code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowercase (SCREAMING_SNAKE_CASE_ : str ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = [
"""encoder... | 247 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class Upp... | 635 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase : Optional[Any] ... | 716 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from... | 47 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 287 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeature... | 287 | 1 |
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_A ):
"""simple docstring"""
A = ['''note_seq''']
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ):
'''simple docstring'''
requires_backe... | 111 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
_... | 111 | 1 |
import os
import numpy
import onnx
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Dict , UpperCamelCase__: str ):
SCREAMING_SNAKE_CASE__ = a.name
SCREAMING_SNAKE_CASE__ = b.name
SCREAMING_SNAKE_CASE__ = """"""
SCREAMING_SNAKE_CASE__ =... | 6 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 0 |
'''simple docstring'''
lowerCAmelCase__ : List[str] = {
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarb... | 502 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import comput... | 502 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=... | 188 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPho... | 286 | 0 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow... | 703 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase = logging.getLogger()
def A ( lowercase__ : ... | 383 | 0 |
"""simple docstring"""
import os
import sys
import unittest
UpperCamelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get... | 227 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
... | 458 | 0 |
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int = 100_0000 ):
'''simple docstring'''
lowercase_ = limit + 1
lowercase_ = [0] * limit
for first_term in range(1 , __lowerCamelCase ):
for n in range(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
... | 601 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from... | 601 | 1 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is... | 34 |
import math
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 563 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( __snake_case : int , __snake_case : int , __snake_case : bool , __snake_case : list[int] , __snake_case : float ):
if d... | 141 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _UpperCAm... | 141 | 1 |
"""simple docstring"""
snake_case = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def snake_case ( lowerCAmelCase_ ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(lowerCAmelCase_ , lowerCAmelCase... | 103 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( __lowerCamelCase : ArgumentParser ):
"... | 103 | 1 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def lowerCAmelCase__ ( UpperCAmelCase ):
"""simple docstring"""
snake_case__ : List[str] = year % 19
snake_case__ : Optional[Any] = ye... | 172 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase__ ( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
if len(UpperCAmelCase ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase ... | 172 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 278 | """simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = False ) -> list[float]:
'''simple doc... | 530 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> None:
"""simple docstring"... | 323 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> None:
"""simple docstring"... | 323 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 376 |
import numpy as np
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 376 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp... | 720 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : List[str] = len(_UpperCamelCase )
__lowerCAmelCase : Tuple = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
__lower... | 549 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__a: int = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenager... | 108 |
'''simple docstring'''
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 UpperCamelCase ( a , a ) -> Tuple:
... | 432 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__A : Optional[int] = logging.get_logger(__name__)
__A : List[Any] = r"""
Args:
input_... | 703 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__A : Dict = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__A : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
def lowerCamelC... | 450 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate impor... | 61 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 61 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
# TODO Update this
_SCREAMING_SNAKE_CASE : Optional[int] = {
'... | 55 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 55 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
A_ = logging.getLog... | 29 |
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 ... | 311 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, ... | 436 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 436 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : List[Any] = logging.get_logger(__name__)
_snake_case : int = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
... | 441 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __a :
pass | 552 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffuse... | 84 |
import re
def a_ ( __magic_name__ ) -> bool:
"""simple docstring"""
snake_case : List[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(... | 84 | 1 |
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,
PILImageResampling,
get_... | 520 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'google/bit-50': 'https://huggi... | 520 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stabl... | 717 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =get_failure_array(lowerCAmelCase_ )
... | 153 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : List[str] , __a : Collection[float] | None = None ) -> None:
... | 149 |
from __future__ import annotations
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : str , __a : Dict=None ) -> int:
"""simple docstring"""
__lowercase : int = data
__lowercase : Optional[int... | 149 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
a__ : Optional[int] = 4
a__ : Union[str, Any] = 3
class ... | 721 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_ut... | 223 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_... | 278 |
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()
A : D... | 219 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class lowerCAmelCase :
