code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
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 import Iterabl... | 54 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE... | 118 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
snake_case : Tuple = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case : ... | 638 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 118 | 0 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default... | 373 |
"""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 __a ( __a ):
''... | 118 | 0 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase__ ( __a ):
lowercase__ :... | 337 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase = {
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''M... | 118 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_lowercase : Any =logging.get_logger(__name__)
... | 364 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils impo... | 118 | 0 |
__UpperCAmelCase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'... | 40 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_lowercase = 2_048
_lowercase = 4_096
_lowercase = 42
_lowercase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
_lowercase = {'''null''': 0, '''short''... | 118 | 0 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCAmelCase_ ( ):
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import rename as original_renam... | 329 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowercase = get_logger(__name__)
class __a ( enum.Enum ):
'''simple docstring'''
... | 118 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tra... | 506 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
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_configurati... | 118 | 0 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lower... | 423 |
"""simple docstring"""
_lowercase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com... | 118 | 0 |
"""simple docstring"""
import math
def UpperCAmelCase__ ( lowerCAmelCase__ :Union[str, Any] ) -> bool:
'''simple docstring'''
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and (
number >= 0
), "'number' must been an ... | 359 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ = 1_0 ) ->str:
if not isinstance(__magic_name__ , __magic_name__ ) or n < 0:
raise ValueError("Invalid input" )
__lowercase = 1_0**n
__lowercase = 2_8_4_3_3 * (pow(2 ,... | 118 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ : Dict = get_logger(__name__)
class __lowerCAmelCase ( enum.Enum ):
_UpperCamelCase : Tuple = """all_... | 112 |
"""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.d... | 118 | 0 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : str =logging.get_logger(__name__)
def a__ ( lowercase__ , ... | 54 |
"""simple docstring"""
from __future__ import annotations
from random import random
class __a :
'''simple docstring'''
def __init__( self , _lowerCamelCase = None ) -> str:
'''simple docstring'''
__lowe... | 118 | 0 |
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class snake_case__ :
"""simple docstring"""
lowerCamelCase = None
def lowerCAmelCase ( self :... | 638 |
"""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 ... | 118 | 0 |
"""simple docstring"""
import numpy as np
def lowercase__ ( lowerCAmelCase : Any ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def lowercase__ ( lowerCAmelCase : Any ) -> np.array... | 373 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_lowercase = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.... | 118 | 0 |
"""simple docstring"""
from __future__ import annotations
def __a ( A ) -> bool:
'''simple docstring'''
return len(set(A ) ) == len(A )
if __name__ == "__main__":
import doctest
doctest.testmod() | 337 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase = pd.read_csv('''sample_data.csv''', head... | 118 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Tuple =logging.get_logger(__name__)
_lowercase : str ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTo... | 364 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers i... | 118 | 0 |
from __future__ import annotations
from PIL import Image
# Define glider example
__UpperCAmelCase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, ... | 40 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''... | 118 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCAmelCase_ ( __A : List[str] , __A : Any , __A : str , ... | 329 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def lowerCAmelC... | 118 | 0 |
"""simple docstring"""
from math import pi
def lowerCamelCase_ (UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Dict ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 506 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_lowercase = logging.get_log... | 118 | 0 |
import requests
lowercase : List[Any] = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def UpperCAmelCase_ ( _UpperCAmelCase ):
# fetching a list of articles in json format
lowerCamelCase_: Any = requests.get(_NEWS_API + bbc_new... | 423 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ ) ->int:
if not isinstance(__magic_name__ , __magic_name__ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_pe... | 118 | 0 |
"""simple docstring"""
__lowerCAmelCase : int =9.8_0665
def UpperCAmelCase__ ( lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Optional[int] = g ) -> float:
'''simple docstring'''
if fluid_density <= 0:
... | 359 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCAmelCase__ ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_na... | 118 | 0 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokeni... | 112 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.p... | 118 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase : int ={"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfi... | 54 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE... | 118 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {"configuration_xlnet": ["XLNET... | 638 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 118 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowercase__ ( ) -> int:
"""simple docstring"""
UpperCAmelCase = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<... | 373 |
"""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 __a ( __a ):
''... | 118 | 0 |
"""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
class lowerCAmelCase... | 337 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase = {
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''M... | 118 | 0 |
from __future__ import annotations
from collections.abc import Callable
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ = 1_00 ,):
lowerCamelCase_ : Tuple = x_start
lowerCamelCase_ : Any =... | 364 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils impo... | 118 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
if n... | 40 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_lowercase = 2_048
_lowercase = 4_096
_lowercase = 42
_lowercase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
_lowercase = {'''null''': 0, '''short''... | 118 | 0 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = 0... | 329 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowercase = get_logger(__name__)
class __a ( enum.Enum ):
'''simple docstring'''
... | 118 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__a ) ,'''Tato... | 506 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
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_configurati... | 118 | 0 |
def UpperCAmelCase_ ( _UpperCAmelCase ):
if length <= 0 or not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(_UpperCAmelCase )]
if __name__ == "__main__":
prin... | 423 |
"""simple docstring"""
_lowercase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com... | 118 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__lowerCAmelCase : Union[str, Any] ={
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """... | 359 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ = 1_0 ) ->str:
if not isinstance(__magic_name__ , __magic_name__ ) or n < 0:
raise ValueError("Invalid input" )
__lowercase = 1_0**n
__lowercase = 2_8_4_3_3 * (pow(2 ,... | 118 | 0 |
from math import factorial
def _A ( lowerCamelCase = 20 ):
a__ : List[str] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
a__ : Union[str, Any] = n // 2
return int(factorial(lowerCamelCase ) / (factorial(lowerCamelCase ) ... | 112 |
"""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.d... | 118 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A ( __a ):
_snake_case ="""Wav2Vec2Feature... | 54 |
"""simple docstring"""
from __future__ import annotations
from random import random
class __a :
'''simple docstring'''
def __init__( self , _lowerCamelCase = None ) -> str:
'''simple docstring'''
__lowe... | 118 | 0 |
'''simple docstring'''
from __future__ import annotations
lowercase__ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ... | 638 |
"""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 ... | 118 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 373 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_lowercase = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.... | 118 | 0 |
"""simple docstring"""
import os
import platform
import sys
__UpperCAmelCase ="""3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("... | 337 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase = pd.read_csv('''sample_data.csv''', head... | 118 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase : Tuple =pd.read_csv("""sample_data.csv""", header=None)
_lowerca... | 364 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers i... | 118 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def UpperCamelCase ( snake_case__ : Dict , snake_case__ : List[Any] , snake_case__ : int , snake_case__ : List[str] ) -> str:
UpperCamelCase ... | 40 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''... | 118 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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 im... | 329 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def lowerCAmelC... | 118 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
a__ ... | 506 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_lowercase = logging.get_log... | 118 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common impo... | 423 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ ) ->int:
if not isinstance(__magic_name__ , __magic_name__ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_pe... | 118 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCAmelCase : Any ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
ex... | 359 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCAmelCase__ ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_na... | 118 | 0 |
from __future__ import annotations
from random import random
class __lowerCAmelCase :
def __init__( self , snake_case = None ) -> str:
"""simple docstring"""
a__ : List[Any] = value
a__ : Dict = random()
a__ : Op... | 112 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.p... | 118 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 ... | 54 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE... | 118 | 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
... | 638 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 118 | 0 |
"""simple docstring"""
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... | 373 |
"""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 __a ( __a ):
''... | 118 | 0 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__UpperCAmel... | 337 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase = {
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''M... | 118 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Dict ={
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
