code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resol... | 100 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggin... | 100 | 1 |
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,
TrainingArgumen... | 368 |
"""simple docstring"""
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()
lowerCAmelCase__ = [
'''word_e... | 175 | 0 |
__snake_case = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def lowerCAmelCase_ ... | 348 | from collections.abc import Sequence
from queue import Queue
class a__ :
def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ):
"""simple docstring"""
... | 18 | 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
lowerCamelCase_ : Dict = logging.get_logger(__name__)
lowerCamelCase_ : Dict = r"""
Args:
input_ids (`torch.LongTensor... | 364 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(lowerCamelCase ) * abs(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose... | 223 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Union[str, Any] ={
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_avail... | 170 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretr... | 169 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
a_ = Phobert... | 363 |
from collections.abc import Sequence
def a_ ( lowerCAmelCase_ : Sequence[float], lowerCAmelCase_ : bool = False ):
if not arr:
return 0
__lowerCAmelCase = 0 if allow_empty_subarrays else float('-inf' )
__lowerCAmelCase = 0.0
for n... | 207 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, Prop... | 252 |
def __lowerCamelCase ( lowerCamelCase__ : Any , lowerCamelCase__ : Optional[int] ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __lowerCamelCase ( lowerCamelCase__ : List[str] , lowerCamelCase__ ... | 252 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
_lowerCamelCase : str = u
for i in range(1 , _lowerCamelCase ):
_lowerCamelCase :... | 356 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class A_ ( _a ):
lowe... | 340 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
... | 79 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( snake_case__ , unittest.TestCase):
"""simple... | 39 | 0 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_con... | 240 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase :Tuple = "Muhammad Umer Farooq"
__UpperCAmelCase :Tuple = "MIT"
__UpperCAmelCase :Union[str, Any] = "1.0.0"
__UpperCAmelCase :Optional[int] = "Muhammad Umer F... | 240 | 1 |
"""simple docstring"""
from math import ceil, sqrt
def lowerCAmelCase__ ( UpperCamelCase__ = 1_0_0_0_0_0_0 ):
'''simple docstring'''
_a : Any = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_... | 294 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
... | 294 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_lowerCamelCase):
_a = ['''torch''', '''scipy''']
def __init__( self: Dict , *_lowerCAmelCase: Any , **_lowerCAmelCase: int ):
requires_backends(self , [... | 356 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
g... | 158 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/reso... | 327 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_SCREAMING_SNAKE_CASE = 50_00_00
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = os.path.split(__file__)
_SCREAMING_SNAKE_CASE ... | 327 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE :int = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_... | 358 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch... | 156 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def lowercase__( __SCREAMING_SNAKE_CASE : Callable ):
@wraps(__SCREAMING_SNAKE_CASE )
def _inner_fn(*__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE ... | 213 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaF... | 213 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ='▁'
lo... | 242 |
'''simple docstring'''
lowercase =[0, 2, 4, 6, 8]
lowercase =[1, 3, 5, 7, 9]
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[int] , __lowerCamelCase : int ):
'''simple docstring'''
i... | 242 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''facebook/xmod... | 37 | import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
cl... | 175 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: Any = logging.get_logger(__name__)
a__: Optional[int] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
... | 350 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a__: Union[str, Any] = False
class SCREAMING_SNAKE_CASE_... | 39 | 0 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {'''vocab_file''': '''vocab.txt'''}
_snake_case = {
'''vocab... | 36 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is... | 223 | 0 |
'''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_g... | 243 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 243 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :str = logging.get_logger(__name__)
_lowerCAmelCase :Any = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json'... | 263 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _UpperCAmelCase ( A__ ):
... | 207 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict ... | 368 | """simple docstring"""
def lowerCAmelCase (__UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
__UpperCamelCase =[0 for i in range(r + 1 )]
# nc0 = 1
__UpperCamelCase =1
for i in range(1 , n + 1 ):
... | 85 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 279 |
from collections import defaultdict
from math import gcd
def _a ( UpperCamelCase_ : int = 1_500_000 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = defaultdict(UpperCamelCase_ )
lowerCAmelCase__ = 2
while 2 * euclid_m ... | 340 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _a :
pass
| 362 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''facebook/xlm-roberta-xl''': '''https://h... | 211 | 0 |
import math
def __lowercase ( __lowerCAmelCase : float , __lowerCAmelCase : float ):
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if an... | 240 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
cl... | 240 | 1 |
'''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_pipeline_test,