def __init__( self :Optional[int] ):
'''simple docstring'''
lowercase__ = ""
lowercase__ = ""
lowercase__... | 611 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transforme... | 611 | 1 |
"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformer... | 139 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCamelCase ( ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Any = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]"... | 139 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_lowerCAmelCase = logging.get_logger(__name__)
class UpperCamelCase (lowercase__ ):
def __init__( self :List[Any] , *__magic_name__... | 704 |
"""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/LI... | 348 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils im... | 576 | def UpperCAmelCase__( __UpperCAmelCase : int ):
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__snake_case : str = 4
__snake_case : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
... | 576 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tok... | 709 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if i... | 537 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_d... | 71 |
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_ = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
pr... | 411 | 0 |
'''simple docstring'''
def __magic_name__( _A , _A ):
'''simple docstring'''
UpperCamelCase__ = len(_A )
UpperCamelCase__ = len(_A )
UpperCamelCase__ = (
first_str_length if first_str_length > second_str_length else second_st... | 265 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] = {
'''SenseTime/deformable-detr''': '''ht... | 265 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 41 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def _UpperCamelCase ( ):
"""simple docstring"""
__magic_name__ : Optional[int] = 9
__magic_name__ : Tuple = [
... | 436 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
_lowerCAmelCase : ... | 604 |
def UpperCAmelCase_ ( snake_case__ = 200 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = [1, 2, 5, 10, 20, 50, 100, 200]
lowerCAmelCase__ = [0] * (pence + 1)
lowerCAmelCase__ = 1 # base case: 1 way to make 0 pence
for coin in coins:
... | 604 | 1 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A , A ) -> float:
lowerCAmelCase__ = sorted(numsa + numsa )
lowerCAmelCase__ , lowerCAmelCase__ = divmod(len(A ) , 2 )
if mod == 1:
... | 90 |
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
from sagemaker.huggingface impor... | 402 | 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( _a , unittes... | 704 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_UpperCamelCase : Optional[int] = datasets.logging.get_logger(__name__)
_UpperCamelCase : Dict = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust ... | 341 | 0 |
import math
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if (
not isinstance(SCREAMING_SNAKE_CASE , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError(... | 303 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __a ( SC... | 303 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from tran... | 193 |
import math
def lowerCamelCase__ ( _a , _a):
if (
not isinstance(_a , (int, float))
or power_factor < -1
or power_factor > 1
):
raise ValueError("power_factor must be a valid float value between -1 and 1.")
return apparent_power * power_factor
def lowerCamelCase__ ( _a ... | 193 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate... | 95 | """simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowerCamelCase( a , a = "cpu" , a = None ):
__a = torch.load(a , map_location=a )
for k, v in tqdm(state_dict.items() ):
if not isinstance(a , tor... | 528 | 0 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ... | 397 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowercase__ ( ):
with offline(OfflineSimulationMode.CONNECTION_T... | 397 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowerCamelCase_ ( _UpperCAmelCase ):
__lowercase : ... | 147 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 698 | 0 |
from __future__ import annotations
from typing import Any
def lowerCamelCase_ ( lowerCAmelCase: list[Any] )-> None:
create_state_space_tree(lowerCAmelCase , [] , 0 )
def lowerCamelCase_ ( lowerCAmelCase: list[Any] , lowerCAmelCase: list[Any] , lo... | 669 |
def lowerCamelCase_ ( lowerCAmelCase: int )-> list:
_snake_case : List[Any] = int(lowerCAmelCase )
if n_element < 1:
_snake_case : int = ValueError('a should be a positive number' )
raise my_error
_snake_case : Union[str, Any] ... | 669 | 1 |
def lowerCAmelCase_ ( __a , __a ) -> list[int]:
"""simple docstring"""
lowerCamelCase__: Optional[int] =int(__a )
# Initialize Result
lowerCamelCase__: Any =[]
# Traverse through all denomination
for denomination in reversed(__a ):
... | 59 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureEx... | 431 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A = 1_000):
"""simple docstring"""
_a = 2**power
_a = 0
while n:
_a , _a = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input())... | 719 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 352 | 0 |
import math
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : int , lowerCAmelCase__ : Tuple=0 ) -> List[Any]: # a graph with Node 0,1,...,N-1
snake_case__ = n
snake_case__ = [
... | 214 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/... | 156 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierO... | 525 | 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_available, is_vision_availa... | 525 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = 1 / sqrt(2 ) ):
"""simple docstring"""
a_ = tau * frequency / samp... | 483 |
"""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 lowercase_ ( __lowerCAmelCas... | 7 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"""google/pix2struct-textcaps-base""": (
... | 705 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( _UpperCamelCase ):
@require_torch
def _snake_case ( self ) -> str:
"""simple ... | 629 | 0 |
from __future__ import annotations
A_ : Union[str, Any] = 'Muhammad Umer Farooq'
A_ : Tuple = 'MIT'
A_ : int = '1.0.0'
A_ : Optional[int] = 'Muhammad Umer Farooq'
A_ : Dict = ... | 57 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class lowerCA... | 247 | 0 |
def __UpperCamelCase ( a, a) ->Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(a, int(b / 2)) * actual_power(a, int(b / 2))
else:
return a * actual_power(a, int(b / 2)) * actual_power(a, int(b / 2))
def __U... | 360 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , _lowerCAmelCase , _lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = text, pattern
lowerCamelCase__ , lowerCamelCase__ = len(_l... | 360 | 1 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_SCREAMING_SNAKE_CASE = '.'
