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"""
# 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 ... | 690 |
"""simple docstring"""
import os
import sys
UpperCamelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
... | 690 | 1 |
def a_ (_lowerCAmelCase : list[int] )-> float:
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
snake_case: Tuple = sum(_lowerCAmelCase ) / len(_lowerCAmelCase ) # Calculate the average
return sum(abs(... | 164 | from __future__ import annotations
def a_ (_lowerCAmelCase : int )-> list[int]:
snake_case: List[str] = 2
snake_case: Dict = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_lowerCA... | 164 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Any = {
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioCo... | 444 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def lowercase (_A , _A = 2 , _A = 1 , _A = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError('The input value cann... | 444 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", "... | 487 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class a__ ( __magic_name__ ):
lowercase_ = ["image_processor", "feature_extractor"]
lowercase_ = "TvltImageProcessor"
lowercase_ = "TvltFeatureExtractor"
def __init__( self : Tuple , Up... | 487 | 1 |
import os
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> List[str]:
SCREAMING_SNAKE_CASE_ = len(grid[0] )
SCREAMING_SNAKE_CASE_ = len(__UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
SCRE... | 31 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : int ):
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ ... | 31 | 1 |
'''simple docstring'''
from math import isclose, sqrt
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> tuple[float, float, float]:
lowerCamelCase__ : str = point_y / 4 / point_x
lowerCamelCase_... | 631 |
'''simple docstring'''
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
lowerCamelCase__ : Optional[int] = 0
# ... | 631 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( snake_case__ , snake_case__ = None , snake_case__ = None ) -> None:
if start is None:
_UpperCamelCase :Tuple = 0
if end is None:
_UpperCamelCase :List[str] = ... | 355 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp imp... | 355 | 1 |
from math import sqrt
def _UpperCamelCase ( lowerCAmelCase_ = 1_0_0_0_0_0_0 ) ->int:
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , ... | 627 |
from __future__ import annotations
def _UpperCamelCase ( lowerCAmelCase_ ) ->None:
create_state_space_tree(lowerCAmelCase_ , [] , 0 , [0 for i in range(len(lowerCAmelCase_ ) )] )
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAm... | 627 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_SCREAMING_SNAKE_CASE = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst... | 18 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
snake_case = logging.get_logger(__name__)
snake_case = {
"Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
# See all DPT models at http... | 424 | 0 |
"""simple docstring"""
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
A__ : Any = logging.get_log... | 272 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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... | 272 | 1 |
import re
def a_ ( lowerCAmelCase_ : str ):
return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]', str_ )]
def a_ ( lowerCAmelCase_ : str ):
__lowerCAmelCase = split_input(str_ )
return "".join(
[''.join([char.c... | 53 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, ... | 315 | 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_sta... | 703 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> float:
if days_between_payments <= 0:
raise ValueError('days_between_payments m... | 137 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 199 |
'''simple docstring'''
from math import factorial
SCREAMING_SNAKE_CASE = {str(digit): factorial(digit) for digit in range(1_0)}
def snake_case_ ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise TypeError("Parameter number must be int" )
if number <... | 199 | 1 |
"""simple docstring"""
def snake_case__ ( _snake_case : str , _snake_case : str ):
"""simple docstring"""
def get_matched_characters(_snake_case : str , _snake_case : str ) -> str:
UpperCamelCase__ = []
... | 712 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffuser... | 304 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
UpperCAmelCase__ : List[str] ... | 410 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...test_... | 463 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __lowercase ):
_snake_case =(IPNDMScheduler,)
_snake_case =(('''num_inference_steps''', 50),)
def lowerCA... | 550 |
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_ut... | 550 | 1 |
import os
from distutils.util import strtobool
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
for e in env_keys:
_lowercase : List[Any] = int(os.environ.get(SCREAMING_SNAKE_CASE , -1 ) )
if... | 66 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : str = ["image_processor", "tokenizer"]
_UpperCamelCase : Union[str, Any] = "AutoImageProcesso... | 66 | 1 |
"""simple docstring"""
from statistics import mean, stdev
def lowercase_ ( _snake_case ,_snake_case = 3 ):
SCREAMING_SNAKE_CASE__ : Any = min(_snake_case )
SCREAMING_SNAKE_CASE__ : int = max(_snake_case )
# normalize data
return [round((x -... | 545 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggin... | 545 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.... | 663 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeli... | 459 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCAmelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
@register_to_config
def __init__( self , *,
_UpperCAmelCase = 4 ,... | 715 |
import copy
import re
class lowerCAmelCase_ :
'''simple docstring'''
__snake_case = "hp"
__snake_case = {}
__snake_case = None
@classmethod
def UpperCamelCase__ ( cls , _UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = prefix
snake_case_ = d... | 531 | 0 |
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> tuple[float, float]:
# Check if the input is valid
if not len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE ) == 3:
raise ValueError('Please enter a valid equation.' )
i... | 66 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowercase ( lowerCAmelCase__ ):
def wrapper(*lowerCAmelCase__ ,**lowerCAmelCase__ ):