nested_simplify,... | 361 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(_SCREAMI... | 270 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@requir... | 69 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
while a != 0:
_lowerCAmelCase , _lowerCAmelCase = b % a, a
return b
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING... | 158 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_con... | 128 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _a ... | 128 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testin... | 27 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=lowerCAmelCase_ ):
"""simple docstring"""
A__ : Tuple = ['''speech''']
def __init__( self : List[Any] , *_snake_case : str , **_snake... | 156 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Tuple = logging.get_logger(__name__)
lowerCAmelCase_ : List[Any] = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-ba... | 248 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=a_ )
class UpperCamelCase_ ( a_ ):
_A : str = field(default='ima... | 248 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_A = logging.get_logger(__name__)
class _lowerCamelCase ( a_ ):
def __init__( self : int , *UpperCamelCase : int , **UpperCamelCase : ... | 242 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 242 | 1 |
def UpperCamelCase ( __magic_name__ : str ) -> str:
"""simple docstring"""
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string w... | 359 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : Tuple = {
'YituTech/conv-bert-base': 'https://h... | 146 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCamelCase_( lowerCamelCase_ ) -> float:
return np.dot(lowerCamelCase_ , lowerCamelCase_ )
class _lowerCamelCase:
def __init__( self, *,
lo... | 21 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transfor... | 39 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = ""
snake_case = (
... | 65 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , *_SCREAMING_SNAKE_CASE , ... | 65 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_visi... | 243 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
a_ = set()
# Replace all the whitespace in our sentence
a_ = input_str.replace(" " , "" )
for alpha in input... | 243 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require... | 367 |
from string import ascii_uppercase
_lowercase : str ={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase : Dict =dict(enumerate(ascii_uppercase))
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
... | 266 | 0 |
"""simple docstring"""
lowerCAmelCase_ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCAmelCase_ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCAmelCase_ = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday'... | 16 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , lowercase_ ):
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
... | 85 | 0 |
"""simple docstring"""
import copy
import re
class _UpperCAmelCase :
_lowerCAmelCase : int = """hp"""
_lowerCAmelCase : List[Any] = {}
_lowerCAmelCase : Any = None
@classmethod
def _snake_case ( cls : str , lowercase_ : List[str] , lowercas... | 155 |
"""simple docstring"""
from math import factorial
lowercase__ : Optional[int] = {str(d): factorial(d) for d in range(10)}
def __lowercase ( _a ):
return sum(DIGIT_FACTORIAL[d] for d in str(_a ) )
def __lowercase ( ):
snake_case_ : Optional[int] ... | 155 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case__ = 2_0_0_0_0_0_0 ) -> Optional[int]:
lowerCAmelCase = [0]
lowerCAmelCase = 4_2
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 338 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at ... | 211 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 352 |
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,
AutoTokeniz... | 328 | 0 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__SCREAMING_SNA... | 22 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowerCAmelCase__ :
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> Dict:
... | 270 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __lowerCAmelCase : list , __lowerCAmelCase : int , __lowerCAmelCase : int = 0 , __lowerCAmelCase : int = 0 ) -> int:
snake_case = right or len(__lowerCAmelCase ) - 1
if left >... | 353 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
snake_case_ = "WhisperFeatureExtractor"
snake_case_ = "WhisperTokenizer"
def __init__( self : D... | 3 | 0 |
def _lowerCAmelCase (_lowerCAmelCase = 1_00):
UpperCamelCase_ = 0
UpperCamelCase_ = 0
for i in range(1 , n + 1):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"{s... | 128 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 128 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Conditiona... | 360 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _UpperCAmelCase (UpperCamelCase_ : str , UpperCamelCase_ : Tuple=None ):
'''simple docstring'''
_lowerCAmelCase : List[Any] ... | 159 | 0 |
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : List[Any] = len(a__)
for i in range(a__):
for j in range(i + 1 , a__):
if numbers[j] < numbers[i]:
a_ , a_ : int = numbers[j], numbers[i]
return numbers
if __name__ == ... | 248 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils i... | 248 | 1 |
"""simple docstring"""
class __snake_case : # Public class to implement a graph
def __init__( self , lowercase , lowercase , lowercase) -> None:
'''simple docstring'''
a__: Optional[int] = row
a__: List[str] ... | 353 | """simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase__ = re.compile(r'\b(a|an|the)\b', re.UNICODE)
lowercase__ = None
def __a ( ) ->List[Any]:
a__: Dict = argparse.Ar... | 203 | 0 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase... | 205 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any ... | 146 | 0 |
"""simple docstring"""
from math import ceil, sqrt
def snake_case_(_UpperCamelCase = 1_000_000 ) -> int:
"""simple docstring"""
_snake_case = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_snake_case = max(c... | 357 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 278 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCAmelCase_ ( __A ) -> typing.Counter[int]:
'''simple docstring'''
UpperCAmelCase__ = Counter()
for base in range(1, max_perimeter + 1 ):
