code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
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 (
FlaxF... | 262 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = "cpu" , lowerCAmelCase_ = None )-> None:
lowerCAmelCase_ : str = torch.load(lowerCAmelCase_ , map_location=low... | 262 | 1 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCAmelCase : Optional[Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase : str = """\
@... | 148 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Any = (PNDMScheduler,)
_lowercase : str = (("""num_infe... | 148 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCamelCase (lowercase_: dict ) -> tuple:
return (data["dat... | 192 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : Optional[Any] ... | 192 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCamelCase ( a_ = "" ) -> dict[str, float]:
lowerCAmelCase_ = url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
lowerCAmelCase_ = ... | 14 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemant... | 14 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ : List[Any] ={
'''configuratio... | 70 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 264 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impo... | 17 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Layo... | 17 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFre... | 30 |
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
... | 30 | 1 |
"""simple docstring"""
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
def count_of_possible_combinations(SCREAMING_SNAKE_CASE__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_co... | 355 |
import datasets
lowercase_ = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n and S... | 194 | 0 |
"""simple docstring"""
def lowercase_ ( ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = 0
for i in range(1 , 1001 ):
total += i**i
return str(_UpperCamelCase )[-10:]
if __name__ ... | 135 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowercase ( _UpperCamelCase = 8 ) ->str:
"""simple docstring"""
lowercase : List[str] = ascii_letters + digits + punctuation
... | 337 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
UpperCAmelCase : Optional[Any] = {
"""linear""": PIL.Image.Resampling.BILINEAR,
... | 148 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 148 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _a ( UpperCamelCase__ , UpperCamelCase__):
"""simple docstring"""
@r... | 149 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_co... | 149 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
),
#... | 368 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _snake_case :
def __init__( self , _lowerCamelCase):
UpperCAmelCase__ : Any = data
UpperCAmelCase__ ... | 163 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import ... | 163 | 1 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : List[Any] = get_failure_array(SCREAMING_SNAKE_CASE_ )
# 2) St... | 315 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Union[str, Any] = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfi... | 105 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 55 | 0 |
"""simple docstring"""
import numpy as np
def __UpperCAmelCase ( UpperCAmelCase_ : np.array ) -> np.array:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 95 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_rofo... | 95 | 1 |
import unittest
from transformers import 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 ModelTesterMixin, ids_tensor
from .... | 287 |
def _a ( lowerCamelCase ):
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 287 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowercase_ ( A__ ) -> ... | 137 |
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 ModelTesterMixi... | 137 | 1 |
"""simple docstring"""
# 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 sw... | 74 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A_ : str = logging.get_logger(__name__)
# TODO: upload to AWS
A_ : Optional[int] = {
"yjernite/retribert-base-uncased": (
"https://huggingface.... | 165 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Optional[Any] = {
"configuration_whisper": ["WHISPER_PRETRAINED... | 358 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling... | 99 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.... | 88 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
fr... | 58 | 0 |
def UpperCamelCase ( __lowerCamelCase : int ):
if n == 1 or not isinstance(__lowerCamelCase , __lowerCamelCase ):
return 0
elif n == 2:
return 1
else:
snake_case : Optional[Any] = [0, 1]
