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"""
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
if "model" in orig_key:
__snake_case = orig_key.replace("model... | 163 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _snake_case ( _A ):
_A = 'Speech2TextFeatureExtractor'
_A = 'Speech2TextTokenizer'
def __init__( self ,UpperCamelCase ,UpperCame... | 241 | 0 |
from __future__ import annotations
__A =[
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a ( _UpperCAmelCase : list[list[int]] , _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] , _Upper... | 241 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges fi... | 241 | 1 |
import string
from math import logaa
def A__ ( snake_case_ : str , snake_case_ : str ):
SCREAMING_SNAKE_CASE__: Optional[int]= document.translate(
str.maketrans('''''' , '''''' , string.punctuation ) ).replace('''\n''' , '''''' )
SCREAMI... | 64 | from math import factorial
def A__ ( snake_case_ : int , snake_case_ : int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError('''Please enter positiv... | 64 | 1 |
"""simple docstring"""
def A__ ( _UpperCAmelCase : int = 50 ) -> int:
'''simple docstring'''
snake_case__ : Tuple = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_leng... | 150 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def A__ ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
if not is_accelerate_available():
return method
snake_ca... | 150 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_lowercase = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowercase = [ord(letter) for letter in string.ascii_lowercase]
_lowercase = {ord(char) f... | 632 |
def lowerCAmelCase__ ( UpperCamelCase_ : int )-> int:
A__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCAmelCase__ ( UpperCamelCase_ : int )-> int:
A__ = 0
while number > 0:
... | 632 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCas... | 718 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowerc... | 633 | 0 |
import datasets
from .evaluate import evaluate
UpperCamelCase__ : Union[str, Any] = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv prep... | 105 |
'''simple docstring'''
def a_ ( UpperCamelCase_ ):
if length <= 0 or not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(UpperCamelCase_ )]
if __name__ == "__main__":
pr... | 452 | 0 |
'''simple docstring'''
from __future__ import annotations
_a : Tuple = 1.60_21e-19 # units = C
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ) -> tuple[str, float]:
"""simple docstring"""
if (c... | 721 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | 0 |
'''simple docstring'''
class a__:
def __init__( self ) -> Union[str, Any]:
snake_case__ =''
snake_case__ =''
snake_case__ =[]
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase ) -> int:
... | 538 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 538 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 126 |
'''simple docstring'''
from __future__ import annotations
__A : Optional[int] = list[list[int]]
# assigning initial values to the grid
__A : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8,... | 126 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __lowerCamelCase :
a__: List[str]
a__: Optional[str] ... | 29 | """simple docstring"""
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLO... | 434 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers... | 63 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/microsoft/... | 63 | 1 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def _lowerCAmelCase ( __magic_name__ : jnp.ndarray , __magic_name__ : int , __magic_name__ : float = 1 , __magic_name__ : float = 1 , __magic_name__ : float = 1.0... | 92 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowercase_ = logging.get_logger(__name__)... | 291 | 0 |
def lowerCamelCase__ ( a : list ) -> list:
"""simple docstring"""
if len(a ) <= 1:
return lst
a__ :int = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a__ , a__ :List[Any] = lst[i], lst[i - 1]
i ... | 373 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
snake_case__ = False
class lowerCAmelCase_ ( unittest.TestCase):
pass... | 373 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class _snake_case :
def __init__( self , a__ , a__ ) -> None:
'''simple docstring'''
if len(a__ ) != degree + 1:
... | 400 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import... | 400 | 1 |
"""simple docstring"""
import argparse
import datetime
def a__ ( snake_case__ ) -> str:
lowerCamelCase = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
... | 705 |
"""simple docstring"""
def a__ ( snake_case__ = 1_00_00_00 ) -> int:
lowerCamelCase = 1
lowerCamelCase = 1
lowerCamelCase = {1: 1}
for inputa in range(2 , snake_case__ ):
lowerCamelCase = 0
lowerCamelCase ... | 533 | 0 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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... | 84 |
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 ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstr... | 362 | 0 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCAmelCase_ ( _lowerCamelCase: float , _lowerCamelCase: float , _lowerCamelCase: bool = False ):
