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"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[s... | 231 |
"""simple docstring"""
import math
import qiskit
def lowercase ( __snake_case : int = 1 , __snake_case : int = 1 , __snake_case : int = 1 ):
if (
isinstance(__snake_case , __snake_case )
or isinsta... | 231 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/mic... | 6 |
'''simple docstring'''
from __future__ import annotations
class __lowercase :
def __init__( self : Union[str, Any] , UpperCAmelCase_ : list[list[int]]):
UpperCamelCase__ : int = TypeError(
'Matrices must be formed fro... | 6 | 1 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def A__ ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = FileLock(str(tmpdir / 'foo.lock' ) )
_lowerCAmelCase = File... | 589 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://hug... | 406 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : List[Any] =... | 713 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : int = logging.get_logger(__name__)
class lowerCAmelCase__ ( UpperCAmelCa... | 570 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_... | 566 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute... | 328 | 0 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
__SCREAMING_SNAKE_CASE : Optional[Any] = namedtuple('covid_data', 'cases deaths recovered')
def _a ( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"... | 2 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if index == number_of_items:
return 0
snake_case_ = 0
snake_case_ ... | 2 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.... | 112 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase_... | 468 | 0 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
SCREAMING_SNAKE_CASE : List[Any] = re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")
@total_order... | 441 |
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 LearnedClassifie... | 441 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable(... | 412 |
def UpperCAmelCase_ ( UpperCAmelCase__ = 1_0_0_0_0_0_0 ):
lowercase_ = set(range(3 , UpperCAmelCase__ , 2 ) )
primes.add(2 )
for p in range(3 , UpperCAmelCase__ , 2 ):
if p not in primes:
continue
primes.difference_update(... | 412 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class A :
def __init__( self : Dict ) -> str:
"""simple docstring"""
UpperCamelCase_ = {}
def lowercase__ ( ... | 700 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__a : Union[str, Any] = TypeVar("""KEY""")
__a : Union[str, Any] = TypeVar("""VAL""")
@dataclass(frozen=lowerCamelCase_ , slots=lowerCamelCase_ ... | 559 | 0 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> None:
_lowercase : str = len(lowerCamelCase_ )
print('The following activities are selected:' )
# The first activity is always selected
_lowercase : Optional[int] = 0
prin... | 89 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__lower... | 467 | 0 |
"""simple docstring"""
def lowercase ( a__ : str , a__ : Dict , a__ : Tuple=False ) -> Optional[Any]:
if isinstance(a__ , a__ ) and isinstance(a__ , a__ ):
_UpperCamelCase = len(set_a.intersection(a__ ) )
if alternative_union:
... | 342 | """simple docstring"""
from timeit import timeit
UpperCAmelCase = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal panama"
}
# Ens... | 342 | 1 |
import math
import sys
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
if number != int(_lowerCAmelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negativ... | 59 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 662 | 0 |
'''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,
... | 718 |
'''simple docstring'''
from __future__ import annotations
import math
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : int ):
a__ : List[str] = u
for i in range(1 , lowerCAmelCase__ ):
a__ : List[Any] = temp * (u - i)
... | 340 | 0 |
def __lowerCAmelCase ( A_ : int ) -> int:
__UpperCAmelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __lowerCAmelCase ( A_ : int = 1_00 ) -> int:
__UpperCAmelCase = 1
__UpperCAmelCase = ... | 221 | import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedu... | 221 | 1 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
imp... | 301 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __snake_case( _lowerCAmelCase = "" ) -> dict[str, float]:
snake_case__ : Tuple = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snak... | 301 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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 ..pip... | 512 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from ... | 512 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependen... | 112 |
"""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
... | 112 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=_A):
"""simple docstring"""
a__ : Any = ["torch", "transformers", "onnx"]
def __init__( self : Union[str, Any] , *__lowerCAmelCase : Union[str, Any] , **__lower... | 2 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeniz... | 641 | 0 |
import socket
def UpperCAmelCase__ ( ) -> Union[str, Any]:
__lowercase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__lowercase = socket.gethostname()
__lowercase = 12_312
sock.connect((host, port) )
sock.... | 703 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"hustv... | 634 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {
"""bert-bas... | 80 |
def UpperCamelCase ( snake_case__ : list ):
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 ,len(grid[0] ) ):
grid[0][cell_n] +=... | 455 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 710 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import ... | 167 | 0 |
from numpy import exp, pi, sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : float = 0.0 , SCREAMING_SNAKE_CASE_ : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2... | 32 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 30 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def UpperCAmelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def decorator(lowerCAmelCase__ ):
__A = getattr(lowerCAmelCase__ , "handle_key" , [] )
handle += [key]
