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 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__... | 96 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _Uppe... | 367 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPri... | 47 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : List[Any] = 0
if start < end:
... | 47 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class Uppe... | 487 |
lowerCamelCase :Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list[s... | 487 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import requests
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
... | 706 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowercase = 300 # TEMPERATURE (unit = K)
def A (__lowerCamelCase :float , __lowerCamelCase :float , __lowerCamelCase :float , ):
if donor_conc <= 0:
raise V... | 162 | 0 |
"""simple docstring"""
_lowerCAmelCase = """Tobias Carryer"""
from time import time
class __UpperCamelCase :
def __init__( self ,_A ,_A ,_A ,_A=int(time() ) ): # noqa: B008
'''simple docstring'''
_lowerCAmelCase :... | 259 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_alig... | 259 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class __snake_case ( UpperCame... | 717 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = list(snake_case__)
lowerCAmelCase_ : Tuple = list(snake_case__)
lowerCAmel... | 683 | 0 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
class __magic_name__ ( A__):
'''simple docstring'''
def __init__( self: Optional[Any] , ... | 234 |
from __future__ import annotations
_lowercase : Optional[int] =1.6021E-19 # units = C
def lowerCAmelCase_ ( _lowercase : float , _lowercase : float , _lowercase : float , ) -> tuple[str, float]:
"""simple docstring"""
if (condu... | 136 | 0 |
"""simple docstring"""
import operator as op
lowercase = '''scaler.pt'''
lowercase = '''pytorch_model'''
lowercase = '''random_states'''
lowercase = '''optimizer'''
lowercase = '''scheduler'''
lowercase = '''pytorch_model... | 468 |
"""simple docstring"""
def _lowerCAmelCase ( __lowerCamelCase:list , __lowerCamelCase:list ):
'''simple docstring'''
_validate_point(__lowerCamelCase )
_validate_point(__lowerCamelCase )
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
... | 468 | 1 |
def __A(lowerCAmelCase , lowerCAmelCase ) -> list:
"""simple docstring"""
_UpperCamelCase = len(lowerCAmelCase )
_UpperCamelCase = []
for i in range(len(lowerCAmelCase ) - pat_len + 1 ):
_UpperCamelCase = True
for j in range(lowerCAmelCa... | 612 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __A(lowerCAmelCase , lowerCAmelCase ) -> np.array:
"""simple docstring"""
_UpperCamelCase = F'{sampling_rate}'
_UpperCamelCase = """1"""
_UpperCam... | 612 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_lowerCamelCase ... | 710 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
UpperCAmelCase_ : Tuple = {"+", "-", "*", "/"}
... | 323 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging... | 328 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowerCamelCase :Dict = get_logger(__name__)
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=None ):
A_ : Optional[int] = attrs or []
if m... | 667 | 0 |
'''simple docstring'''
UpperCamelCase_ = '''Input must be a string of 8 numbers plus letter'''
UpperCamelCase_ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def lowerCamelCase ( UpperCAmelCase__ : List[Any] ) -> bool:
'''simple docstring'''
if not isinstance(__SCREAMING_... | 701 | '''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _SCREAMING_SNAKE_CASE:
A_ : int
A_ : int
class _SCREAMING_SNAKE_CASE:
def __init__( self ... | 320 | 0 |
from __future__ import annotations
class lowerCamelCase:
'''simple docstring'''
def __init__( self , snake_case_=None ):
_A = data
_A = None
def __repr__( self ):
_A = []
... | 27 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase( un... | 27 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__lowerCamelCase = (3, 9, -11, 0, 7, 5, 1, -1)
__lowerCamelCase = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
lowercase = ... | 704 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_ava... | 667 | 0 |
from __future__ import annotations
def UpperCamelCase ( _a ) -> list[int]: # This function is recursive
'''simple docstring'''
lowercase_ :Any = len(_a )
# If the array contains only one element, we return it (it's the stop condition o... | 257 |
import math
def UpperCamelCase ( _a ) -> bool:
'''simple docstring'''
lowercase_ :int = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_a )
def UpperCamelCase ( _a = 1 / 1_2_3... | 257 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"BridgeTower/bridgetower-base": "https:/... | 712 |
'''simple docstring'''
def _a ( _lowercase : int = 50000000 ):
'''simple docstring'''
__UpperCAmelCase : List[str] = set()
__UpperCAmelCase : Tuple = int((limit - 24) ** (1 / 2) )
__UpperCAmelCase... | 266 | 0 |
from __future__ import annotations
def A ( lowercase__ : int ) -> list[int]:
UpperCamelCase__ :Tuple = 2
UpperCamelCase__ :Dict = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(lowercase__ )
if n > 1:
factors.append(lowercase__ )
retu... | 45 |
'''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
