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 tensorflow as tf
from ...tf_utils import shape_list
class __lowercase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ... | 52 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def a__ ( a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = math.sqrt(a__ )
__SCREAMING_SNAKE_CASE = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img /... | 627 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : str = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'microsoft/unispeech-large-... | 464 |
'''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... | 464 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ... | 75 |
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y)
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
return (x * y) // greatest_common_divisor(a__ , a__)
def _UpperCAmelCase ( a... | 540 | 0 |
"""simple docstring"""
import qiskit
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> qiskit.result.counts.Counts:
SCREAMING_SNAKE_CASE__ = qiskit.Aer.get_backend("aer_simulator" )
SCREAMING_SNAKE_CASE__ = qiskit.Quantu... | 717 | """simple docstring"""
import math
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> float:
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initi... | 538 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
A__ : Any = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 13 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase_ (tf.keras.optimizers.schedules.Learni... | 13 | 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... | 709 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _lowerCAmelCase ( UpperCAmelCase__ : Tuple, UpperCAmelCase__ : Union[str, Any]=None ) ->Tuple... | 498 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:... | 129 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
__magic_name__ = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : int=None ... | 129 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (... | 709 |
'''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 UpperCamelCase__ ( lowerCamelC... | 602 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ : Dict = logging.get_logger(__name__)
lowercase__ : List[str] = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/ma... | 376 | '''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorTyp... | 309 | 0 |
'''simple docstring'''
import os
from distutils.util import strtobool
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
for e in env_keys:
snake_case__ : Optional[int] = int(os.environ.get(_lowerCAmelCase , -1 ) )
if val >= 0... | 301 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> bool:
snake_case__ : Tuple = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def __snake_case( _lowerCAmelCase = 5_000 ) -> int:
snake_case__ : Any = [(i * (3 * i - ... | 301 | 1 |
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> float:
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(__SCREAMING_SNAKE_CASE ) * abs(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
... | 312 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
... | 582 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self )... | 44 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
... | 44 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from trans... | 22 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 174 | 0 |
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_utils import... | 470 |
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase_ = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def __lowerCAmelCase... | 470 | 1 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _SCREAMING_S... | 580 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.tes... | 453 | 0 |
"""simple docstring"""
import os
import sys
import unittest
_lowercase : int = 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_d... | 716 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int = 600_851_475_143 ):
try:
__UpperCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Param... | 397 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A__ ( A__ ):
A__ ... | 405 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=A__ ):
A__ = ['torch', 'transformers', 'onnx']
def __init__( self : Any , *_a : Union[str, Any] , **_a : Optional[Any] ) -> int:
... | 405 | 1 |
'''simple docstring'''
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
SCREAMING_SNAKE_CASE__ : Optional[int] = {1: (1, 1), 2: (2, 1... | 233 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any ... | 233 | 1 |