lowerCamelCase_ = timeit... | 29 | 0 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__A : Dict = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.10... | 95 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[... | 95 | 1 |
from __future__ import annotations
import bisect
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 0 , __UpperCamelCase = -1 ):
if hi < 0:
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase )
while lo < hi:
SCREAMING_SNAKE_CASE_ = lo + (hi -... | 140 | import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = job["started_at"]
SCREAMING_SNAKE_CASE_ = job["completed_at"]
SCREAMING_SNAKE_CASE_ = date... | 140 | 1 |
from __future__ import annotations
from math import pi
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0... | 718 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : str ={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNe... | 269 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Any = logging.get_logger(__name__)
__a : Any = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/tiiuae... | 637 |
def _SCREAMING_SNAKE_CASE ( __lowercase : List[Any] ) -> Any:
"""simple docstring"""
stooge(__lowercase , 0 , len(__lowercase ) - 1 )
return arr
def _SCREAMING_SNAKE_CASE ( __lowercase : str , __lowercase : Dict... | 637 | 1 |
from __future__ import annotations
from random import choice
def lowerCAmelCase (__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
return choice(__UpperCamelCase )
def lowerCAmelCase (__UpperCamelCase : list[int] , __UpperCamelCase ... | 716 | """simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 296 | 0 |
def lowercase ( __A : str , __A : str ) -> int:
'''simple docstring'''
if len(__A ) != len(__A ):
raise ValueError("""String lengths must match!""" )
snake_case : List[Any] = 0
for chara, chara in zip(__A , __A ):
if chara != ... | 36 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_) -> bool:
return len(set(lowerCamelCase_)) == len(lowerCamelCase_)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 596 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
... | 707 | '''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
... | 10 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCas... | 60 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_ava... | 512 | 0 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInp... | 715 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> list:
snake_case__ : List[str] = int(_lowerCAmelCase )
if n_element < 1:
snake_case__ : List[Any] = ValueError("""a should be a positive number""" )
raise my_error
... | 301 | 0 |
def lowerCamelCase_ ( _lowercase ) -> int: # noqa: E741
__A : Dict = len(_lowercase )
__A : Tuple = 0
__A : int = [0] * n
__A : int = [False] * n
__A : Union[str, Any] = [False] * n
def dfs(_... | 520 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
UpperCamelCase = get_te... | 520 | 1 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase ( __lowerCamelCase ):
a__ :Any = (IPNDMScheduler,)
a__ :Union[str, Any] = (('''num_inference_steps''', 50),)
... | 706 | class UpperCamelCase :
def __init__(self , __UpperCamelCase ) -> None:
UpperCamelCase_ : Tuple = len(__UpperCamelCase )
UpperCamelCase_ : List[str] = [0] * len_array
if len_array > 0:
UpperCamelCase_... | 138 | 0 |
'''simple docstring'''
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 He... | 640 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 13 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
lowercase_ : Any = tuple[int, int]
class UpperCamelCase :
def __init__( self , snake_case__ , snake_case__ ):
"""simple docstring"""
_... | 572 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
lowercase_ : Tuple = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classif... | 572 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 27 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase ( lowerCamelCase : int):
if num <= 0:
A_ : List[Any] = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCamelCase)
... | 27 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https:/... | 611 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
# See al... | 611 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a__ ( a__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def __SCREAMING_SNAKE_CASE ( lowerCamelC... | 98 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transforme... | 98 | 1 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from tr... | 94 | '''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
__a: Tuple = collections.namedtuple("""_Datasets""", [... | 152 | 0 |
'''simple docstring'''
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config... | 704 |
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,
TrainerCallback,
TrainingArgum... | 421 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ : List[An... | 24 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingS... | 24 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def A_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , ) ->np.ndarray:
lowercase_ = np.shape(SCREAMING_SNAKE_CASE_ )
lowercase_ = np.shape(SCREAMING_SNAKE_CASE_... | 603 | '''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,
resize,
to_channel_d... | 603 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowerCamelCase__ = 50_0000
lowerCamelCase__ , lowerCamelCase__ = os.path.split(__file__)
lowerCamelCase__ = os.path.join(RESULTS_BASEP... | 624 |
"""simple docstring"""
import numpy as np
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ = 1e-12 ,lowercase_ = 100 ,) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[1]
# Ensure pr... | 624 | 1 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowerCAmelCase = False
try:
_lowerCAmel... | 160 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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 ..pipeline_p... | 160 | 1 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import C... | 648 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 648 | 1 |
"""simple docstring"""
import enum
import shutil
import sys
UpperCAmelCase , UpperCAmelCase = shutil.get_terminal_size()
UpperCAmelCase = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class UpperCAmelCase_ ( enum.Enum):
snake_case__... | 342 | """simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import T... | 342 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __snake_case ( __SCREAMING_SNAKE_CASE )... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_available():
raise OptionalDe... | 74 | 0 |
"""simple docstring"""