setat... | 706 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer... | 205 | 0 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
fr... | 274 | '''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.u... | 274 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list , SCREAMING_SNAKE_CASE_: int | None = None , SCREAMING_SNAKE_CASE_: int | None = None ) -> None:
'''simple docstring'''
if start is None:
A__ = ... | 626 |
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 import TFCamembertModel
@require_tf... | 626 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 92 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
cl... | 715 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logg... | 233 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled... | 314 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_tor... | 314 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
return x if y == 0 else greatest_common_divisor(UpperCAmelCase__ , x % y )
def _a ( UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
return (x * y) // greatest_common_d... | 482 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 10 ) -> str:
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or n < 0:
raise ValueError('''Invalid input''' )
__SCREAMING_SNAKE_CASE = 10**n
__SCREAMING_SNAKE_CASE = 2_84_3... | 482 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCAmelCase_ : list[int | float] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> Dict:
'''simple docstring'''
if len(UpperCamelCase__ ) == 0:
raise ValueErro... | 715 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = """▁"""
lowerCAm... | 648 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :List[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
... | 86 | """simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__lowercase : str = get_test... | 564 | 0 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
SCREAMING_SNAKE_CASE__ : Tuple = logging.getLogger()
@unittest.skip... | 509 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fl... | 509 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 50_00_00_00 ):
'''simple docstring'''
lowerCamelCase_ = set()
lowerCamelCase_ = int((limit - 24) ** (1 / 2) )
lowerCamelCase_ = set(range(3 , prime_square_limit ... | 70 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Eva... | 687 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteSche... | 405 |
from collections import Counter
from timeit import timeit
def __UpperCamelCase ( _lowerCAmelCase = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def __UpperCamelCase ( _lowerCAmelCase = "" ):
... | 405 | 1 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCamelCase_ (__A ):
__magic_... | 95 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = ("dense.weight", "attention.self.query", "attention.self.... | 55 | 0 |
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 .modeling_utils impo... | 470 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __lowerCAmelCase ( UpperCamelCase ) -> str:
for param in module.parameters():
lowerCAmelCase__ : int = False
def __lowerCAmelCase ( ) -> Optional[Any]:
lowerCAmelCase__ ... | 470 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__ ( _lowercase ):
__magic_name__ : List[Any] = ["image_processor", "tokenizer"]
__magic_name__ : Optional[Any] = "C... | 507 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependen... | 507 | 1 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ = "AAPL" ):
UpperCamelCase__ : Tuple = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
UpperCamelCase__ : str = BeautifulSoup(requests.get(UpperCamelCase__ ).te... | 462 |
import doctest
from collections import deque
import numpy as np
class _lowerCamelCase :
"""simple docstring"""
def __init__( self ) -> None:
"""simple docstring"""
UpperCamelCase__ : Tuple = [2, 1, 2, -1]
UpperCamelCase__ ... | 462 | 1 |
# 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 switching between checkouts... | 84 |
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_... | 322 | 0 |
from collections.abc import Generator
from math import sin
def _a ( __SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
if len(_UpperCAmelCase ) != 32:
raise ValueError('Input must be of length 32' )
_lowerCAmelCase = b''
for i in [3, 2, 1, ... | 716 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 585 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowerCAmelCase__ = 'scheduler_config.json'
class lowerCAmelCase__ ( a):
... | 503 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
'JukeboxVQVAEConfig',
... | 503 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase):
if (ksize % 2) == 0:
... | 704 |
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
class _a ... | 618 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 658 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case = 10
def _A ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , S... | 658 | 1 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, P... | 360 |
def __UpperCamelCase ( a, a, a=False) ->Dict:
if isinstance(a, a) and isinstance(a, a):
lowerCamelCase__ = len(set_a.intersection(a))
if alternative_union:
lowerCamelCase__ = len(a) + len(a)
else:
... | 360 | 1 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCa... | 130 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F40... | 130 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any ... | 705 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''SpeechT5FeatureExtractor'''
lowerCamelCase__ = '''SpeechT5Tokenizer'''
def __init__( self :List[Any] , _lo... | 401 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 52 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (DDPMScheduler,)
... | 52 | 1 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from ut... | 702 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaF... | 14 | 0 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_ava... | 350 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStru... | 350 | 1 |
"""simple docstring"""
import random
def __A ( a_ :Union[str, Any] , a_ :List[Any] , a_ :List[Any]) -> Union[str, Any]:
__a : str = a[left_index]
__a : Optional[int] = left_index + 1
for j in range(left_... | 101 |
"""simple docstring"""
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_tokeni... | 101 | 1 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__A ='<<<<<<< This should probably be modified because it mentions: '
__A ='===... | 407 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_t... | 407 | 1 |
from typing import Any
import numpy as np
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :np.ndarray ) -> bool:
return np.array_equal(SCREAMING_SNAKE_CASE , matrix.conjugate().T )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :np.ndarray , SCREAMING_SNAKE_... | 240 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class snake_case_ ( pl.LightningModule ):
def __init__( self : Union[str, Any] , _snake_case : List[str] )... | 240 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
def __init__( self :int ,_UpperCamelCase :int ):
snake_case_ : str = num_of_nodes
snake_case_ : list[list[int]] = []
... | 334 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require... | 334 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase = get_tests... | 715 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"""vocab_file""": """vocab.json""",
... | 150 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 591 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
fr... | 294 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.te... | 713 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common imp... | 344 | 0 |
'''simple docstring'''
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_d... | 275 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__SCREAMIN... | 220 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
... | 538 | """simple docstring"""
import collections
import os
import re
from pathlib import Path
_A = 'src/transformers'
# Matches is_xxx_available()
_A = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
_A = re.compile(R'^_import_stru... | 538 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Union[str, Any] = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config... | 287 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Dict = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''... | 287 | 1 |
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_gpu, require_vision, sl... | 380 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def A__ ( self ) -> List[str]:
'''simple docstring'''
_lowercase =0
_lowercase =[0]
_lowercase ... | 380 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_visio... | 542 |
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_b... | 542 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class __lowercase( nn.Module ):
... | 585 |
from math import isqrt
def _a ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(__SCREAMING_SNAKE_CASE ) + 1 ) )
def _a ( __SCREAMING_SNAKE_CASE : int = 10**6 ):
... | 585 | 1 |
"""simple docstring"""
from typing import Any
import numpy as np
def lowercase_ ( _lowercase : np.ndarray ):
'''simple docstring'''
return np.array_equal(_lowercase , matrix.conjugate().T )
def lowercase_ ( _lowercase : np.ndarray , ... | 595 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import clas... | 595 | 1 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive"""... | 711 | from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __snake_case :
def __init__( self : Union[str, Any] , _lowercase : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ ... | 379 | 0 |
"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils impo... | 426 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_visio... | 426 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case_ : Tuple = logging.get_logger(__name__)
snake_case_ : Tuple ... | 644 |
'''simple docstring'''
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int ) -> str:
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCAmelCase_ : Union[str, Any] = len(bin(SCREAMING_SNAKE_CASE__ )[3:] )
... | 644 | 1 |
def lowerCAmelCase_ ( __a ) -> list:
"""simple docstring"""
for i in range(len(__a ) - 1 , 0 , -1 ):
lowerCamelCase__: List[Any] =False
for j in range(__a , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]... | 59 |
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_common impo... | 276 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 368 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
... | 368 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {}
try:
if not is_sentencepiece_available():
... | 645 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def a__ ( ):
... | 645 | 1 |
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 import GenerationTesterMixin
... | 710 |
import string
import numpy
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , SCREAMING_SNAKE_CASE_ )
class snake_case_ :
lowerCamelCase :Tuple = string.ascii_... | 262 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def a__ ( lowerCAmelCase ) -> Optional[int]:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowerCamelCase ( _sna... | 182 |
__UpperCAmelCase = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_lib... | 406 | 0 |
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return number | (1 << position)
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return number & ~(1 << position)
def UpperCAmelCa... | 719 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) ... | 24 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impo... | 532 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
... | 477 | 0 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def UpperCamelCase_ ( A__ ):
a_ = [
"""decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""... | 701 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common imp... | 511 | 0 |
a__: dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_... | 190 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_av... | 190 | 1 |
import numpy as np
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = int(np.ceil((x_end - xa) / h... | 719 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils... | 0 | 0 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionCo... | 218 | '''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__Upp... | 546 | 0 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