... | 65 | def lowerCAmelCase_ ( __A, __A ) -> float:
'''simple docstring'''
def get_matched_characters(__A, __A ) -> str:
UpperCAmelCase__ = []
UpperCAmelCase__ = min(len(_stra ), len(_stra ) ) // 2
... | 65 | 1 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
... | 364 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"""vocab_file""": """vocab.txt"... | 297 | 0 |
'''simple docstring'''
def a__ ( a__ = 1_00_00_00 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = set(range(3 , a__ , 2 ) )
primes.add(2 )
for p in range(3 , a__ , 2 ):
if p not in primes:
... | 267 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCAmelCase__ ( ... | 267 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _lowerCAmelCase ( _UpperCamelCase : Any ) -> Dict:
"""simple docstring"""
if "cls_token" in name:
... | 114 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =int(number**0.5 )
return number == sq * s... | 114 | 1 |
"""simple docstring"""
# 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(... | 155 |
"""simple docstring"""
def lowercase (snake_case__ : list[int] , snake_case__ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case__ ) == len(snake_case__ ) == 3:
raise ValueError("""Please e... | 155 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_r... | 108 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImage... | 108 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTrai... | 55 |
from __future__ import annotations
from collections.abc import Callable
def A_ ( snake_case : Callable[[int | float], int | float] , snake_case : int | float , snake_case : int | float , snake_case : int = 100 , ) ... | 328 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
... | 366 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[dict, list, tu... | 177 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
_A : Tuple = []
_A : Union[str, Any] = []
_A : Optional[int] = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
"""%""": 2,
"""+""": 1,
"""-""": 1,
... | 26 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A ( nn.Module ):
__magic_name__ = 42
__magic_name__ ... | 3 | 0 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__UpperCamelCase : Union[str, Any] = namedtuple(
... | 74 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
ra... | 74 | 1 |
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 lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ ... | 131 |
def _lowerCAmelCase ( lowerCAmelCase_ :int = 1_000 )->int:
'''simple docstring'''
snake_case_ , snake_case_ = 1, 1
snake_case_ = 2
while True:
snake_case_ = 0
snake_case_ = fa + fa
snake_case_ , snake_... | 159 | 0 |
"""simple docstring"""
from __future__ import annotations
class lowerCAmelCase__ :
def __init__( self : str , snake_case__ : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = order
# a_{0} ... a_{k}
UpperCAmelCase__ ... | 298 |
"""simple docstring"""
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils ... | 298 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from t... | 43 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 203 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get... | 359 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficien... | 130 | 0 |
import string
def __magic_name__ ( A : str ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
a = ""
for symbol in message:
if symbol in string.ascii_uppercase:
a = string.ascii_uppercase.find(A )
... | 107 |
import argparse
from collections import defaultdict
import yaml
_A = '''docs/source/en/_toctree.yml'''
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = defaultdict(_A )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase_ = [key for k... | 278 | 0 |
'''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 lowerCamelCase ( __lowerCAmelCase ):
snake_cas... | 332 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( A ) -> list:
if len(A ) == 0:
return []
snake_case , snake_case = min(A ), max(A )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] for _ in ra... | 332 | 1 |
class _lowercase :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ : int = ''
lowerCamelCase__ : List[Any] = ''
lowerCamelCase__ : ... | 184 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ ( _A , _A ):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative... | 297 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 146 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch... | 146 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers... | 114 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixi... | 114 | 1 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def __snake_case( _lowerCAmelCase ) -> Union[str, Any]:
snake_case__ , snake_case__ : Union[str, Any] = img.shape[0], img.shape[1]
# converting each pixel's color to its negati... | 357 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 0 |
"""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,
AutoModelForSe... | 108 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ ... | 108 | 1 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : int = {
"microsoft/xprophetnet-large-wiki100-cased": (
... | 326 |
"""simple docstring"""
__A : Tuple = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__A : U... | 326 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, to... | 101 | """simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prep... | 177 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowerCamelCase : List[str] = logging.get_logger(__name__)
class lowerCAmelCase ( __a ):
'''simple docstring'''
def __init__( self : str , *__a :... | 306 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@requi... | 306 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _snake_case ( snake_case__ : int ): # picklable ... | 74 |
"""simple docstring"""
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : int ,A_ : int ) -> Union[str, Any]:
A = n
A = [None] * self.n
A = 0 # index of the first element
A = ... | 74 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Any = {
'''huggingface/time-series-transformer-tourism-monthly''': (
... | 366 |
def lowerCAmelCase__ ( _a : dict ):
snake_case_ : List[Any] = set()