# Internal TensorFlow ops tha... | 401 |
'''simple docstring'''
import math
def a__ ( a__ ):
"""simple docstring"""
return math.sqrt(a__ ) * math.sqrt(a__ ) == num
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = n
while left <= ... | 627 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCamelCase__ ( yaml.SafeLoader ):
"""simple docstring"""
def lowerCamelCase__ ( self : Optional[int] , UpperCamelCase ... | 707 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
loggin... | 299 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def snake_case_ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = Non... | 199 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A__ ( __A : Any , __A : str , __A : str , __A : Path , __A : str = None , __A :... | 184 | 0 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
__A ={
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_co... | 313 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def lowerCamelCase_ ( lowerCamelCase__ ):
if "cls_token" in name:
lowerCamelCase_ = name.replace("cls_token" , "vit.em... | 313 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase_ : Tuple = 3
def A__ ( snake_case_ : int ):
print('''Generating primitive root of p''' )
while True:
SCREAMING_SNAKE_CASE__: List[Any]= random.randrange(3 ... | 64 | 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 | 1 |
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class lowercase_ :
"""simple docstring"""
def __init__( self : List[Any] ):
__lowercase = psutil.Process()
__lowercase = False
def SCREAMI... | 720 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
__lowercase = sum(A__ ) / len(A__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / ... | 624 | 0 |
"""simple docstring"""
import math
import qiskit
def _snake_case ( _snake_case : int = 1 , _snake_case : int = 1 , _snake_case : int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(_snake_case ,... | 7 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case_ (lowerCamelCase_ ):
UpperCAmelCase__ : List[str] = ['''image_processor''', '''tokenizer''']
UpperCAmelCase__ : Dict = '''CL... | 335 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ... | 476 | import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCAmelCase ( unittest.TestCase ):
def a__ ( self ):
... | 476 | 1 |
'''simple docstring'''
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_mod... | 94 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimensio... | 36 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class ... | 718 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTester... | 177 | 0 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import Base... | 368 |
'''simple docstring'''
a__ : Optional[Any] = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def __lowerCamelCase ( UpperCAmelCase_ ) ->int:
snake_case__ = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
... | 368 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, 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
... | 680 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 680 | 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
__A : int = False
class __lowerCAmelCase ( ... | 656 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 44 | 0 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def A__ ( A__ , A__ ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = int(A__ )
assert noofclusters < len(A__ )
# Find out the dimensionality
_U... | 579 |
"""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 ... | 579 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(A ) , ... | 11 |
import logging
from transformers import PretrainedConfig
lowerCamelCase__ = logging.getLogger(__name__)
lowerCamelCase__ = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}
class S... | 225 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowercase__ : Union[str, Any] ... | 714 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase__ : Union[str, Any] = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_A... | 43 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a_ = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarning(
'... | 25 | import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCAmelCase_ = logging.getLogger(__name__)
def lowerCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase: List[str] = argpa... | 271 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Dict = {
"facebook/dat... | 5 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 5 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 59 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__magic_name__ : Optional[int] = False
class __SCREAMING_SNAKE_CA... | 672 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCamelCase ( _... | 715 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: int ):
if number > 0:
raise ValueError("""input must be a negative integer""" )
__SCREAMING_SNAKE_CASE : str = len(bin(_lowerCamelCase )[3:] )
__SCREAMING_SNAKE_CASE : Any = bin(abs(_lowerCamel... | 178 | 0 |
'''simple docstring'''
import re
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
if len(re.findall("[ATCG]" , _SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("Invalid Strand" )
return dna.tra... | 71 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
snake_case : int = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Language... | 124 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : str = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerC... | 719 |