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 __lowerCAmelCase (_UpperCam... | 549 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 549 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, requ... | 531 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql i... | 506 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase :Any = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtra... | 701 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase :List[str] = {
'configura... | 42 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case_ = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2StructT... | 164 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_... | 164 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowerCamelCas... | 265 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowerCamelCas... | 265 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class _A ( _lowerCamelCa... | 217 |
def UpperCamelCase_( _A :Union[str, Any] )-> List[str]:
UpperCamelCase__ = [0] * len(_A )
UpperCamelCase__ = []
UpperCamelCase__ = []
UpperCamelCase__ = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for... | 551 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
SCREAMING_SNAKE_CASE_ = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS K... | 116 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...te... | 116 | 1 |
"""simple docstring"""
_lowerCAmelCase : Any = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : int = {"*": op.mul, "/": op.truedi... | 46 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def UpperCAmelCase_ (_lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ):
if version.parse(hfh.__version__ ).release < versio... | 327 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class A__ ( _lowerCamelCase):
def __init__( s... | 721 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase ):
if n_term == "":
return []
__lowerCAmelCase : list = []
for temp in range(int(_UpperCamelCase ) ):
series.append(F"1/{temp + 1}" if series else '1' )
return series
if __name__ == "__main__":
lowerCamelCase__ ... | 549 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not... | 39 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : Dic... | 39 | 1 |
import heapq
def _snake_case ( __snake_case ):
_UpperCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to b... | 707 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 71 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(a ):
... | 511 |
from ...processing_utils import ProcessorMixin
class _A ( __magic_name__):
SCREAMING_SNAKE_CASE : Dict = '''SpeechT5FeatureExtractor'''
SCREAMING_SNAKE_CASE : Optional[Any] = '''SpeechT5Tokenizer'''
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CA... | 511 | 1 |
class a__ :
def __init__( self : Union[str, Any] , lowerCamelCase_ : Optional[int] ):
a_ : Optional[int] = arr.split(""",""" )
def UpperCAmelCase( self : List[Any] ):
a_ : Union[str, Any] ... | 713 |
import numpy as np
__lowerCamelCase = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', '''y''', '''z'''],
]
class ... | 478 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list ):
if not nums:
raise ValueError('List is empty' )
return sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctes... | 476 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : List[str] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE... | 476 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
UpperCAmelCase : str = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no corr... | 714 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : int = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available()... | 47 | 0 |
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,... | 411 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
... | 49 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}
... | 162 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_lowercase = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
... | 162 | 1 |
import math
def __magic_name__ ( lowercase_ ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = 0
UpperCamelCase = 0
while num > 0:
UpperCamelCase = num % 8
UpperCamelCase = octal + (remainder... | 606 |
import heapq as hq
import math
from collections.abc import Iterator
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : str, lowerCamelCase : List[Any] ):
'''simple docstring'''
lowercase__ = str(id_ )
lowercase__ = ... | 183 | 0 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowercase__ ( snake_case_ :bytes , snake_case_ :int ):
__UpperCAmelCase = F'''{sampling_rate}'''
__UpperCAmelCase =... | 717 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : Tuple = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config... | 397 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 360 |
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Dict = 0
for i in range(1 , 1001 ):
total += i**i
return str(_lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 30 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__( _A , _A = None , _A = None , _A = False , ):
'''simple docstring'''
UpperCamelCase__ = cipher_alphabet or [chr(_A ) for i in range(97 , 123 )]... | 265 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILIma... | 265 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 202 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a :
def __init__( self , _lowerCAmelCase ):
"""simple docstring"""
if isinstance(_lowerCAmel... | 202 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_i... | 700 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE ... | 406 | 0 |
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( a ):
"""simple docstring"""
... | 511 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCAmelCase : Optional[int] = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'... | 511 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {
"""speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc... | 700 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _A () -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = 9, 14 # noqa: F841
... | 176 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def _snake_case ( A , A , A ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must... | 90 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __snake_case ( SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[Any]:
'''simple docstring'''
_UpperCA... | 289 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