if radian_mode:
... | 178 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
UpperCamelCase__ : Optional[int] = {
'''faceb... | 178 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-large''': '''h... | 164 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environm... | 164 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase_ ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
snake_case__ : int =int(number**0.5 )
return number == sq * sq
de... | 701 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import lo... | 408 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
Au... | 446 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow... | 446 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
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, p... | 717 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__lowerCAmelCase = '''http:... | 396 | 0 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class _lowerCamelCase ( snak... | 365 |
'''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
#
# Un... | 365 | 1 |
_SCREAMING_SNAKE_CASE = 9.8_0665
def __a(SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float = g ):
'''simple docstring'''
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volu... | 714 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch,... | 489 | 0 |
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(100, 0.25) = }''')
print(f'''{price_plus_tax(1_25.50, 0.05) = }''')
| 66 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"PoolFormerOnnxConfig"... | 66 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@req... | 706 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__magic_name__ : Union[str, Any] = collecti... | 368 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( __A : Tuple , __A : Union[str, Any] , __A ... | 94 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_avai... | 710 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 506 | 0 |
from itertools import permutations
def lowercase__ ( A_: tuple ) -> bool:
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 68 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = int(number**0.5 )
return number == sq * sq
def a__ ( lowerCAmelCase__ , lowerCAmel... | 82 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
t... | 704 | A = 'Alexander Joslin'
import operator as op
from .stack import Stack
def lowerCamelCase ( UpperCamelCase : str ) -> int:
_lowerCamelCase = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
_lowerCamelCase = Stack()
_lowerC... | 234 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import Tokenizer... | 19 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : List[Any] = {'''conf... | 549 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class lowercase ( lowercase_ ):
def __init__( self , *snake_cas... | 711 |
_UpperCAmelCase : str = [0, 2, 4, 6, 8]
_UpperCAmelCase : Any = [1, 3, 5, 7, 9]
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if remaining_len... | 108 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from... | 50 |
'''simple docstring'''
from PIL import Image
def A__ ( __lowerCAmelCase : Image , __lowerCAmelCase : float ):
def brightness(__lowerCAmelCase : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
rai... | 50 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ = logging.get_logger(__name__)
A_ ... | 712 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , _lowerCAmelCase , _lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = text, pattern
lowerCamelCase__ , lowerCamelCase__ = len(_l... | 360 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class _a ( UpperCame... | 43 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteSch... | 43 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from trans... | 179 | '''simple docstring'''
from __future__ import annotations
def __lowerCAmelCase ( a_ , a_ = None ) -> list[list[str]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = word_bank or []
# create a table
... | 179 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCAmelCase : List[str] = logging.get_logger(_... | 295 |
'''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 OptionalDepe... | 51 | 0 |
from __future__ import annotations
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = []
while i * i <= n:
if n % i:
i += 1
else:
... | 620 |
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : int = 200 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [1, 2, 5, 10, 20, 50, 100, 200]
SCREAMING_SNAKE_CASE_ = [0] * (pence + 1)
SCREAMING_SNAKE_CASE_ = 1 # base case: 1 way to make... | 620 | 1 |
'''simple docstring'''
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 ... | 536 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...on... | 449 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : List[Any] = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
'processi... | 721 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acce... | 662 | 0 |
from sklearn.metrics import fa_score
import datasets
lowercase__ : Optional[Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
lowercase__ : Union[str, Any] = "\nA... | 515 |
lowercase__ : Optional[int] = 9.8_0665