for i in... | 10 |
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
__lowerCamelCase = """."""
if __name__ == "__main__":
__lowerCamelCase = os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
... | 10 | 1 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from ... | 91 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
A: Optional[Any] = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 109 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
... | 67 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import Ba... | 67 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi... | 155 |
"""simple docstring"""
from __future__ import annotations
def lowercase (snake_case__ : list[int] ) -> list[int]: # This function is recursive
'''simple docstring'''
lowerCAmelCase = len(snake_case__ )
# If the array contains only... | 155 | 1 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def snake_case_ ( __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CAS... | 264 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require... | 264 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 193 |
from __future__ import annotations
import math
def UpperCamelCase__( UpperCamelCase__ : int )->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, ... | 193 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXL... | 365 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCAmelCase_ = logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> Any:
UpperCamelCase__ : Dict = argparse.Arg... | 247 | 0 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..u... | 92 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.mode... | 92 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCAmelCase_ : Any = logging.getLogger(__name__)
class lowerCAmelCase__ :
'''simple docstri... | 370 |
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Tuple , lowercase_ : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ ... | 318 | 0 |
def A_ ( A__ , A__ ) -> str:
a__ : list[list[str]] = [[] for _ in range(A__ )]
a__ : Optional[int] = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 or len(A__ ) <= key:
retur... | 99 |
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_transformer import P... | 99 | 1 |
"""simple docstring"""
import os
import sys
import transformers
_a = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
... | 370 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers im... | 144 | 0 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_m... | 295 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enabl... | 295 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_at... | 354 |
import baseaa
def lowerCamelCase ( a_ ) -> bytes:
return baseaa.baaencode(string.encode('utf-8' ) )
def lowerCamelCase ( a_ ) -> str:
return baseaa.baadecode(a_ ).decode('utf-8' )
if __name__ == "__main__":
lowerCamelC... | 14 | 0 |
'''simple docstring'''
from __future__ import annotations
class snake_case__ :
def __init__( self : List[Any] , _A : List[Any] ) -> Union[str, Any]:
UpperCAmelCase_ : Optional[int] = TypeError(
'''Matrices must be formed from a list... | 304 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
... | 163 | 0 |
def A ( lowercase ) -> int:
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
UpperCamelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(lowercase )
if number < 1:
UpperCamelCase = f'''Input value of [numb... | 110 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 110 | 1 |
from collections import namedtuple
_lowerCamelCase : Tuple = namedtuple("""from_to""", """from_ to""")
_lowerCamelCase : Dict = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00_454, 264... | 14 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int, _lowerCAmelCase : int ) -> int:
_UpperCAmelCase : str = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_UpperCAmelCase : Dict = n - k
# Calculat... | 246 | 0 |
from math import ceil, sqrt
def snake_case_ (__A : int = 1_0_0_0_0_0_0 ) -> int:
__lowerCAmelCase : Optional[Any] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCAmelCase : List[... | 139 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__UpperCAmelCase = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7""... | 139 | 1 |
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
UpperCAmelCase__ = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """atte... | 289 | """simple docstring"""
import math
class a :
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : list[list[float]] , __lowerCAmelCase : list[int] ):
_UpperCAmelCase = 0.0
_UpperCAmelCase = 0.0
for i in range(len(__lowerCAm... | 289 | 1 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEX... | 81 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a ( __lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCAmelC... | 81 | 1 |
"""simple docstring"""
import numpy
# List of input, output pairs
__UpperCAmelCase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__UpperCAmelCase = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
__UpperCAmelCa... | 84 |
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 GenerationTeste... | 244 | 0 |
import requests
_UpperCAmelCase = """""" # <-- Put your OpenWeatherMap appid here!
_UpperCAmelCase = """https://api.openweathermap.org/data/2.5/"""
def UpperCamelCase ( __lowercase : str = "Chicago" ,__lowercase : str = APPID ):
'''simple docstring'''
... | 350 | 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 be checked befor... | 192 | 0 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Any = [0] * len(_lowercase )
snake_case_ :List[str] = []
snake_case_ :int = []
snake_case_ :Union[str, Any] = 0
for values in graph.values():
... | 66 |
from heapq import heappop, heappush
import numpy as np
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> tuple[float | int, list[tuple[int, int]]]:
'''simple docstring'''
lowercase , lowercase : Op... | 308 | 0 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
_lowercase : str = logging.get_logger(__name__)
_lowercase : ... | 91 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCamelCase__:
__magic_name__ : List[str]
__magic_name_... | 91 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class UpperCAmelCase_ (UpperCamelCase_ ):
"""simple docstring"""
lowerCamelCase : str
lowerCamelCase : int
def UpperCAmelCase__ ( UpperCAmelCase_ : List[str] ... | 185 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str
_SCREAMING_SNAKE_CASE :int
def _lowercase ( __lowerCAmelCase ) -> list[str]:
if not ... | 132 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCAmelCase__ : Any = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"... | 37 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 37 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