... | 54 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_be... | 54 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import Au... | 249 |
# 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
fr... | 249 | 1 |
def _lowercase ( ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 0
for i in range(1 , 1001 ):
total += i**i
return str(UpperCamelCase_ )[-10:]
if __name__ == "__main__":
print(solution())
| 400 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class lowercase__ ( _UpperCAm... | 400 | 1 |
"""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: Tupl... | 607 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 0 |
'''simple docstring'''
from __future__ import annotations
from random import random
class _a :
'''simple docstring'''
def __init__( self, A = None ):
'''simple docstring'''
... | 717 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_... | 508 | 0 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase__ : str = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"... | 376 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 259 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, 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 load_image
if is_torch_av... | 611 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import B... | 611 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say tha... | 60 |
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, prepare_image_inputs
if ... | 60 | 1 |
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,
resize,
to_channel_dimension_f... | 586 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/bigbird-roberta-base": "https://huggingface.c... | 586 | 1 |
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 _lowercase ( __lowerCamelCase : Dataset ,__lowerCamelCase : Dict[str, str] ) -> Opt... | 344 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE : str = [
"""word_embeddings... | 344 | 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
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case_... | 618 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _a ... | 618 | 1 |
"""simple docstring"""
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
snake_case : Optional[int] = False
... | 545 |
'''simple docstring'''
from math import isqrt
def __lowercase (_SCREAMING_SNAKE_CASE :int ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(_SCREAMING_SNAKE_CASE ) + 1 ) )
def __lowercase (_SCREAMING_SNAKE_CASE :int = 10**6 ):
SC... | 507 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__a = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Ale... | 257 | '''simple docstring'''
def __UpperCAmelCase ( ):
return 1
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __UpperCAmelCase ( a_: int ):
return 0 if x < 0 else five_pence(x - 5 ) + two_p... | 257 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
lowerCAmelCase__ : Optional[int] ='%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('S... | 101 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConf... | 159 | 0 |
import math
A = 1_0
A = 7
A = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCamelCase ( UpperCamelCase : Tuple = 20 ) -> str:
_lowerCamelCase = math.comb(UpperCamelCase , UpperCamelCase )
_lowerCamelCase = math.comb(NUM_BA... | 711 | import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_m... | 234 | 0 |
snake_case : Union[str, Any] = 8.31_44_62 # Unit - J mol-1 K-1
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. Enter ... | 605 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder... | 142 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention... | 717 | '''simple docstring'''
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 SCREAMING_SNAKE_CASE__ ( snake_case : Dict ) -> Optional[Any]:
... | 610 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__SCREAMING_SNAKE_CASE = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 357 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
... | 442 | 0 |
import os
def lowerCamelCase__ (_UpperCAmelCase = "matrix.txt"):
with open(os.path.join(os.path.dirname(_UpperCAmelCase) , _UpperCAmelCase)) as in_file:
SCREAMING_SNAKE_CASE = in_file.read()
SCREAMING_SNAKE_CASE = [[int(_UpperCAmelCase) for cell in row.split(',')] ... | 444 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
... | 444 | 1 |
__lowercase : List[Any] ="""Alexander Joslin"""
import operator as op
from .stack import Stack
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ ={"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
UpperCAme... | 54 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 446 | 0 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import sh... | 649 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowerCamelCase : Any = None
try:
import msvcrt
except ImportError:
lowerCamelCase : str = None
try:
import fcntl
except ImportError:
lowerCamelCase : Optional[Any] = ... | 649 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __lowerCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Optional[... | 354 |
"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
A: Union[str, Any] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_... | 160 | 0 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Any = logging.get_logger(__name__... | 329 |
'''simple docstring'''
lowerCAmelCase__ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCAmelCase__ : int = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCAmelCase__ : List[Any] = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "F... | 329 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class __snake_case ( nn.Module ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = jnp.floataa
def a ( self ) -> Union[s... | 268 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __snake_case :
"""simple docstring"""
def __init__( self ... | 268 | 1 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transform... | 222 |
"""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_property
fr... | 222 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"microsoft/focalnet-tiny": "https... | 579 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_fi... | 579 | 1 |
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.testing_utils import D... | 703 |
__a: List[Any] = tuple[float, float, float]
__a: Optional[int] = tuple[float, float, float]
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> Vectorad:
_UpperCAmelCase = end_pointa[0] - end_pointa[0]
_UpperCAmelCase = ... | 402 | 0 |
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