__UpperCAmelCase : int = 0 # The first color of the flag.
__UpperCAmelCase : List[str] = 1 # The second color of the flag.
__UpperCAmelCase : Union[str, Any] = 2 # The third color of the flag.
__UpperCAmelCase : Optional[Any] = (red, white, blue)
def ... | 471 |
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
import jax.numpy as jnp
@slow
... | 471 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
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 impor... | 716 |
'''simple docstring'''
import math
import sys
def _UpperCamelCase ( lowerCAmelCase__: str ) -> str:
SCREAMING_SNAKE_CASE_ = ''
try:
with open(lowerCAmelCase__ ,'rb' ) as binary_file:
SCREAMING_SNAKE_CAS... | 238 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
retur... | 237 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = ... | 237 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class UpperCamelCase__ :
def __init__( self : Optional[Any] ):
'''simple docstring'''
lowercase_ = {}
def UpperCAmelCase__ ( ... | 713 |
from typing import Dict, Iterable, 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,
resize,
to_channel_dimension_for... | 650 | 0 |
"""simple docstring"""
import re
def snake_case__ ( _lowerCamelCase ) ->bool:
"""simple docstring"""
__lowercase : str = re.compile(
R"^(?:0|94|\+94|0{2}94)" R"7(0|1|2|4|5|6|7|8)" R"(-| |)" R"\d{7}$" )
return bool(re.search(_lowerCamelCase, ... | 575 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class lowerCAmelCase__ ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Any , lowercase__ : Any="" , lowercase... | 575 | 1 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def a_ ( __UpperCAmelCase , __UpperCAmelCase=None ) -> str:
"""simple... | 721 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https... | 347 | 0 |
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = len(snake_case ) + 1
__SCREAMING_SNAKE_CASE : Tuple = len(snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# le... | 74 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
lowerCamelCase = None
def a__ ( ):
UpperCAmelCase_ = ... | 82 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ) -> set[str]:
__lowerCAmelCase , __lowerCAmelCase : Dict = set(__snake_case ), [start]
while stack:
__lowerCAmelCase : Option... | 615 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
__snake_case : Tuple = logging.getLogger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE... | 615 | 1 |
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 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE_ = ""
SCREAMING_SNAKE_CASE_ = ""
SCREAMING_SNAKE_CASE_ = ""
SCREAMING_SNAKE_CASE_ = 1 # (0 is vertical, 1 is horizontal)
def lowerCamelCase__ ( ) -> ... | 517 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def a (_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = args.pruning_method
SCREAMING_SNAKE_CASE_ = args.threshold
SCREAMING... | 706 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __magi... | 89 | 0 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_... | 212 |
'''simple docstring'''
# Copyright 2021 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/LIC... | 212 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transfo... | 707 |
'''simple docstring'''
import random
from typing import Any
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
for _ in range(len(lowerCamelCase_ ) ):
snake_case_ : Union[str, Any] = random.randint(0 , len(lowerCamelCase_ ) - 1 )
... | 267 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosi... | 37 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .schedulin... | 538 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"""configur... | 720 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
__UpperC... | 270 | 0 |
"""simple docstring"""
A = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie... | 52 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
... | 260 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowercase_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( self : Dict , *a : Dict , **a ... | 707 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 100 , ) -> float:
lowercase__ = x_start
lowercase__ ... | 45 | 0 |
def snake_case (UpperCAmelCase__ = 1_0_0_0 ) -> int:
UpperCamelCase_: Dict = 2**power
UpperCamelCase_: List[str] = 0
while n:
UpperCamelCase_ ,UpperCamelCase_: str = r + n % 1_0, n // 1_0
return r
if __name__ == "__main__"... | 57 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: ... | 399 | 0 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def lowercase_ ( _lowercase ) -> Optional[Any]:
'''simple docstring'''
for i in range(0 , _lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ... | 357 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 357 | 1 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __A ( ctypes.Structure ):
'''simple docstring'''
... | 560 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
def __init__( self : ... | 560 | 1 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[Any]:
'''simple docstring'''
A__ = tf.convert_to_tensor(lowerCAmelCase_ )
A__ = 0.5 * (1.0 + tf.math.erf(x / tf.ca... | 703 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCase ( a ):
... | 93 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
a : int = '''scheduler_config.json'''
class a_ ( _UpperCAmelCase ):
a : ... | 555 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 162 |
'''simple docstring'''
import os
def A (__lowerCamelCase :Dict ):
_lowerCAmelCase = len(grid[0] )
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
# Check vertically, hor... | 162 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 1_000 ) -> int:
"""simple docstring"""
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 ... | 430 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( A__ , A__ , A__ , ) -> tuple[str, float]:
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('Y... | 430 | 1 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
lowercase__ : List[Any] = '''
import os
'''
lowercase__ : Union[str, Any] = '''
def foo():
import os
return False
'''
lowercase__ ... | 338 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def _lowerCAmelCase ( __snake_case : str ) -> int:
def decorator(__snake_case : Union[str, Any] ):
__A : int = getattr(__snake_case ... | 338 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
... | 546 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
if digit_amount > 0:
return round(number - int(UpperCamelCase__ ) , UpperCamelCase__ )
return number - int(UpperCamelCase__ )
if __name__ == "__... | 546 | 1 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging i... | 156 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
... | 156 | 1 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageProcessingS... | 679 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : str = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research/efficientf... | 679 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from... | 455 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case_ (lowercase__ ):
"""simple docstring"""
_lowerCamelCase = """ClapFeatureExtractor"""
_lowerCamelCase = ("""RobertaTokenizer""", """RobertaTokeniz... | 455 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForced... | 113 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Union[str, Any] ={
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
"""C... | 113 | 1 |
from __future__ import annotations
from typing import Any
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0 ):
a =row, column
a =[[default_value for ... | 703 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TrajectoryTransformerConfig''',
... | 321 | 0 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class UpperCAmelCase__ ( unittest.TestCase):
def __lowerCamelCase ( self ) -> Optional[Any]:
__UpperCamelCase = 0
__UpperCamelCase = [0]
__UpperCamelCase = [0]
__... | 601 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
a = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: \"D... | 518 | 0 |
'''simple docstring'''
from itertools import count
def __lowercase (_lowercase = 50 ) -> Any:
"""simple docstring"""
__lowerCamelCase : Optional[Any] = [1] * min_block_length
for n in count(_snake_case ):
fill_count_functions.append(1 )
... | 708 |
'''simple docstring'''
def __lowercase (_lowercase ) -> list:
"""simple docstring"""
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
__low... | 483 | 0 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def __a ( lowerCAmelCase__ : np.ndarray ):
a__ , a__ , a__ : Dict = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def ... | 688 |
'''simple docstring'''
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()
... | 688 | 1 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase__ ( _snake_case ):
"""simple docstring"""
def __init__(self ,... | 438 | '''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase_ : List[Any] = len(SCREAMING_SNAKE_CASE_ )
for _ in range(SCREAMING_SNAKE_CASE_ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
... | 438 | 1 |
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowerCamelCase ):
# we need a list not a string, so do something to change the type
snake_case__ = arr.split("," )
def A_ ( self ):
snake_case__ = [int(self.array[0] )] * le... | 276 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
# vision encoder
if "img_encoder.pos_embed" in name:
snake_case__ = name.replace("img_en... | 276 | 1 |
import torch
from torch import nn
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self, snake_case__, snake_case__, snake_case__, snake_case__, snake_case__=1, snake_case__=False ) -> str:
... | 715 |
import math
def __magic_name__ ( lowercase ) -> bool:
"""simple docstring"""
lowercase_ : Optional[Any] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowercase ... | 436 | 0 |
'''simple docstring'''
class UpperCamelCase__:
def __init__( self : str , lowerCAmelCase : str = "" , lowerCAmelCase : bool = False )-> None:
"""simple docstring"""
UpperCAmelCase = {}
# A node will be a leaf if th... | 210 |
'''simple docstring'''
def lowerCamelCase__ ( A : int = 50 ):
'''simple docstring'''
UpperCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile... | 210 | 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 GradientAccu... | 718 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 59 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def snake_case ( snake_case__ :dict) -> tuple:
... | 401 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.ut... | 401 | 1 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Pa... | 59 |
# 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 app... | 59 | 1 |
class __snake_case :
'''simple docstring'''
def __init__( self , A_ , A_ , A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = name
SCREAMING_SNAKE_CASE__ = value
SCREAMING_SNAKE_CASE__ = wei... | 100 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ (__lowerCamelCase = "AAPL" ):
_SCREAMING_SNAKE_CASE : Dict = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_SCREAMING_SNAKE_CASE : str = BeautifulSoup(reque... | 249 | 0 |
from math import isclose, sqrt
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = point_y / 4 / point_x
SCREAMING_SNAKE_CASE__ : Tuple = 2 * normal_gradient / ... | 636 | class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ... | 636 | 1 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transforme... | 32 |
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 ...... | 307 | 0 |
'''simple docstring'''
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = args.log_outp... | 708 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 applicab... | 46 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixi... | 6 |
'''simple docstring'''
from typing import Any
class _lowercase :
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Any ) -> Any:
__snake_case = data
__snake_case = None
class _lowercase :
de... | 56 | 0 |
from collections import defaultdict
from math import ceil, sqrt
def a_ (_lowerCAmelCase : int = 1000000 , _lowerCAmelCase : int = 10 )-> int:
snake_case: defaultdict = defaultdict(_lowerCAmelCase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
... | 164 | # 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 appl... | 164 | 1 |
from __future__ import annotations
from cmath import sqrt
def lowerCamelCase__ ( _a , _a , _a):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero.")
