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 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 (
... | 98 |
'''simple docstring'''
from PIL import Image
def _a( UpperCamelCase__ : Image, UpperCamelCase__ : float ):
'''simple docstring'''
def brightness(UpperCamelCase__ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
... | 152 | 0 |
"""simple docstring"""
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `StableD... | 351 | """simple docstring"""
from math import pi
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 312 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_A = logging.get_logger(__name__)
# TODO: upload to AWS
_A = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
),
}
... | 62 | from math import sqrt
def _UpperCamelCase ( snake_case__ ) -> int:
__UpperCAmelCase : Union[str, Any] = 0
for i in range(1, int(sqrt(snake_case__ ) + 1 ) ):
if n % i == 0 and i != sqrt(snake_case__ ):
... | 157 | 0 |
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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import... | 357 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 297 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = OrderedDict... | 59 |
lowercase = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowercase = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase = {
... | 178 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
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_wa... | 228 |
'''simple docstring'''
import argparse
import json
import subprocess
def _snake_case ( A , A ) -> Tuple:
lowerCAmelCase__ = []
lowerCAmelCase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: ... | 228 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a :str = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
... | 312 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backb... | 171 | 0 |
'''simple docstring'''
import math
import sys
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
if number != int(_SCREAMING_SNAKE_CASE ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the va... | 294 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .t... | 294 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__UpperCAmelCase =logging.get_logger(__n... | 67 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase ={
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransforme... | 67 | 1 |
"""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... | 226 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# p... | 226 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase = " " ) -> list:
lowerCamelCase__ : Tuple = []
lowerCamelCase__ : str = 0
for index, char in enumerate(_UpperCAmelCase ):
if char == separator:
split_words.append(... | 50 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_UpperCAmelCa... | 50 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : str = '''ClapFeatureExtractor'''
A : List[Any] = ... | 353 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase_ = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass clas... | 246 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
... | 303 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowercase_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFil... | 303 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from .... | 354 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 0 |
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__)
class A ( a_ , a_ ):
__UpperCAmel... | 65 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE_CASE( a_ ... | 307 | 0 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ : Dict = logging.get_logger(__... | 349 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = [10, 20, 30, 40, 50, 60]
... | 349 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_ten... | 280 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
for i in range(1 ,len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 ... | 312 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
UpperCamelCase__ = len(UpperCamelCase__ )
UpperCamelCase__ = ... | 35 | import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging impo... | 35 | 1 |
'''simple docstring'''
UpperCamelCase__: Optional[int] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def snake_case_ ( _lowerCAmelCase : Union[str, Any... | 23 |
"""simple docstring"""
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict, lowerCamelCase : list )-> None:
lowerCamelCase__ : Tuple =set_counts
lowerCamelCase__ : Dict =max(lowerCamelCase )
lo... | 238 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOu... | 147 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import B... | 147 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> List[Any]:
UpperCamelCase__ : str = [0] * len(__lowerCamelCase )
UpperCamelCase__ : Optional[Any] = []
UpperCamelCase__ : str = []
UpperCamelCase__ : Tuple = ... | 189 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_snake_case : int ... | 123 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 260 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common impor... | 260 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_C... | 294 |
"""simple docstring"""
import unittest
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , ):
'''simple docstring'''
_a : List[Any] = np.shape(UpperCame... | 294 | 1 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a ( __a = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def a ( _... | 355 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
__snake_case = logging.getLogger(__name__)
class lowercase ( A__ ):
"""simple docstring"""
_a = 'masked_bert'
def __init__( self , UpperCamelCase_=30522 ... | 219 | 0 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
imp... | 99 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_ac... | 249 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inp... | 228 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ = None ) -> List[str]:
lowerCAmelCase__ = value
... | 228 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
a =pytest.mark.integration