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = g) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density")
if volume < 0:
raise ValueError("Imposs... | 515 | 1 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :List[str] ):
'''simple docstring'''
snake_case_ : Tuple = len(__lowerCAmelCase )
snake_case_ : str = len(matrix[0] )
snake_case_ : Any = mi... | 718 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __UpperCamelCase ( lowercase__ ):
@staticmethod
@abstractmethod
def a__ ( _UpperCamelCase :ArgumentParser ):
raise NotImplementedError()
@... | 267 | 0 |
"""simple docstring"""
from __future__ import annotations
_lowercase : Union[str, Any] = 1.6_0_2_1e-1_9 # units = C
def lowercase__ ( snake_case_ :List[str] , snake_case_ :int , snake_case_ :List[str] , ):
if (conductivity, electron_conc, m... | 49 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONF... | 407 | 0 |
'''simple docstring'''
from math import sqrt
def _lowerCAmelCase ( lowerCamelCase_ : int ):
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
__lowercase = True
... | 56 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers imp... | 56 | 1 |
from __future__ import annotations
import math
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
if depth < 0:
raise ValueError("""Depth can... | 336 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : str = {
"""configuration_blenderbot_small""": [
"""BLENDERBOT_SMALL_... | 336 | 1 |
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... | 717 | from math import loga
def a__ ( a ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(a , a ):
raise TypeError('''Input value must be a \'int\' type''' )
return 0 if (a == 0) els... | 236 | 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""t5-small""": """https:... | 71 |
from collections.abc import Generator
from math import sin
def _A ( _lowercase ) -> bytes:
"""simple docstring"""
if len(_lowercase ) != 32:
raise ValueError('Input must be of length 32' )
__UpperCamelCase = B''
for i in [3, 2, 1, 0]:
... | 1 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A_ ( ):
"""simple docstring"""
_a = ArgumentParser(
description=(
'''Py... | 709 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 10_00 ):
"""simple docstring"""
_a , _a = 1, 1
_a = 2
while True:
_a = 0
_a = fa + fa
_a , _a = fa, f
index += 1
... | 285 | 0 |
import torch
from transformers import AutoModel
class UpperCAmelCase ( torch.nn.Module ):
def __init__( self: Optional[int] , __UpperCamelCase: Tuple="sayef/fsner-bert-base-uncased" ):
super(__UpperCamelCase , self ).__init__()
_a ... | 487 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, i... | 487 | 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 switc... | 715 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class __UpperCAmelCase (_UpperCAmelCase ):
# `task` is not a ClassVar si... | 569 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = ... | 26 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_i... | 580 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class a__ :
def __init__( self, _UpperCAmelCase=None, _UpperCAmelCase=None ):
'''simple docstring'''
lowercase__ = list(poly_a or [0] )[:]
lowercase__ = list(poly_b... | 668 | """simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from... | 668 | 1 |
def UpperCamelCase ( snake_case__ : Optional[Any] ):
'''simple docstring'''
def merge(snake_case__ : Optional[int] ,snake_case__ : Optional[Any] ) -> list:
def _merge():
while left and right:
yield (left if le... | 455 |
def __magic_name__ ( lowercase = 1000 ) -> int:
"""simple docstring"""
lowercase_ , lowercase_ : Optional[Any] = 1, 1
lowercase_ : Tuple = 2
while True:
lowercase_ : Dict = 0
lowercase_ : ... | 458 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase_ ( lowerCAmelCase__ ):
__UpperCamelCase = "Speech2TextFeatureExtractor"
__UpperCamelCase = "Speech2TextTokenizer"
def __init__( self: Any, _l... | 334 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_... | 334 | 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 ..pipel... | 437 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a_ = logging.get_logger(__name__)
class A_(SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self , *A , **A ... | 437 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 716 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...... | 364 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_A ... | 100 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_UpperCAmelCase = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8... | 297 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _lowerCamelCase ( _a ):
"""simple docstring"""
_lowerCamelCase = FileLock(str(tmpdir / '''foo.lock''' ) )
_lowerCamelCase = FileLock(str(tmpdir / '''foo.lock''' ) ... | 297 | 1 |
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __lowercase ( __lowercase ):
"""simple do... | 457 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoic... | 156 | 0 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase = logging.getLogger(__nam... | 103 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase = logging.get_logg... | 103 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : int = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFI... | 533 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(SCREAMING_SNAKE_CASE__ ):