A__ : Any = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot... | 185 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : List[str] ... | 185 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils imp... | 328 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 328 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : str = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.js... | 252 |
import enum
import shutil
import sys
UpperCAmelCase, UpperCAmelCase : Union[str, Any] = shutil.get_terminal_size()
UpperCAmelCase : Dict = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class __lowercase ( enum.Enum ):
"""simple docstring"""
UpperCamel... | 252 | 1 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...on... | 241 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licen... | 241 | 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
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
"facebook/data2vec-vision-base-ft": ... | 216 |
# 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 ... | 342 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require... | 350 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = """▁"""
__lowerCam... | 10 | 0 |
import math
import unittest
def A_ ( a ):
"""simple docstring"""
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif numb... | 253 | '''simple docstring'''
import math
def __UpperCAmelCase ( a_: int ):
return math.sqrt(a_ ) * math.sqrt(a_ ) == num
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : List[str] = n
while ... | 145 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig",
]... | 369 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCamelCase : Dict = namedtuple("covid_data", "cases deaths recovered")
def _UpperCAmelCase (UpperCamelCase_ : str = "https://www.worldometers.info/coronavirus/" ):
'''simple docstring'''
... | 159 | 0 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowercase_ ( a__ ):
def __init__( self , a , a ):
super().__init__()
se... | 80 |
'''simple docstring'''
def _UpperCamelCase ( __A ) -> int:
'''simple docstring'''
UpperCamelCase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _UpperCamelCase ( __A = 100 ) ... | 80 | 1 |
from itertools import permutations
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 !=... | 352 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
sna... | 200 | 0 |
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__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"hustvl/yolos-small":... | 302 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a : L... | 114 | 0 |
'''simple docstring'''
import functools
def _snake_case ( A , A ) -> int:
# Validation
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days should be a list of integers''' ... | 359 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_... | 228 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class a__ :
def __init__( self , _a=None , _a=None ):
lowercase : Tuple = list(poly_a or [0] )[:]
lowercase : List[str] = list(poly_b or [0]... | 202 | import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied f... | 65 | 0 |
"""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__ : Any = {
'shi-labs/dinat-mini-i... | 350 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def _snake_case ( lowerCamelCase__ : float , lowerCamelCase__ : float , lowerCamelCase__ : float ) -> tuple:
lowerCamelCase_ : Optional[Any] =... | 209 | 0 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_... | 37 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mas... | 306 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
UpperCAmelCase : Optional[Any] = 'examples/'
UpperCAmelCase : List[Any] = {
'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")... | 320 |
"""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 ... | 320 | 1 |
"""simple docstring"""
def __lowercase ( _a , _a ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
snake_case_ : Dict = str(bin(_a ) )[2:] # remove the leading "0b"
snake_case_ : Optional[int] = str(bin(_a ) )[2... | 264 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ... | 264 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _snake_case ( lowerCamelCase__ : List[str] ) -> str:
if "model" in orig_key:
lowerCamelCase_ : Dict =orig_key.replace("model." , ""... | 209 |
"""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... | 209 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart impor... | 27 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, lo... | 366 |
"""simple docstring"""
import argparse
import datetime
def __lowerCAmelCase (_UpperCamelCase ):
__lowerCAmelCase : Optional[Any] = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',... | 182 | 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requir... | 271 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMSchedu... | 202 | 0 |
def lowerCAmelCase( __lowerCamelCase ):
__a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCAmelCase( __lowerCamelCase = 100 ):
__a = 1
__a = 2
for i in range(2 , max... | 197 | from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__snake_case ):
A__ : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(... | 197 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_av... | 7 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 7 | 1 |
def lowerCAmelCase_ ( _snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
__magic_name__ : str = len(lowercase__ )
for _ in range(lowercase__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_... | 362 |
import re
import string
import numpy as np
import datasets
snake_case : Any = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
snake_case : Optional[Any] = "\nArgs:\n predict... | 41 | 0 |
import argparse
import os
import re
import packaging.version
A__ : Dict = '''examples/'''
A__ : Any = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^... | 103 |
from pathlib import Path
import fire
def UpperCamelCase( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : int ):
lowerCAmelCase_ : List[str] = Path(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = Path(__UpperCamelCase )
d... | 103 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
snake_case_ = 2048
snake_case_ = 4096
snake_case_ = 42
snake_case_ = os.environ.pop('PROCESS_TRAIN', 'false')
snake_case_ = {'null': 0, 'short': 1, '... | 366 |
from __future__ import annotations
snake_case_ = [True] * 1000001
snake_case_ = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
snake_case_ = False
i += 1
def lowerCamelCase__ ( s... | 238 | 0 |
import random
from typing import Any
def _UpperCAmelCase ( a__):
'''simple docstring'''
for _ in range(len(a__)):
a_ : Tuple = random.randint(0 , len(a__) - 1)