SCREAMING_SNAKE_CASE : Tuple = b * b - 4 * a * c
SCREAMING_SNAKE_CASE : Dict = (-b + sqrt(_a)... | 25 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 25 | 1 |
A_ :Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ :str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ :List[Any] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday'... | 154 |
from __future__ import annotations
A_ :Union[str, Any] = '''#'''
class __A :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
__UpperCamelCase : dict ={}
def __lowercas... | 154 | 1 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class _UpperCAmelCase :
"""simple docstring"""
def lowercase ( self : Dict , lowerCAmelCase_ : Tuple ) -> str:
... | 53 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase : Dict = """docs/source/en/_toctree.yml"""
def lowercase (_A ):
"""simple docstring"""
_lowerCAme... | 444 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowercase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 357 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 357 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( a , a ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
... | 356 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import r... | 356 | 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
_A = logging.get_logger(__name__)
_A = {
"google/vit-ba... | 716 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowercase (_snake_case ,_snake_case ,_snake_case=1024 ,_snake_case=1024 ,_snake_case=False ,**_snake_case ) ->... | 228 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_o... | 47 |
from __future__ import annotations
class UpperCamelCase:
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ) -> str:
'''simple docstring'''
__snake_case , __s... | 371 | 0 |
import os
import sys
UpperCamelCase__ : Optional[Any] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Aut... | 486 |
from __future__ import annotations
def A_( A ):
if not nums:
raise ValueError("""List is empty""" )
return sum(A ) / len(A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 486 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class snake_case_ ( lowerCAmelCase ):
__lowerCam... | 345 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> ... | 345 | 1 |
from __future__ import annotations
import math
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
__snake_case : Optional[int] = u
for i in range(1 , __SCREAMING_SNAKE_CAS... | 720 | from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 390 | 0 |
'''simple docstring'''
def A_ ( _lowerCamelCase : float , _lowerCamelCase : float ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modul... | 309 |
'''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():
f... | 94 | 0 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
... | 311 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowerCAmelCase__: List[Any] = logging.get_logger(__name__)
class snake_case_ :
__lowerCamelCase : Any = None
@experimental
def ... | 311 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A__ : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A__ : list[int] = [ord(letter) for letter in string.as... | 13 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class lowerCamelCase_ ( ... | 75 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a__ : Union[str, Any] =logging.get_logger(__name__)
a__ ... | 716 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def lowercase__ ( __lowercase : int , __lowercase : int = 2 , __lowercase : int = 1 , __lowercase : int = 3 , ) -> int | None:
"""simple... | 434 | 0 |
"""simple docstring"""
from timeit import timeit
UpperCAmelCase_ : Any = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a ma... | 255 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_blip''': [
'''... | 255 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : List[Any] = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/re... | 343 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
if len(snake_case__ ) != 2 or len(a[0] ) != 2 or len(snake_case__ ) != 2 or len(b[0] ) != 2:
... | 343 | 1 |
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.set_verbosity_info()
SCREAMING_S... | 85 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : str = False
class __SCREAMING_SN... | 328 | 0 |
'''simple docstring'''
def _snake_case ( A = 1 , A = 1000 ) -> int:
lowerCAmelCase__ = 1
lowerCAmelCase__ = 0
for divide_by_number in range(A , digit + 1 ):
lowerCAmelCase__ = []
lowerCAmelCase_... | 98 |
'''simple docstring'''
def _snake_case ( A ) -> bool:
if not isinstance(A , A ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(A ) == 0:
raise ValueError('''Input list must be a non empty list''... | 98 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = ... | 298 | import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