for j in range(SCREAMING_SNAKE_CASE__ ):
... | 533 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as... | 701 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_uti... | 586 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_: Any = logging.get_logger(__name__)
A_: Optional[int] = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class _lowercase ( _UpperCAmelCase ):
... | 398 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_: Tuple = logging.get_logger(__name__)
A_: str = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
... | 398 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase = 2_0_0_0_0_0_0):
__lowerCAmelCase = [0 for i in range(n + 1)]
__lowerCAmelCase = 1
__lowerCAmelCase = 1
for i in range(2, int(n**0.5) + 1):
if pri... | 711 |
'''simple docstring'''
def __magic_name__( ):
return [
a * b * (1_0_0_0 - a - b)
for a in range(1, 9_9_9)
for b in range(lowerCamelCase, 9_9_9)
if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f... | 474 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 394 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__A : Optional[Any] = logging.getLogger(__name__)
def lowerCAmelCase_ ( ):
a__ = argparse.ArgumentParser(
... | 394 | 1 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, c... | 702 | '''simple docstring'''
from __future__ import annotations
lowercase_ = 10
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =1
_lowerCAmelCase =max(a__ )
while placement <= max_digit:
# declare and initializ... | 58 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 241 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 43 | 0 |
from itertools import product
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = sides_number
UpperCAmelCase_ : List[str] = max_face_number * dice_number
UpperCAmelCase_ : O... | 703 |
from math import ceil
def lowerCamelCase__ ( _lowercase = 1001 ):
'''simple docstring'''
UpperCAmelCase_ : Dict = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
UpperCAmelCase_ : Tuple = 2 * i + 1
UpperCAmelCase_ : ... | 300 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__: Tuple = logging.get_logger(__name__)
lowerCAmelCase__: Union[str, Any] = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/m... | 345 |
import warnings
from functools import wraps
from typing import Callable
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> Callable:
@wraps(SCREAMING_SNAKE_CASE )
def _inner_fn(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
warnings.warn(
(f'\'{fn.__name__}\' is experiment... | 345 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test... | 592 |
from __future__ import annotations
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase ) ->bool:
_UpperCAmelCase =get_failure_array(_lowerCamelCase )
# 2) Step through text searching for pattern
_UpperCAmelCase , _UpperCAmelCase =0, 0 # index into text, pattern
... | 592 | 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, slow, t... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : str = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-b... | 563 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'vocab_file': 'vocab.jso... | 112 |
"""simple docstring"""
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> ... | 112 | 1 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 |
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
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 1 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = '''T5Config'''
class _snake_case ( __lowe... | 720 | """simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugg... | 635 | 0 |
'''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, prepar... | 430 |
from manim import *
class lowercase_ (lowercase__ ):
def __UpperCamelCase ( self) -> List[Any]:
a__ =Rectangle(height=0.5 , width=0.5)
a__ =Rectangle(height=0.46 , width=0.46).set_stroke(width=0)
a__ =[mem.copy() for... | 20 | 0 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_... | 700 |
import operator
def lowerCAmelCase_ ( snake_case_,snake_case_ = False,snake_case_ = None ):
_A : str = operator.lt if reverse else operator.gt
_A : Optional[Any] = solution or []
if not arr:
return solution
_A : Dict = [arr.po... | 54 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__A = version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def __A (_SCREAM... | 93 |
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,
)
snake_case_ : Tuple = {
"configuration_albert": ["ALBERT_PRE... | 488 | 0 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
snake_case__ : int ... | 700 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_conf... | 618 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 657 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINE... | 657 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int ,lowerCAmelCase_ : int ,lowerCAmelCase_ : bool ,lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : float ) -> int:
"""simple docstring"""
... | 153 |
