UpperCAmelCase = None
... | 373 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""BAAI/AltCLIP""": """https:/... | 562 |
"""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, Times... | 562 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercas... | 118 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...ut... | 463 | 0 |
from functools import lru_cache
def a_ ( _A ) -> set:
"""simple docstring"""
snake_case__ = 2
snake_case__ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add... | 710 |
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 import Mod... | 372 | 0 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( snake_case_ : int ) -> str:
'''simple docstring'''
def is_in_circle(snake_case_ : float , ... | 78 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int ) -> bool:
'''simple docstring'''
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
do... | 78 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 717 |
'''simple docstring'''
__snake_case : Dict = {
"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==1.7.3",
... | 691 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accel... | 418 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils... | 543 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : Dict ) -> List[Any]:
'''simple docstring'''
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
snake_case__ : List[str] = False
for j in range(__snake_case , ... | 705 |
'''simple docstring'''
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataT... | 419 | 0 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, req... | 280 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 280 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase : Dict =[
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new sched... | 237 | """simple docstring"""
lowerCamelCase : int =[0, 2, 4, 6, 8]
lowerCamelCase : List[str] =[1, 3, 5, 7, 9]
def _lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : li... | 237 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowercase_ = logging.getLogger(__name__)
class __A ( A ):
'''simple docstring'''
... | 11 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 121 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
class __magic_name__ ( __lowerCAmelCase):
A: Optional[Any] = "timm_backbone"
def __init__( self : Optiona... | 717 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Any = {
... | 106 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case( _lowercase ,... | 433 |
from math import sqrt
def __SCREAMING_SNAKE_CASE ( a__ : int = 1000000 ) -> int:
__A : int = 0
__A : int = 0
__A : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 ,2 * max_cuboid_size + 1 ):
if sqrt(sum_short... | 17 | 0 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
UpperCAmelCase : Di... | 672 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
a : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __lowerCamelCase... | 672 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
UpperCamelCase_ : List[Any] = tuple[int, int]
class __lowercase :
def __init__(self : int , snake_case : set[int] , snake_case : Mapping[EdgeT, int] ) -> None:
_lowe... | 461 |
def UpperCamelCase ( _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(_UpperCAmelCase ) == len(_UpperCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0... | 461 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : int , A : int , A : int ):
'''simple docstring'''
UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
... | 50 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__( metaclass=lowerCAmelCase ):
__magic_name__ : List[str] = ["note_seq"]
def __init__( self : Any , *lowerCAmelCase : List[str] , **lowerCAmelCase : int... | 50 | 1 |
'''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... | 396 | '''simple docstring'''
def __snake_case ( lowerCAmelCase : str ):
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
__UpperCAmelCase = sorted(string.lower() )
return len(lowerCAmelCase ) == le... | 396 | 1 |
'''simple docstring'''
from __future__ import annotations
A__ : str ='Muhammad Umer Farooq'
A__ : Optional[int] ='MIT'
A__ : int ='1.0.0'
A__ : List[str] ='Muhammad Umer Farooq'
A__ : Union[str, Any] ='contact@muhammadumerfarooq.me'
A__ : List[Any] ='Alpha'
import re
from html.parser im... | 712 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def A_ ( __SCREAMING_SNAKE_CASE : Dict ) -> Tuple:
"""simple docstring"""
__A : Dict = {}
__A : Op... | 499 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 215 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__snake_case : Dict = logging.g... | 215 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( _UpperCAmelCase):
"""simple docstring"""
UpperCamelCase__ = ['image_processor', 'tokenize... | 719 |
from __future__ import annotations
def lowerCAmelCase_ ( A_ ,A_ ,A_):
if (voltage, current, resistance).count(0) != 1:
raise ValueError("One and only one argument must be 0")
if resistance < 0:
raise ValueError("Resistance cannot be negative")
... | 221 | 0 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, ... | 531 |
A : 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.git\n'
A : Optional[int] ... | 371 | 0 |
class lowercase :
def __init__( self : Dict , _lowercase : Union[str, Any] , _lowercase : Union[str, Any] , _lowercase : Any ):
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Dict = value
... | 709 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
a_ :s... | 250 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase( __a ... | 609 |
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, TimestepEmbedding, Timesteps
from .model... | 469 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_lowerCamelCase : Dic... | 512 | '''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_t... | 512 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers i... | 436 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Union[str, An... | 436 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from... | 315 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[Any] = logging.get_logger(__name__)
__lowercase : Optional[Any] = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretra... | 315 | 1 |
"""simple docstring"""
def __magic_name__ ( _lowerCamelCase : int ):
__a : Dict = abs(lowerCAmelCase_ )
__a : Optional[int] = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _... | 581 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 149 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 687 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
... | 687 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class snake_case ( unittest.T... | 346 |