_SCREAM... | 369 | 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.ut... | 506 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...u... | 506 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class __lowercase ( __lowerCamelCase ... | 65 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow ... | 383 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Tuple = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDepe... | 663 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __A ( unittest.TestCase ):
def __A ( self ):
_lowerCAmelCa... | 663 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : List[str] = {}
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE... | 69 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCamelCase : Union[str, Any] =parse(importlib.metadata.version("torch"))
def lowerCamelCase_ ( A_ , A_ ... | 316 | 0 |
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
I... | 705 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_na... | 582 | 0 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
... | 112 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : int = 1 << 8
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLA... | 112 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_fl... | 625 |
"""simple docstring"""
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def snake_case__ ... | 625 | 1 |
import os
def a ( snake_case__: str = "matrix.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_lowerCamelCase ) , _lowerCamelCase ) ) as in_file:
lowercase_ = in_file.read()
lowercase_ = [[int(_lowerCamelCase )... | 97 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'... | 24 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {"""vocab_file""": """sentencepiece.model"""}
_a = ... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Dict = logging.get_logger(__name__)
_a : Optional[Any] = {
"""caidas/swin2sr-classicalsr-x2-64""": (
"""https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json... | 111 | 0 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
UpperCAmelCase__ : Union[str, Any] = Fals... | 48 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
... | 48 | 1 |
from itertools import count
def lowerCamelCase__ (_UpperCAmelCase = 50):
SCREAMING_SNAKE_CASE = [1] * min_block_length
for n in count(_UpperCAmelCase):
fill_count_functions.append(1)
for block_length in range(_UpperCAmelCase , n + 1):
for block_start in ... | 444 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 444 | 1 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Token... | 426 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class _A ( _lowerCamelC... | 217 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__lowerCAmelCase , n - ... | 239 |
"""simple docstring"""
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
if is_vision_available():
from PI... | 239 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_h... | 25 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ):
# Initiali... | 307 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( snake_case = 4 ) -> list[list[int]]:
_UpperCAmelCase = abs(snake_case ) or 4
return [[1 + x + y * row_size for x in range(snake_case )] for y in range(snake_case )]
... | 175 |
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> bool:
_UpperCAmelCase = len(snake_case ) + 1
_UpperCAmelCase = len(snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i o... | 175 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCAmelCase = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''sto... | 84 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_... | 495 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_A : Tuple = logging.get_logger... | 721 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_A : Optional[Any] = logg... | 518 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 575 |
"""simple docstring"""
from __future__ import annotations
def snake_case__ ( _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, ) ->tuple:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more o... | 575 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if ... | 700 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 6008_5147_5143 ):
try:
lowercase = int(__SCREAMING_SNAKE_CASE )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater tha... | 565 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase_ ( tf.keras.layers.Layer ):
def __init__( self , _lower... | 66 |
from collections.abc import Sequence
def __UpperCamelCase ( _A , _A = False ):
if not arr:
return 0
lowerCAmelCase_ = 0 if allow_empty_subarrays else float('''-inf''' )
lowerCAmelCase_ = 0.0
for num in arr:
lowerCAmelCase_ ... | 431 | 0 |
def _SCREAMING_SNAKE_CASE ( __snake_case = 1_0_0_0 ) -> int:
_UpperCAmelCase = 2**power
_UpperCAmelCase = str(__snake_case )
_UpperCAmelCase = list(__snake_case )
_UpperCAmelCase = 0
for i in list_num:
sum_of_num... | 708 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase ) , '''Tatoeba d... | 402 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 102 |
from math import isclose, sqrt
def snake_case__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float ):
lowerCAmelCase__ :Optional[int] = point_y / 4 / point_x
lowerCAmelCase__ :Tuple = 2 * normal_gradient / (1 + norm... | 145 | 0 |
"""simple docstring"""
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self , lowerCAmelCase__ ):