@pytest.mark.parametrize('path' , ['paws', 'csv'] )
d... | 73 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, loggi... | 126 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise Optional... | 29 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoRea... | 29 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pip... | 181 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( A_ : Tuple, A_ : int, A_ : Dict ):
... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
create_state_space_tree(UpperCamelCase , [] , 0 )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , ... | 184 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''vocab_file''': '''vocab.... | 184 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ ):
if not numbers:
return 0
if not isinstance(UpperCamelCase_ , (list, tuple) ) or not all(
isinstance(UpperCamelCase_ , UpperCamelCase_ ) for number in numbers ):
raise ValueError("""numbers must be an... | 100 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ ):
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError("""'str' obj... | 100 | 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_availab... | 341 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__A = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Met... | 341 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onn... | 35 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
snake_case__ : List[str]... | 35 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :List[str] = logging.get_logger(__name__)
_lowerCAmelCase :Optional[int] = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.js... | 357 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 / sqrt(2 ) ):
_UpperCAmelCase : str = tau * fr... | 68 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 53 | import random
from typing import Any
def snake_case ( snake_case__ :list) -> list[Any]:
for _ in range(len(snake_case__)):
_A = random.randint(0 , len(snake_case__) - 1)
_A = random.randint(0 , len(snake_case__) - 1)
... | 180 | 0 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = ... | 349 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Any = {
"""configuration_roberta""": ["... | 347 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_lowercase , int(b / 2 ) ) * actual_power(_lowercase , int(b / 2 ) )
else:
retur... | 265 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"c... | 363 | """simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_m... | 149 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCamelCase__ = 5_0_0_0_0
UpperCamelCase__ = 5_0_0_0
UpperCamelCase__ , UpperCamelCase__ = os.path.split(__file__)
... | 181 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
clas... | 181 | 1 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> str:
_lowercase : list[list[str]] = [[] for _ in range(lowerCamelCase_ )]
_lowercase : str = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key... | 360 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE : str = logging.getLogger()
def ... | 84 | 0 |
def _A ( _lowercase ) -> list[list[float]]:
"""simple docstring"""
__UpperCamelCase = []
for data in source_data:
for i, el in enumerate(_lowercase ):
if len(_lowercase ) < i + 1:
data_lists.ap... | 310 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 336 | 0 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A_ : Optional[int] =TypeVar("""T""")
class __a ( Generic[T] ):
SCREAMING_SNAKE_CASE__ : deque[T] # Cache store of keys
SC... | 80 |
"""simple docstring"""
from math import factorial, pi
def SCREAMING_SNAKE_CASE_ ( snake_case : float , snake_case : int = 30 )-> float:
if not isinstance(snake_case , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for... | 80 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self , *_Upp... | 29 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSch... | 29 | 1 |
import unittest
from transformers import SqueezeBertConfig, 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 ModelTesterMixin, ids_tenso... | 359 | import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_snake_case = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', ... | 342 | 0 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
class A__ ( lowerCamelCase_ ):
def __init__( self , *A_ , **A_ ):
... | 52 |
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... | 334 | 0 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
fro... | 359 |
"""simple docstring"""
import numpy as np
def a__ ( __SCREAMING_SNAKE_CASE ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 108 | 0 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
'''simple docstring'''
def __init__( self : List[str], _lowerCamelCase : str, _lowerCamelCase : str ):
'''simple docstring'''
__A , __A = text, pattern
__A , ... | 266 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
if not postfix_notation:
return 0
__A = {'''+''', '''-''', '''*''', '''/'''}
__A = []
for token in postfix_n... | 266 | 1 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate... | 129 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : Union[str, Any] =logging.get_logger(__name__)
_A : List[Any] ={
'''SenseTime/deformabl... | 129 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ( _low... | 256 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 265 | 0 |
"""simple docstring"""
from math import sqrt
def _lowerCamelCase( a = 1_0_0_0_0_0_0 ):
__a = 0
__a = 0
__a = 4_2
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_s... | 354 | """simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE__:Any = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
... | 268 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "_... | 7 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( UpperCamelCase = "laptop" ):
A = F"https://www.amazon.in/laptop/s?k={product}"
A = {
"User-Agent": "Mozilla/5.0 (X11; Li... | 292 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _UpperCAmelCase ( _UpperCamelCase : Any, _UpperCamelCase : int=(), _Upp... | 18 | '''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : float, _UpperCamelCase : list[float] ) -> float:
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows ... | 18 | 1 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : list[int] ):
A__ = len(_lowerCamelCase )
for i in range(_lowerCamelCase ):
for j in range(i + 1 , _lowerCamelCase ):
if numbers[j] < numbers[i]:
... | 237 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase ( Uppe... | 237 | 1 |
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import Base... | 351 |
import random
from typing import Any
def _A ( SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
for _ in range(len(SCREAMING_SNAKE_CASE ) ):
a__ : Tuple =random.randint(0 , len(SCREAMING_SNAKE_CASE ) - 1 )
a__ : Any =ra... | 148 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 72 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""roberta-base""": """https://huggingface.co/roberta-base/r... | 179 | 0 |
def lowerCAmelCase_ ( _snake_case : int ) -> int:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : List[str] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_snake_case )
i... | 369 |
def lowerCAmelCase_ ( _snake_case : int ) -> bool:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : Union[str, Any] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_snake_case )
... | 41 | 0 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logg... | 212 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class A__ :
def __init__( self : Dict , a : str ):
'''simple docstring'''
lowerCAmelCase__ : Optio... | 212 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL... | 368 |
import os
def __lowerCAmelCase ()-> List[Any]:
"""simple docstring"""
snake_case_ = os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , '''num.txt''' )
with open(SCREAMING_SNAKE_CASE ) as file_hand:
return str(sum(int(SCREAMING_SNAKE_CASE ) for line in file_hand ) )[... | 267 | 0 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class a :
"""simple docstring"""
def __init__( self , l... | 180 | import argparse
import gc
import json
import os
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 import A... | 180 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datase... | 258 | import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
... | 258 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SC... | 24 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import T... | 108 | 0 |
def a_ ( __snake_case : str ) -> int:
"""simple docstring"""
lowerCamelCase_ =hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
lowerCamelCase_ =hex_num[0] == "-"
... | 350 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
a_ : List[Any] = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={ar... | 6 | 0 |
import math
import os
import sys
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : List[Any] = ''''''
try:
with open(_a , '''rb''' ) as binary_file:
lowerCAmelCase__ : Optional[int] ... | 131 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 131 | 1 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
SCREAMING_SNAKE_CASE = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = "\\n@InProceedings{moosavi201... | 230 |
"""simple docstring"""
# Copyright 2022 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
#... | 230 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 58 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV... | 268 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase: Optional[int] = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_AR... | 367 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config ... | 336 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : List[Any] = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0... | 18 | from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_ba... | 18 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
A_ : Any = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false po... | 365 |
import argparse
from collections import defaultdict
def UpperCamelCase (lowercase_: List[str] , lowercase_: Optional[int] , lowercase_: Optional[Any] , lowercase_: Union[str, Any] , lowercase_: Any ) -> int:
A__ : Optional[Any] = f"""{file}_{class_name}... | 141 | 0 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigT... | 120 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super... | 337 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
# Checks if the entire collection has been sorted
if len(lowerCamelCase__ ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase__ , n - 1... | 113 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import ... | 113 | 1 |
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 import Acce... | 322 |
'''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... | 41 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.u... | 362 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_snake_case = logging.get_logger(__name__)
_snake_case = {"voc... | 343 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case_ ( __SCREAMING_SNAKE_CASE : bytes , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
... | 93 |
'''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_M... | 267 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : str = get_activat... | 362 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_A = """__DUMMY_TRANSFORMERS_USER__"""
_A = """Dummy User"""
_A = """hf_hZEmnoOEYISjraJtbySaKC... | 166 | 0 |
'''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
... | 28 | """simple docstring"""
from __future__ import annotations
from cmath import sqrt
def lowerCAmelCase__ ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int ) -> tuple[complex, complex]:
"""simple docstring"""
if a ==... | 150 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase = False ):
'''simple docstring'''
if not arr:
return 0
UpperCAmelCase = 0 if allow_empty_subarrays else float("""-inf""" ... | 248 |
"""simple docstring"""
def _lowerCAmelCase ( ):
'''simple docstring'''
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if ... | 248 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( ) -> int:
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(UpperCamelCase__ , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f'{solution(... | 67 |
def __lowerCAmelCase ( a__ ) -> str:
__a = []
__a = set({'''(''', '''[''', '''{'''} )
__a = set({''')''', ''']''', '''}'''} )
__a = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(a__ ) ):
if s[i]... | 6 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : List[Any] = {
'''configuration_funnel''': ['''FUNNEL_P... | 354 | '''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ ):
'''simp... | 274 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
lowercase_ = 0
lowercase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0,... | 45 |
'''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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosit... | 311 | 0 |
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__ ( __snake_case ):
_UpperCAmelCase... | 362 |
def A_ ( _lowerCAmelCase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCamelCase : Union[str, Any] = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Union[str, Any] = input_str.replace(" " , "" )
... | 140 | 0 |
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''', ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''', ['''filename.csv''', '''filename with blan... | 72 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificati... | 303 | 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, Patc... | 359 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE = {
"""configuration_electra""": ["""ELECTRA_PRETR... | 256 | 0 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case ( _snake_c... | 60 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDi... | 79 | 0 |
def lowerCamelCase_ ( ):
lowerCamelCase_ = [3_1, 2_8, 3_1, 3_0, 3_1, 3_0, 3_1, 3_1, 3_0, 3_1, 3_0, 3_1]
lowerCamelCase_ = 6
lowerCamelCase_ = 1
lowerCamelCase_ = 1_9_0_1
lowerCamelCase_ = 0
while year < 2_0_0_1:
day += 7
... | 357 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__A ={
'''iou_prediction_head.layers.0''': '''io... | 47 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFM... | 113 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__UpperCamelCase = HfApi()
__UpperCamelCase = {}
# fmt: off
__UpperCamelCase = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6... | 113 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_: Tuple ={'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
... | 353 | '''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
UpperCAmelCase_ = {
"repo_name": ["test_repo1"... | 106 | 0 |
from __future__ import annotations
import pandas as pd
def a__ ( A_, A_, A_ ):
'''simple docstring'''
__magic_name__ = [0] * no_of_processes
__magic_name__ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
fo... | 88 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Any = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
... | 88 | 1 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.pa... | 60 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""BridgeTower/bridgetow... | 60 | 1 |
'''simple docstring'''
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_commo... | 53 |
'''simple docstring'''
def a ( __a ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ :Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) =... | 97 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , *_a , **_a ):
... | 353 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from d... | 11 | 0 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class A ( yaml.SafeLoader ):
def A__ ( self , lowerCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ ... | 164 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A = logging.get_logger(__name__)
__A = {
"facebook/convnextv2-tiny-1k-224": "https://... | 164 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""image_processor""", """tokeni... | 359 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAn... | 249 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import ... | 84 |
"""simple docstring"""
from __future__ import annotations
import math
def A ( snake_case__ ):
'''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 ... | 165 | 0 |
from __future__ import annotations
from typing import Any
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ = 0 ) -> None:
'''simple docstring'''
snake_case_ , snake_case_ : Any =... | 353 |
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
lowerCAmelCase_ = logging.getLogger(__name__)
... | 279 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_snake_case : Any = input("Enter image url: ").strip()
print(f'''Downloading image from {url} ...''')
_snake_case : Any = BeautifulSoup(requests.get(url).content, "html.parser")... | 123 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : int = logging.get_logger(__name__)
_snake_case : Union[str, Any] = "▁"
_snake_ca... | 123 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVeca... | 172 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
UpperCAmelCase = logging.get_logger(__name__)
def lowerCamelCase (a_ :str , a_ :Optional[... | 172 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_: Optional[Any] =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: List[Any]... | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
cla... | 142 | 0 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophe... | 357 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 17 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE:
def __init__( self ) -> None:
"""simple docstring"""
__SCREAMING... | 191 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _lowerCAmelCase ( _UpperCamelCase : np.ndarray , _UpperCamelCase : int , _UpperCamelCase : int ) -> np.ndarray:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =n... | 47 | 0 |
from sklearn.metrics import recall_score
import datasets
UpperCAmelCase_ : Optional[int] = '\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN i... | 120 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
@require_torch
def SCREAMING_SNAKE_CASE ( self : Optional[... | 120 | 1 |
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