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 transformers
from transformers import (
AutoConfig,
... | 341 | 0 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
... | 481 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' ,[
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10 ... | 481 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ ={
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 442 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ ={
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"c... | 442 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCAmelCase__ ( _a : int , _a : int , _a : int , _a : int , _a : int , _a : int ):
if (ksize % 2) == 0:... | 568 |
"""simple docstring"""
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""",
"""TimeSeries... | 178 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, ... | 711 |
"""simple docstring"""
a : str = 8.314_4598
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float ) ->float:
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 ... | 31 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def __snake_case ( SCREAMING_SNAKE_CASE_ : Callable[[int | float], int | float] , SCREAMING_SNAKE_CASE_ : int | float , SCREAMING_SNAKE_CASE_ : int | float , SCREAMING_SNAKE_CASE_ : int = 100 ,... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
raise... | 356 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
'''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.js... | 404 |
"""simple docstring"""
def snake_case ( ) -> Tuple:
_snake_case = 0
for i in range(1 , 1001 ):
total += i**i
return str(lowerCAmelCase_ )[-10:]
if __name__ == "__main__":
print(solution())
| 404 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {}
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'llama'
SCREAMING_SNAKE_CASE_ ... | 42 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerC... | 1 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
... | 502 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 502 | 1 |
import math
import qiskit
def __UpperCamelCase ( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ):
"""simple docstring"""
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_lowerCAmelCase , _lowerCAmelCase )
or... | 333 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase =logging.get_logger(__name__)
__lowerCAmelCase ={
"microsoft/beit-base-p... | 333 | 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_for... | 431 |
# Function to print upper half of diamond (pyramid)
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int ) -> Dict:
for i in range(0 , UpperCAmelCase_ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end... | 431 | 1 |
def __UpperCAmelCase( lowercase_ ):
_lowerCamelCase : int = len(lowercase_ )
_lowerCamelCase : str = len(matrix[0] )
_lowerCamelCase : Tuple = min(lowercase_ , lowercase_ )
for row in range(lowercase_ ):
# Check if diagona... | 114 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 1 |
'''simple docstring'''
import os
def _lowerCAmelCase ( ) ->Union[str, Any]:
"""simple docstring"""
lowercase__ = os.path.dirname(os.path.realpath(lowercase ) )
lowercase__ = os.path.join(lowercase , '''triang... | 318 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowerCAmelCase ( lowercase : List[Any] , lowercase : Tuple , lowercase : Union[str, Any] ... | 318 | 1 |
from math import sqrt
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> Optional[int]:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 579 |
'''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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : ... | 365 | 0 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE ( ):
with open(os.path.dirname(a_ ) + '/p022_names.txt' ) as file:
__a = str(file.readlines()[0] )
__a = names.replace('"' , '' ).split(',' )
names.sort()
__a = 0
_... | 490 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_d... | 490 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
tra... | 34 |
"""simple docstring"""
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,
AutoModelForSequ... | 624 | 0 |
'''simple docstring'''
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 t... | 712 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case ( ... | 445 | 0 |
"""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
A_ : str ... | 265 |
"""simple docstring"""
def __snake_case ( __A : int , __A : int ) -> float:
'''simple docstring'''
return base * power(__A , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent us... | 265 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE... | 599 |
'''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 vocab first, and then a tiny model - so the outcome is truly tiny -
#... | 599 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_availab... | 53 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 158 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_... | 348 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( _A , _A=7 ) -> List[str]:
lowercase : Any = None
if token is not None:
lowercase ... | 348 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-e... | 94 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( __magic_name__ : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__magic_name__ ) / len(__magic_name__ )
if __name_... | 38 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 706 |
"""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_inputs
... | 349 | 0 |
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