if... | 364 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils impo... | 118 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCAmelCase = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if not is_torch_available():
... | 40 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_lowercase = 2_048
_lowercase = 4_096
_lowercase = 42
_lowercase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
_lowercase = {'''null''': 0, '''short''... | 118 | 0 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__UpperCAmelCase = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge... | 329 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowercase = get_logger(__name__)
class __a ( enum.Enum ):
'''simple docstring'''
... | 118 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_p... | 506 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
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_configurati... | 118 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class a__ :
def __init__( self : Optional[int] , A_ : int , A_ : Dict , A_ : Optional[Any] , A_ : Tuple , A_ ... | 423 |
"""simple docstring"""
_lowercase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com... | 118 | 0 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datase... | 359 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ = 1_0 ) ->str:
if not isinstance(__magic_name__ , __magic_name__ ) or n < 0:
raise ValueError("Invalid input" )
__lowercase = 1_0**n
__lowercase = 2_8_4_3_3 * (pow(2 ,... | 118 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import Dat... | 112 |
"""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.d... | 118 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[Any] =logging.get_logger(__name__)
__lowercase : Optional[int] ={
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
... | 54 |
"""simple docstring"""
from __future__ import annotations
from random import random
class __a :
'''simple docstring'''
def __init__( self , _lowerCamelCase = None ) -> str:
'''simple docstring'''
__lowe... | 118 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/re... | 638 |
"""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 ... | 118 | 0 |
"""simple docstring"""
import argparse
import os
import re
SCREAMING_SNAKE_CASE_ = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE_ = ... | 373 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_lowercase = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.... | 118 | 0 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def __a ( A , A , A ) -> float:
'''simple docstring'''
A__ = x
A__ = y
for step in range(A ): # noqa: B007
A__ = a * a - b * b + x
A_... | 337 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase = pd.read_csv('''sample_data.csv''', head... | 118 | 0 |
import math
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
lowerCamelCase_ : int = 0
lowerCamelCase_ : List[Any] = 0
while num > 0:
lowerCamelCase_ : List[Any] = num % 8
lowerCamelCase_ : Union[str, Any] = octal ... | 364 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers i... | 118 | 0 |
'''simple docstring'''
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,
)
UpperCAmelCase = pytest.mark.integration
@pytest.mark.parametrize('path', ['p... | 119 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available... | 119 | 1 |
'''simple docstring'''
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_prope... | 119 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( lowercase__ : str = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowercase__ ):
__lowercase =[d for d in dir_names if d != 'scripts' a... | 119 | 1 |
'''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_inputs
if is_torch_avail... | 119 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def __UpperCamelCase ( lowercase__ : int = 1_50_00_00 ):
'''simple docstring'''
__lowercase =defaultdict(lowercase__ )
__lowercase =2
while 2 * euclid_m * (euclid_m + 1) <... | 119 | 1 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
__lowercase =int(lowercase__ )
if n_element < 1:
__lowercase =ValueError('a should be a positive number' )
raise my_error
__lowercase ... | 119 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __UpperCamelCase ( lowercase__ : Optional[Any] ):
'''simple docstring'''
for i in range(0, lowercase__ ):
for _ in range(0, n - i - 1 ): # printing spaces
p... | 119 | 1 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Met... | 119 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils imp... | 119 | 1 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : Any ):
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5... | 119 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A )
class lowerCAmelCase ( A ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSO... | 119 | 1 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(lowercase__, (list, tuple) ) or not all(
isinstance(lowercase__, lowercase__ ) for number in numbe... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
if n == 1 or not isinstance(lowercase__, lowercase__ ):
return 0
elif n == 2:
return 1
else:
__lowercase =[0, 1]
for i in r... | 119 | 1 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int, lowercase__ : int ):
'''simple docstring'''
return 1 if input_a == input_a else 0
def __UpperCamelCase ( ):
'''simple docstring'''
assert xnor_gate(0, 0 ) == 1
... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : List[str], lowercase__ : Tuple ):
'''simple docstring'''
__lowercase =[0 for i in range(r + 1 )]
# nc0 = 1
__lowercase =1
for i in range(1, n + 1 ):
# to comput... | 119 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 119 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase ( unittest.TestCase ):
... | 119 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def __UpperCamelCase ( lowercase__ : int = 1_50_00_00 ):
'''simple docstring'''