__SCREAMING_SNAKE_CASE = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__SCREAMING_SNAKE_CASE = [{'t... | 153 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCamelCase : int = pd.read_csv('''sample_data.csv''', header=None)
lowerCamelCa... | 367 |
from math import pow, sqrt
def __lowerCAmelCase ( *__snake_case ):
__lowerCAmelCase = len(__snake_case ) > 0 and all(value > 0.0 for value in values )
return result
def __lowerCAmelCase ( __snake_case , __snake_case ):
ret... | 367 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class a ( unit... | 555 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTok... | 555 | 1 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock imp... | 567 |
'''simple docstring'''
from torch import nn
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU... | 474 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ : List[str] = logging.get_logger(__name__)
UpperCAmelCase__ : Optional[int] ... | 700 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 676 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import... | 180 |
"""simple docstring"""
import numpy as np
def __UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ):
assert np.shape(snake_case__ )[0] == np.shape(snake_case__ )[1]
# Ensure proper dimensionality.
assert np.shape(snake_case__ )[0] ==... | 180 | 1 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under... | 500 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,... | 500 | 1 |
'''simple docstring'''
_lowerCAmelCase = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_lowerCAmelCase = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_lowerCAmelCase = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Sat... | 161 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig... | 161 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def lowercase_ ( lowercase__ ) ->Optional[Any]:
return np.maximum(0 , lowercase__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 273 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
A : Dict = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. ... | 273 | 1 |
'''simple docstring'''
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 UpperCAmelCase_ ( __A , __A ):
"""simple docstring"""
... | 94 | '''simple docstring'''
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_a : Tuple = "src/transformers"
... | 168 | 0 |
import math
import sys
import cva
import numpy as np
def __snake_case ( _UpperCamelCase , _UpperCamelCase ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
_a = math.sqrt(_UpperCamelCase )
_a = 1 / (sigma * math.sqrt(2 * ... | 346 |
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_UpperCamelCase ) )
def __snake_case ( _UpperCamelCase , _UpperCamelCase ... | 346 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'''
),
}
class ... | 221 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def a_ ( __lowercase : Any ) -> List[An... | 686 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import... | 706 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : int = {
'''configuration_roberta'''... | 4 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MOD... | 46 | 0 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase ( __... | 719 |
'''simple docstring'''
import os
import sys
import unittest
A_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
... | 384 | 0 |
def __lowerCAmelCase ( A = 600851475143 ):
try:
UpperCAmelCase_ = int(A )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
UpperCAmelCase_... | 162 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a: int = logging.get_logger(__name__)
_a: Optional[Any] = {
"""SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve... | 162 | 1 |
"""simple docstring"""
from __future__ import annotations
snake_case = '''Muhammad Umer Farooq'''
snake_case = '''MIT'''
snake_case = '''1.0.0'''
snake_case = '''Muhammad Umer Farooq'''
snake_case = '''contact@muhamma... | 404 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, 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... | 404 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : str = {
'shi-labs... | 519 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__UpperCamelCase : str = logging.getLogger(__name__)
class _UpperCamelCase ( A ):
'''simple docstring'''
... | 519 | 1 |
import math
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = 2
__lowercase = int(math.sqrt(_lowerCamelCase ) ) # Size of every segment
__lowercase = [True] * (end + 1)
__lowercase = []
while sta... | 700 |
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 : Union[str, Any] = logging.get_logger(__name__)
a : Union[str, Any] = {'''vocab_file''': ''... | 527 | 0 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_pr... | 435 | '''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowerCAmelCase_ : List[str] = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a compa... | 435 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( UpperCamelCase_ ,... | 721 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Any = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass... | 303 | 0 |
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