"""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
... | 346 | 1 |
import numpy as np
def A_( A ):
return 1 / (1 + np.exp(-vector ))
def A_( A ):
return vector * sigmoid(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : List[Any] = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise ... | 486 | 0 |
from random import randint, random
def A_ ( lowercase_ , lowercase_ , lowercase_ , lowercase_ = False , lowercase_ = False , lowercase_ = 5 , ) -> list:
_snake_case : Any = [[-1] * number_of_cells] # Create a highway without any car
_snake_case ... | 326 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main... | 326 | 1 |
def __UpperCamelCase ( _A : str ) ->list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(SCREAMING_SNAKE_CASE_ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').... | 702 |
from collections import deque
from math import floor
from random import random
from time import time
class _SCREAMING_SNAKE_CASE :
def __init__( self )-> List[str]:
lowerCamelCase_ ={}
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SC... | 75 | 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
if i... | 107 |
'''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
SCREAMI... | 301 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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... | 564 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impo... | 564 | 1 |
"""simple docstring"""
import numpy as np
def __lowerCAmelCase ( __UpperCamelCase : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class __magic... | 358 | 0 |
import colorsys
from PIL import Image # type: ignore
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
__lowerCAmelCase = x
__lowerCAmelCase = y
for step in range(UpperCAmelCase__ ): # n... | 719 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
'''configuration_data2v... | 102 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...tes... | 282 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = psutil.Process()
__lowerCAmelCase = False
def ... | 282 | 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, TimestepEmbedding, Timesteps
f... | 586 | """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 ):
lowercase__: Any = {}
def _snake_case ( self , _UpperCAmelCas... | 586 | 1 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
snake_ca... | 404 |
"""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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils i... | 404 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Datase... | 558 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 15 | 0 |
'''simple docstring'''
__magic_name__ : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
_snake_case = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op... | 368 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
__magic_name__ : str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 368 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARC... | 513 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( _a ):
snake_ca... | 619 | 0 |
def _UpperCAmelCase ( _UpperCamelCase : str ) -> int:
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
A_ = gray_code_sequence_string(_lowercase )
#
# convert them to intege... | 718 | '''simple docstring'''
from __future__ import annotations
__snake_case : str = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class __UpperCAmelCase :
'''simple doc... | 174 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowerCamelCase = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import iterto... | 464 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.... | 464 | 1 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer... | 703 | """simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ac... | 558 | 0 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaI... | 459 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils... | 459 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCAmelCase_ = generate_large_matrix()
UpperCAmelCase_ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -... | 706 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
UpperCAmelCase_ = "path-to-your-trained-model"
UpperCAmelCase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
UpperCAmelCase_ = "A photo of sks dog... | 490 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
raise OptionalDepe... | 299 |
"""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 AddedToken
from ...test_t... | 299 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__snake_case :Tuple =datasets.utils.logging.get_logger... | 710 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :int =logging.get_logger(__name__)
__snake_case :Any ={
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class lowerCAmelCase__ ( _lowerCa... | 224 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_a : Union[str, Any] = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method fo... | 145 | 0 |
def __lowercase( UpperCAmelCase__ = 200 ):
"""simple docstring"""
lowerCamelCase = [1, 2, 5, 10, 20, 50, 100, 200]
lowerCamelCase = [0] * (pence + 1)
lowerCamelCase = 1 # base case: 1 way to make 0 pence
for coin in coins:
... | 711 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a_ : Optional[int] = (3, 9, -1_1, 0, 7, 5, 1, -1)
a_ : str = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class lowerCamelCase__ :
"""simple d... | 484 | 0 |
def _lowerCAmelCase ( A__: int , A__: Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = [1]
for i in range(2 , A__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
... | 254 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class lowercase... | 254 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import flo... | 706 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArg... | 74 | 0 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : Tuple = '''MCTCTFeatureExtractor'''
UpperCamelCase_ : Dict = '''AutoTokeni... | 554 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class ... | 554 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
a__: Dict = ['small', 'medium', 'large']
a__: Tuple = 'lm_head.decoder.weight'
a__: Any = 'lm_head.weight'
def UpperCamelCase__( UpperCamelCase__ : str ,... | 212 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging... | 212 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_... | 78 |
"""simple docstring"""
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
snake_case_ : Any = """."""