'''simple docstring'''
_UpperCamelCase : List[str] = size
_UpperCamelCase : Optional[int] = [0] * size
... | 239 |
"""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_tokenizers
from .... | 239 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 312 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 0 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 650 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 650 | 1 |
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ ):
return round(float(moles / volume ) * nfactor )
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ ):
return round(float((moles * 0.0_821 * temperature) / (volum... | 226 |
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 .tokenization_fnet ... | 583 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_co... | 414 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMi... | 414 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ = {
'configuration_blenderbot_small': [
'BLENDERBOT_SMALL_P... | 14 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 557 | 0 |
'''simple docstring'''
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
imp... | 686 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nest... | 686 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_snake_case = _symbol_database... | 500 |
def A ( _lowerCamelCase ):
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = F"Input value of [number={number}] must be an integer"
raise TypeError(_lowerC... | 500 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 4000000 ):
_lowercase : List[Any] = [0, 1]
_lowercase : Dict = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
... | 712 |
"""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()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyN... | 600 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( UpperCamelCase__ ):
"""simple docstring"""
_lowerCamelCase = (PNDMScheduler,)
... | 675 |
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"hustvl/yolos-small": "https... | 307 | 0 |
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 __snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
@register_to_config
def __init__( ... | 709 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is... | 604 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None) -> Any:
if start is None:
a = 0
if end is None:
a = len(__UpperCamelCase) - 1
if start >= end:
... | 515 |
'''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,... | 508 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTes... | 721 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Conf... | 52 | 0 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase__ : str = """#"""
class a :
"""simple docstring"""
def __init__( self : str ):
'''simple docstring'''
snake_case__ : dict = {}
... | 347 |
'''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 (
... | 347 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
'''s... | 643 |
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_roformer import RoFormerTokenizer
f... | 643 | 1 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixi... | 13 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_te... | 13 | 1 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1024 ) -> List[Any]:
_snake_cas... | 404 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
A__ : Tuple = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Union[str, Any] ... | 404 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ :Union[str, Any] = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase__ :Union[str, Any] = {
"""yjernite/retribert-base-uncased""": (
"""https... | 355 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ :str = logging.get_logger(__name__)
UpperCamelCase__ :Optional[int] = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/c... | 355 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE__ = logging.get... | 393 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_co... | 393 | 1 |
def _lowerCamelCase ( a_ : list):
lowerCamelCase :Optional[int] = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowerCamelCase :Union[str, Any] = True
for i in range(0 , len(a_) - 1 , 2): # iterating over all eve... | 166 | import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( a_ : Optional[int] , a_ : Optional[Any] , a_ : List[st... | 166 | 1 |
from __future__ import annotations
from PIL import Image
# Define glider example
_SCREAMING_SNAKE_CASE = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, ... | 703 | from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class a ( __lowerCAmelCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[str]:
return [
... | 83 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 105 |
'''simple docstring'''
import sys
_UpperCAmelCase : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6... | 72 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
... | 719 |
import os
from pathlib import Path
def A ( ):
"""simple docstring"""
from torch.utils.cpp_extension import load
UpperCAmelCase__ :Any = Path(SCREAMING_SNAKE_CASE ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
UpperCAmelCase__ :Tuple = [
ro... | 433 | 0 |
import random
from typing import Any
def __lowercase ( snake_case ):
"""simple docstring"""