# edges = list of graph's edges
snake_case_ : int = get_edges(_a )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extre... | 36 | 0 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__(self , _UpperCAmelCase="" , _UpperCAmelCase="train" ) -> ... | 298 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(snake_case__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod(... | 298 | 1 |
'''simple docstring'''
from maths.prime_check import is_prime
def snake_case__ ( lowerCamelCase__ : int ) -> int:
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ : Dict = f'Input value of [number={number}] must be an... | 4 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enu... | 4 | 1 |
from __future__ import annotations
import math
def __lowerCAmelCase ( a__ ) -> list[int]:
if num <= 0:
__a = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(a__ )
__a = [True] * (num + 1)
__a = []
_... | 6 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobe... | 130 | 0 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.... | 340 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_( _lowerCamelCase ) -> Any:
'''simple docstring'''
for param in module.parameters():
_lowerCamelCase : Optional[int] = False
def ... | 340 | 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 _UpperCAmelCase ( _lowerCAmelCase )... | 332 |
"""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
... | 332 | 1 |
'''simple docstring'''
def a_ ( __snake_case : Optional[int] ) -> Dict:
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def a_ ( __snake_case : ... | 368 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def a_ ( __snake_case : str , __snake_case : bool = True , __snake_case : float = math.inf , __snake_case : float = ... | 6 | 0 |
import argparse
__UpperCamelCase : Dict = "docs/source/_static/js/custom.js"
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCamelCase__ : List[str]... | 146 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : s... | 146 | 1 |
"""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... | 302 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configu... | 302 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : int = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class __sn... | 51 | 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_image_size... | 43 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(lowercase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 176 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Optional[Any] = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE... | 176 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/... | 326 |
def lowerCAmelCase__( lowercase : list[int] , lowercase : int ) -> bool:
__snake_case : List[str] = len(lowercase )
__snake_case : int = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed ... | 326 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiff... | 27 |
"""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_uti... | 27 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
... | 306 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pi... | 306 | 1 |
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase_( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase : Optional[Any] = 9
_lowerCamelCase : Tuple = [
[0, 1, 4],
[0, 7, 8],
[... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = ... | 340 | 1 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
A__ = FileLock(str(tmpdir / 'foo.lock' ) )
A__ ... | 221 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
c... | 36 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
def lowerC... | 359 |
'''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 t... | 160 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def a_ ( lowerCamelCase : int ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
lowerCAmelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCamelCase ... | 4 |
'''simple docstring'''
import os
__snake_case ={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def a_ ( lowerCamelCase : str ):
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(lowerCamelCas... | 4 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase_ ( __lowerCAmelCase = 8 )-> str:
'''simple docstring'''
UpperCAmelCase : str =ascii_letters + digits + punctuation
... | 78 | from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...ut... | 78 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 26 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 331 | 0 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : str = [True] * n
A : Union[str, Any] = False
A : Optional[Any] = False
A : List[str] = True
... | 311 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[str] = 2
A : Dict = []
while i * i <= n:
if n % i:
i += 1
else:... | 311 | 1 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = int(np.ceil((x_end - xa) / step_size ) )
lowerCamelCase_ = np.zeros((n + 1,) )
... | 19 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A : Optional[Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import iter... | 6 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require... | 351 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int , lowercase : int , lowercase : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(lowercase : int , lowercase : int ) -> int:
# BASE... | 112 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 298 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'p... | 298 | 1 |
'''simple docstring'''
from math import isqrt, loga
def __magic_name__ ( __UpperCAmelCase ) -> list[int]:
'''simple docstring'''
snake_case_ = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j... | 72 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
if n == 0:
return 0
snake_case_ = float('''-inf''' )
for i in range(1, ... | 72 | 1 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interl... | 176 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase__ ( _UpperCAmelCase ):
A__ : Union[str, Any] =... | 176 | 1 |
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, De... | 292 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case (UpperCAmelCase__ ) -> tuple:
return (data["data... | 292 | 1 |
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