a_ : List[str] = random.randint(0 , len(a__) - 1)
a_ , a_ : str ... | 248 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_i... | 248 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0,... | 38 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> bool:
SCREAMING_SNAKE_CASE = int(number**0.5 )
return number == sq * sq
def ... | 38 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils i... | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[int] = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whisper... | 6 | 1 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tra... | 253 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 253 | 1 |
from typing import List
import numpy as np
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
__A = {key: len(a_ ) for key, value in gen_kwargs.items() if isinstance(a_ , a_ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError... | 15 |
'''simple docstring'''
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 _UpperCamelCase ... | 80 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowercase__ ( __snake_c... | 145 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
... | 145 | 1 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def lowercase_ ( _... | 184 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase__ : List[str... | 25 | 0 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_i... | 101 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__m... | 101 | 1 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the ro... | 169 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 173 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_a = logging.get_logger(__name__)
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
def __init__( self : int... | 371 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_a = True
except (ImportError, ModuleNotFoundError):
_a = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _A ( UpperCamelCase_ ... | 144 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : List[str] = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_A... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class __UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 0
_snake_case = [0]
_sna... | 160 |
'''simple docstring'''
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = name
_snake_case = value
_snake_case ... | 160 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase=(), __UpperCAmelCase=None, __UpperCAm... | 56 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simp... | 56 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list:
if len(UpperCAmelCase__ ) == 0:
return []
A_ , A_ = min(UpperCAmelCase__ ), max(UpperCAmelCase__ )
A_ = int(max_value - min_value ... | 101 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_blenderbot''': [
... | 101 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 94 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExt... | 236 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadMod... | 204 | from __future__ import annotations
__lowerCamelCase : Dict = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase : Union[str, Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __SCREAMING_SNAKE_CASE ( __UpperCamelCas... | 204 | 1 |
from __future__ import annotations
def __snake_case ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int ) -> bool:
if len(__lowercase ) == 0:
return False
A_ : str = len(__lowercase ) // 2
if a_list[midpoint] == item:
return True
if ite... | 300 |
import numpy as np
def a_ ( __lowercase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 282 | 0 |
'''simple docstring'''
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base... | 187 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
UpperCAmelCase = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def _snake_case ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : i... | 187 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __lowerCamelCase ( lowerCamelCase__ , lowerC... | 130 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Dict = ArgumentParser(
description=(
... | 130 | 1 |
from functools import lru_cache
@lru_cache
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
if num < 0:
raise ValueError("Number should not be negative.")
return 1 if num in (0, 1) else num * facto... | 367 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase : Optional[int] = get_tests_dir('fix... | 151 | 0 |
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 __lowerCamelCase ( snake_case__ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CAS... | 306 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __lowercase ( ):
print('Making key files...' )
make_key_files('rsa' , 1_0_2_4 )
print('Key files generation succes... | 240 | 0 |
import socket
def lowerCamelCase__ ( ) -> str:
__snake_case = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__snake_case = socket.gethostname()
__snake_case = 1_2312
sock.connect((host, port) )
sock.send(B'''Hello server!''' )
... | 356 |
# Algorithm for the pigeonhole sorting
def lowerCamelCase__ ( snake_case_ : int ) -> Optional[int]:
__snake_case = min(snake_case_ ) # min() finds the minimum value
__snake_case = max(snake_case_ ) # max() finds the maximum value
... | 238 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
snake_case : Optional[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
... | 94 |
import string
import numpy
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , UpperCAmelCase_ )
class _snake_case :
SCREAMING_SNAKE_CASE__ = ... | 94 | 1 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCamelCase_ =637_8137.0
UpperCamelCase_ =635_6752.31_4245
UpperCamelCase_ =6_378_137
def a_ ( _lowercase , _lowercase... | 128 |
"""simple docstring"""
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 i... | 128 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import ... | 278 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase = "cpu" ,lowercase = None ) -> None:
snake_case : int = torch.load(lowercase ,map_location=lowercase )
for k, v in tqdm(state_... | 124 | 0 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exi... | 363 |
'''simple docstring'''
def lowercase ():
"""simple docstring"""
_lowerCAmelCase : Optional[int] = [3_1, 2_8, 3_1, 3_0, 3_1, 3_0, 3_1, 3_1, 3_0, 3_1, 3_0, 3_1]
_lowerCAmelCase : int = 6
_... | 25 | 0 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
debug_launcher(t... | 227 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils imp... | 265 | 0 |
'''simple docstring'''
import math
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of ... | 8 |
'''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... | 8 | 1 |
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