A_: Union[str, Any] = 5_0000
A_: str = 5000
A_ , A_: int = os.path.split(__file__)
A_: str = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py',... | 398 | 0 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowerCamelCase ( ) -> Tuple:
raise RuntimeError('CUDA out of memory.' )
class lowerCAmelCase__ ( ... | 721 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
'p... | 234 | 0 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase = version.parse(version.pars... | 61 |
'''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_... | 126 | 0 |
'''simple docstring'''
from collections import deque
class __snake_case :
def __init__( self, A, A, A ):
"""simple docstring"""
lowerCamelCase : str = process_name # process name
lowerCamelCase : Tuple ... | 719 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = ... | 449 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : List[str] = logging.get_logger(__name__)
a__ : str = {
'''xlm-... | 682 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
a__ : int = '''us-east-1''' # defaults region
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
snake_case__ : str
snake_case__ : Optional[Any] = "arn:a... | 682 | 1 |
from math import sqrt
def __UpperCamelCase ( snake_case ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all mu... | 712 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")
def __UpperCamelCase ( snake_case ) -> Union[str, Any]:
'''simple docstring'''
__A = test_fi... | 341 | 0 |
def __UpperCamelCase ( A , A , A ):
UpperCamelCase__ = len(A )
UpperCamelCase__ = [[0] * n for i in range(A )]
for i in range(A ):
UpperCamelCase__ = y_points[i]
for i in range(2 , A ):
for j in range(A... | 415 | def __UpperCamelCase ( A ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
UpperCamelCase__ = gray_code_sequence_string(A )
#
... | 415 | 1 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCamelCase : Dict = """<<<<<<< This should probably be modified because it mentions: """
lowerCamelCase... | 706 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
'facebook/xmod-ba... | 303 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=UpperCamelCase_ ):
'''simple docstring'''
lowerCAmelCase__ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Tuple , *UpperC... | 390 | import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ : Optional[int] = logging.get_logger(__name__)
def A__ ( snake_case_ : List[Any] ):
SCREAMING_SNAKE_... | 64 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase__ ( UpperCAmelCase_ ... | 91 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
UpperCamelCase : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __snake_case ... | 91 | 1 |
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 GPTaTokenizer
if TYPE_CHECKING... | 157 |
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
__a : Dict = ... | 397 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''google/bit... | 401 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeIma... | 401 | 1 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_snake_case : Tuple = logging.getLogger(__name__)
@dataclass
class a (_UpperCAmelCase ):
"""simple... | 81 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def _UpperCamelCase ( _A , _A=False ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = OmegaConf.load(_A )
if disp... | 555 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
snake_case__ : Tuple = list[tuple[int, int]]
snake_case__ : Optional[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ : Any = logging.get_logger(__name__)
class _A ( _lowercase , _lowercase ):
'''simple d... | 655 | 0 |
'''simple docstring'''
import argparse
import os
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_task_guides.py
__A : Tuple = 'src/transformers'
__A : ... | 334 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
... | 325 | 0 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE = "... | 556 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE = TypeVar("""T""")
class __a ( Generic[T] ):
def __init__( self : List[Any] , UpperCAmelCase_ : T )-> None:
... | 556 | 1 |
def __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
# Check if the input is valid
if not len(a__ ) == len(a__ ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & ... | 226 |
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 PIL import Image
from ..image_utils import... | 219 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
A , A = len(__a ), len(grid[0] )
if (
min(__a , __a ) < 0
or row == row_length
or... | 720 | """simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerC... | 674 | 0 |
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