A__: int = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowerCAmelCase_ ( ... | 716 |
def lowerCAmelCase_ ( A_):
UpperCamelCase__: Union[str, Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCAmelCase_ ( A_):
UpperCamelCase__... | 221 | 0 |
'''simple docstring'''
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
lowerCAmelCase__ : Union[str, Any... | 347 |
"""simple docstring"""
def UpperCAmelCase_ ( __a : int ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = int(__a )
if decimal in (0, 1): # Exit cases for the recursion
return str(__a )
_lowerCamelCase , _lowerCame... | 437 | 0 |
from copy import deepcopy
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , a__ = None , a__ = None ):
if arr is None and size is not None:
A_ : Dict = size
A_ : str = [0] * s... | 711 |
from typing import List
from .keymap import KEYMAP, get_character
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
def decorator(_lowerCAmelCase ):
A_ : List[Any] = getattr(_lowerCAmelCase ,"""handle_key""" ,[] )
handle += [key]
setat... | 481 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Inform... | 105 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class A_ ( __lowercase ):
'''simple docstring'''
def __init__( self , *_A ,... | 485 | 0 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCAmelCase : List[str] = logging.getLogger(__name__)
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : int = """masked_bert"""
def __init__( self , ... | 39 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __lowerCAmelCase ( lowerCamelCase : bytes , lowerCamelCase : int ):
'''simple docstring'''
__lowerCAmelCase = f''... | 39 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( __lowercase , __lowercase ):
UpperCamelCase_ : int =get_failure_array(_lowercase )
# 2) Step through text searching for pattern
UpperCamelCase_ : int =0, 0 # index into text, pattern
while i < len(_l... | 357 |
"""simple docstring"""
def lowercase_ ( _lowercase : int ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError("only integers accepted as input" )
else:
UpperCAmelCase : Optional[int] = ... | 595 | 0 |
from math import factorial
def UpperCAmelCase ( lowercase__ : int = 20 ):
'''simple docstring'''
a__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
a__ = n // 2
return int(factorial(lowercase__ ) / (fa... | 412 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerc... | 412 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowercase ( __lowerCamelCase ):
... | 65 |
"""simple docstring"""
import requests
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : Optional[Any] = requ... | 65 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import ... | 79 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
__UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def __A ... | 79 | 1 |
import math
def lowercase ( __A : int ) -> List[str]:
'''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 3 are no... | 36 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def SCREAMING_SNAKE_CASE ( a_ : str , a_ : Union[str, Any] , a_ : Dict ):
__a = {
'en': 'Machine learning is grea... | 539 | 0 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_dif... | 706 | '''simple docstring'''
def _lowerCAmelCase ( __a , __a ) -> float:
'''simple docstring'''
def get_matched_characters(__a , __a ) -> str:
_UpperCamelCase :Any =[]
_UpperCamelCase :List[str] =min(len(_stra ) , len(_stra ... | 512 | 0 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : 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, 0, 1, all e... | 8 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all ViT MSN models at https://hu... | 332 | 0 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if a == 0:
raise ValueError('''Coefficient \'a\' must not be zero.'''... | 718 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__UpperCamelCase : Any = None
try:
import msvcrt
except ImportError:
__UpperCamelCase : Optional[Any] = None
try:
import fcntl
ex... | 106 | 0 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
if edge <= 0 or not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("""Length must be a positive.""" )
return 3 *... | 433 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 433 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
"huggingface/time-series-transformer-tourism... | 367 |
import pytest
import datasets
# Import fixture modules as plugins
UpperCAmelCase_ : Union[str, Any] = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
# Mark tests as "unit" by defa... | 367 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase__ :
_UpperCAmelCase :int
_UpperCAmelCase :int
class lowercase__ :
def __init... | 153 |
"""simple docstring"""
from pathlib import Path
import fire
def _snake_case ( lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : int ) -> int:
lowerCamelCase_ : Any =Path(lowerCamelCase__ )
... | 153 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_... | 313 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
fro... | 313 | 1 |
'''simple docstring'''
import requests
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ) -> None:
UpperCAmelCase : List[Any] = {'''Content-Type''': '''application/json'''}
UpperCAmelCase : List[str] ... | 127 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
cl... | 232 | 0 |
"""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_i... | 707 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@staticmethod
@abstractmethod
def _SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CAS... | 615 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline... | 436 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
__magic_name__ : Optional[Any] = ... | 436 | 1 |
from __future__ import annotations
from typing import Any
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 0):
'''simple docstring'''
... | 99 |
from __future__ import annotations
import time
a__ = list[tuple[int, int]]
a__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
... | 99 | 1 |
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