for _ in range(len(lowerCAmelCase_ ) ):
__magic_name__ :Union[str, Any] = random.randint(0, len(lowerCAmelCase_ ) - 1 )
__magic_name__ :Optional[int] = r... | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowerCAmelCase__ ( __lowerCamelCase ):
"""simple docstring"""
def _UpperCamelCase ( self ):
return [
... | 250 | 0 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a__ ... | 702 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a__ = {
"""configuration_clip""": [
"""CLIP_PRETRAINED_... | 99 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Dict = ['''image_processor''', '''tokenizer''']
__UpperCamelCase... | 223 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase__ : Optional[Any] = {'UserAgent': UserAgent().random}
def lowercase_ ( _snake_case ):
SCREAMIN... | 223 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class A_ ( unittest.TestCase ):
def _UpperCAmelCase ( self... | 525 | import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
SCREAMING_SNA... | 525 | 1 |
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
from transformers.models.b... | 246 |
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 a_ ( ... | 246 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_... | 601 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_available():
r... | 601 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class UpperCAmelCase ( ... | 310 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__lowerCamelCase : int = 637_8137.0
__lowerCamelCase : Any = 635_6752.31_4245
__lowerCamelCase : List[Any] = 637_8137
def UpperCA... | 310 | 1 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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 ... | 702 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCAmelCase__( unittest.TestCase , lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"... | 642 | 0 |
'''simple docstring'''
import functools
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
# Validation
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or not all(isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) for day in days ):
... | 75 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAna... | 75 | 1 |
import numpy
class _a :
def __init__( self: int , UpperCamelCase_: numpy.ndarray , UpperCamelCase_: numpy.ndarray ) -> None:
"""simple docstring"""
lowercase__ = input_array
# Random initial weight... | 429 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 429 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipe... | 106 | """simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses... | 516 | 0 |
def lowercase ( _a = 2000000 ) -> int:
UpperCAmelCase_: List[str] = [0 for i in range(n + 1 )]
UpperCAmelCase_: str = 1
UpperCAmelCase_: Union[str, Any] = 1
for i in range(2 ,int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in ... | 306 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__ ( snake_case__ ):
snake_case_ = ... | 306 | 1 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import ... | 277 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( lowerCAmelCase__ ):
'''simple docstring'''
a : List[Any] = ["image... | 502 | 0 |
import math
from collections.abc import Callable
def __lowerCamelCase ( __a : Callable[[float], float] , __a : float , __a : float ) -> str:
_lowercase = xa
_lowercase = xa
while True:
if x_n == x_na or function(__a ) == function(__a... | 706 | from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, id... | 594 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_lowercase : Any =logging.get_logger(__name__)
... | 364 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils impo... | 118 | 0 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( A : str , A : str , A : Optional[int] ) -> Tuple:
# Initialise PyTorc... | 715 |
'''simple docstring'''
import numpy as np
def __UpperCAmelCase ( A : np.ndarray , A : np.ndarray , A : float = 1e-12 , A : int = 1_0_0 , ) -> tuple[float, np.ndarray]:
assert np.shape(A )[0] == np.shape(A )[1]
# Ensure proper dimensionality.
assert np... | 216 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
... | 544 | def lowerCamelCase ( UpperCamelCase : int = 60_08_51_47_51_43 ) -> int:
try:
_lowerCamelCase = int(UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n... | 544 | 1 |
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ :
def __init__( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = [
[],
... | 707 |
from __future__ import annotations
import math
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int ) -> float:
__lowercase = u
for i in range(1 , SCREAMING_SNAKE_CASE ):
__lowercase = temp * (u - i)
ret... | 688 | 0 |
from collections import namedtuple
SCREAMING_SNAKE_CASE = namedtuple('from_to', 'from_ to')
SCREAMING_SNAKE_CASE = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.0_01, 1000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_04_54, 2_64.1_72),
'cubicyard... | 485 |
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 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_availab... | 703 |
from random import randint, random
def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: bool = False , lowerCAmelCase: bool = False , lowerCAmelCase: int = 5 , )-> list:
_snake_case : Dict ... | 669 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extractio... | 150 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def __UpperCamelCase ... | 235 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 714 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase_ ( lowercase , lowercase ):
@register_to_config
def __init__( self , *,
lowerCamelCase_ = 4 , lowerC... | 589 | 0 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase_ : Tuple = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained... | 24 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 24 | 1 |
from __future__ import annotations
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
if b == 0:
return (1, 0)
((A__) , (A__)) = extended_euclid(lowerCamelCase_ ,a % b )
A__ = a // b
return (y, x - k * y)
... | 712 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case_ ( _lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_: Union[str, Any] = ["""image_processor""", """feature_extractor"""]
SCREAMING_... | 554 | 0 |
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 .tokenizati... | 154 |
def lowerCAmelCase ( ) ->Dict:
"""simple docstring"""
__magic_name__ : Optional[int] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
__magic_name__ : Optional[Any] = 6
__magic_name__ : Dict = 1
... | 154 | 1 |
import math
def snake_case__ ( UpperCAmelCase : Optional[Any] ):
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 not primes
... | 710 |
import re
def snake_case__ ( UpperCAmelCase : str ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def snake_case__ ( UpperCAmelCase : str ):
lowerCAmelCase__ :List[Any] = split_input(str... | 111 | 0 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = JukeboxTokenizer
__lowerCAmelCase = {
'artist': ... | 523 | 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 import require_torch, require_vis... | 613 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Any = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
class... | 709 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...te... | 85 | 0 |
"""simple docstring"""
import os
import sys
import unittest
a : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, c... | 273 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case: Optional[Any] = logging.get_logger(__name__)
class _UpperCAmelCase ( lowerCAmelCase__ ):
"""simple docstring"""
a_ = "timm_backbo... | 577 | 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_distilbert""": [
"""DISTILBER... | 670 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__SCREAMING_SNAKE_CASE = tuple[int, int]
class a__ :
def __init__( self :Union[str, Any] , _lowerCamelCase :set[int] , _lowerCamelCase :Mapping[EdgeT, int] ... | 357 |
"""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_roformer import RoFo... | 357 | 1 |
"""simple docstring"""
def A_ ( __UpperCamelCase : int ):
assert (
isinstance(__UpperCamelCase , __UpperCamelCase ) and number_of_steps > 0
), f"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
l... | 396 |
"""simple docstring"""
import os
from pathlib import Path
def A_ ( ):
from torch.utils.cpp_extension import load
lowercase = Path(__UpperCamelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
lowercase = [
root / filename
for... | 396 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def A ( snake_case__ ):
'''simple docstring'''
if "model" in orig_key:
SCREAMING_SNAKE_CASE__ = orig_key.replace("""model.""" , """""... | 196 |
"""simple docstring"""
import math
import sys
def _lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
_lowercase: Any = ''''''
try:
with open(_UpperCamelCase , '''rb''' ) as binary_file:
_lowercase: Dict = binary_file.read()
for dat in... | 353 | 0 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_... | 69 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers impo... | 69 | 1 |
def A ( lowercase__ : int , lowercase__ : float , lowercase__ : float ) -> float:
return round(float(moles / volume ) * nfactor )
def A ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> float:
return round(float((moles * 0.0821 * temperature) / (... | 45 |
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_roformer import RoFormerTokenizer
from .tokeniza... | 511 | 0 |
def A__ ( _a : Optional[Any] , _a : Tuple , _a : List[str]=False ):
'''simple docstring'''
if isinstance(_a , _a ) and isinstance(_a , _a ):
snake_case__ : int =len(set_a.intersection(_a ) )
if alternative_union:
snake_case__ : int ... | 448 |
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_... | 448 | 1 |
from __future__ import annotations
def __a ( __UpperCAmelCase ):
if len(__UpperCAmelCase ) == 0:
return []
a__ , a__ = min(__UpperCAmelCase ), max(__UpperCAmelCase )
a__ = int(max_value - min_value ) + 1
a__ = [[] for _ in r... | 194 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : Optional[Any] = {
'kakaobrain/align-ba... | 194 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _lowerCAmelCase ( _a : str , _a : complex , _a : str = "x" , _a : float = 10**-10 , _a : int = 1 , ) -> complex:
lowerCAmelCase_ : Di... | 440 |
from collections.abc import Sequence
def _lowerCAmelCase ( _a : Sequence[float] , _a : bool = False ) -> float:
if not arr:
return 0
lowerCAmelCase_ : List[str] = 0 if allow_empty_subarrays else float("""-inf""" )
lowerCAmelCase_ : ... | 440 | 1 |
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