if __name__ == "__main__":
snake_case_ : List[str] = os.path.join(REPO_PATH, """utils/d... | 595 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowerCAmelCase__ : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
... | 699 | 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 (
TEXT_GUIDED_IM... | 699 | 1 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
""... | 683 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 683 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def A_ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = HfArgumentParser(a )
SCREAMING_SNAKE_CASE_ : Tuple = parser.parse_args_into_data... | 353 |
from math import pi
def A_ ( a , a ):
"""simple docstring"""
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(90, 10))
| 353 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__a :List[Any] = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise Optional... | 86 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import F... | 273 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/x... | 705 | import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase_ ( enum.Enum ):
U... | 71 | 0 |
"""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, TimestepEmbedding, Timesteps
fro... | 680 |
"""simple docstring"""
def lowercase_ ( _snake_case ):
if not head:
return True
# split the list to two parts
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = head.next, head
while fast and fast.next:
SCREAMING_SNAKE_CASE__ : ... | 223 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE_ ( _lowe... | 150 |
"""simple docstring"""
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,
... | 150 | 1 |
def A ( snake_case__ : str ) -> str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
UpperCAmelCase__ : Union[str, Any] = logging.getLogger(__name__)
class __lowercase ... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTok... | 707 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( A ):
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , ... | 510 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_: Optional[Any] = logging.get_logger(__name__)
lowercase_: List[str] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 648 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowercase__ :Tuple = TypeVar('T')
class snake_case ( Generic[T] ):
'''simple docstring'''
def __ini... | 522 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowercase__ ( metaclass=snake_case_ ):
'''simple docstring'''
_snake_case = ['''flax''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ : List[Any] = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
... | 350 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pi... | 7 | '''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 ImageProcessingSaving... | 209 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
'''ClapTextConfig''',
... | 219 |
from __future__ import annotations
def _lowerCAmelCase ( __lowerCAmelCase ) -> list[int]:
"""simple docstring"""
if len(__lowerCAmelCase ) == 0:
return array
snake_case__ , snake_case__ : int = min(__lowerCAmelCase ), max(__lowerCAmelCase... | 219 | 1 |
def snake_case (UpperCamelCase : list[list[float]] ):
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(UpperCamelCase ):
if len(UpperCamelCase ) < i + 1:
data_lists.append([] )
data_lists[i].ap... | 165 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase ( UpperCAmelCase_ ):
"""simple docstring""... | 165 | 1 |
'''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 i... | 709 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : List[str] = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/res... | 398 | 0 |
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
snake_case : List[Any] = logging.get_logger(__name__)
snake_case : ... | 445 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE_... | 445 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : List[Any] , snake_case_ : Optional[int] ):
snake_case__ : ... | 704 |
'''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
i... | 301 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCamelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepende... | 92 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a: Dict = logging.get_logger(__name__)
__a: Optional[int] = {
... | 108 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCAmelCase__ = 100
lowerCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
c... | 702 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordere... | 628 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require... | 46 |
'''simple docstring'''
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : int = 1000 ):
UpperCAmelCase = -1
UpperCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCAmelCase = (n * n... | 447 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 708 |
'''simple docstring'''
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_=None , snake_case_=None ):
lowercase =data
lowercase =previous
lowercase =next_node
def __str__( self ):
return f'{self.data}'
... | 145 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_t... | 390 | '''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : Any = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-ba... | 390 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowerCAmelCase_ : List[Any] = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 impl... | 711 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ : List[Any] = {
'''fac... | 156 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_a = None
try:
import msvcrt
except ImportError:
_a = None
try:
import fcntl
except ImportError:
_a = None
# Backward compatibility
# -------------------------------------------... | 481 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' ,[
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10 ... | 481 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Union[str, Any] =logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] ={
"""google/canine-s""": """https://huggingface.co/google/canine-s... | 197 | """simple docstring"""
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,
... | 197 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorT... | 640 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock impo... | 255 | 0 |
from collections.abc import Sequence
def __UpperCamelCase ( A , A = False ):
if not arr:
return 0
UpperCamelCase__ = 0 if allow_empty_subarrays else float('''-inf''' )
UpperCamelCase__ = 0.0
for num in arr:
UpperCamel... | 713 | def __UpperCamelCase ( A = 600851475143 ):
try:
UpperCamelCase__ = int(A )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be ... | 469 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class SCREAMING_SNAKE_C... | 686 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_devi... | 42 | 0 |
"""simple docstring"""
# 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... | 378 |
"""simple docstring"""
import random
from typing import Any
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
for _ in range(len(lowerCAmelCase ) ):
UpperCAmelCase = random.randint(0 , len(lowerCAmelCase ) - 1 )
... | 378 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( a_ = 1 , a_ = 1000 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = 1
SCREAMING_SNAKE_CASE : Optional[int] = 0
for div... | 251 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase :List[str] = {}
class UpperCAmelCase ( _SCREAMING_SNAKE_CASE ):
... | 251 | 1 |
'''simple docstring'''
def _a ( __lowerCAmelCase : list[int] , __lowerCAmelCase : str ):
"""simple docstring"""
snake_case__ : Any = int(__lowerCAmelCase )
# Initialize Result
snake_case__ : Any = []
# Traverse throu... | 715 |
'''simple docstring'''
def _a ( __lowerCAmelCase : int ):
"""simple docstring"""
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
snake_case__ : Any = 4
snake_case__ : int = (1 << p) ... | 502 | 0 |
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