__lowercase =defaultdict(lowercase__ )
__lowercase =2
while 2 * euclid_m * (euclid_m + 1) <... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
__lowercase =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__... | 119 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''shi-labs/dinat-mini-in1k-224... | 119 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase = {
'''configuration_owlvit''': [
... | 119 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
... | 119 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
Up... | 119 | 1 |
'''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_modeling_t... | 119 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( lowercase__ : float, lowercase__ : float, lowercase__ : float ):
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and on... | 119 | 1 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCamelCase ( ):
'''simple docstring'''
with offline(Offline... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : list[int] ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
__lowercase =sum(lowercase__ ) / len(lowercase__ ) # Calculate ... | 119 | 1 |
'''simple docstring'''
import argparse
import json
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
fro... | 119 |
'''simple docstring'''
import string
def __UpperCamelCase ( lowercase__ : str ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
__lowercase =''
for symbol in message:
if symbol in string.ascii_uppercase:
... | 119 | 1 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __UpperCamelCase ( lowercase__ : Optional[Any] ):
'''simple docstring'''
for i in range(0, lowercase__ ):
for _ in range(0, n - i - 1 ): # printing spaces
p... | 119 |
'''simple docstring'''
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... | 119 | 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,
)
UpperCAmelCase = {
'''configuration_distilbert''': [
'''DISTILBERT... | 119 |
'''simple docstring'''
class lowerCAmelCase :
def __init__( self : List[Any] , __lowercase : str , __lowercase : Any , __lowercase : str ):
"""simple docstring"""
__lowercase =name
__lowercase ... | 119 | 1 |
'''simple docstring'''
import math
def __UpperCamelCase ( lowercase__ : list, lowercase__ : int = 0, lowercase__ : int = 0 ):
'''simple docstring'''
__lowercase =end or len(lowercase__ )
for i in range(lowercase__, lowercase__ ):
... | 119 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils... | 119 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowerCAmelCase ( unitte... | 119 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''shi-labs/dinat-mini-in1k-224... | 119 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolv... | 119 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 119 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_m... | 119 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmel... | 119 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase ( A ):
lowerCAmelCase_ = "ClapFeatureExtractor"
lowerCAmelCase_ = ("RobertaTokenizer", "RobertaTokenizerFast")
de... | 119 |
'''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... | 119 | 1 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
if n == 1 or not isinstance(lowercase__, lowercase__ ):
return 0
elif n == 2:
return 1
else:
__lowercase =[0, 1]
for i in r... | 119 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available... | 119 | 1 |
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
UpperCAmelCase = re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')
@total_ordering
@dataclass
cla... | 119 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( lowercase__ : str = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowercase__ ):
__lowercase =[d for d in dir_names if d != 'scripts' a... | 119 | 1 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
UpperCAmelCase = '''path-to-your-trained-model'''
UpperCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
UpperCAmelCase = '''A photo of sks dog in a bucket'''
Upp... | 119 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def __UpperCamelCase ( lowercase__ : int = 1_50_00_00 ):
'''simple docstring'''
__lowercase =defaultdict(lowercase__ )
__lowercase =2
while 2 * euclid_m * (euclid_m + 1) <... | 119 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def __UpperCamelCase ( ):
'''simple docstring'''
__lowercase ={}
__lowercase =2
while True:
__lowercase =factor_map.pop(lowercase__, lower... | 119 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __UpperCamelCase ( lowercase__ : Optional[Any] ):
'''simple docstring'''
for i in range(0, lowercase__ ):
for _ in range(0, n - i - 1 ): # printing spaces
p... | 119 | 1 |
'''simple docstring'''
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 d... | 119 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils imp... | 119 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
... | 119 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A )
class lowerCAmelCase ( A ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSO... | 119 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_t... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
if n == 1 or not isinstance(lowercase__, lowercase__ ):
return 0
elif n == 2:
return 1
else:
__lowercase =[0, 1]
for i in r... | 119 | 1 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : List[str], lowercase__ : Tuple ):
'''simple docstring'''
__lowercase =[0 for i in range(r + 1 )]
# nc0 = 1
__lowercase =1
for i in range(1, n + 1 ):
# to comput... | 119 | 1 |
'''simple docstring'''
def __UpperCamelCase ( ):
'''simple docstring'''
return [list(range(10_00 - i, -10_00 - i, -1 ) ) for i in range(10_00 )]
UpperCAmelCase = generate_large_matrix()
UpperCAmelCase = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2... | 119 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase ( unittest.TestCase ):
... | 119 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowerCAmelCase ( A ):
def snake_case ( self : List[Any] ):
"""simple docstring"""
return [
... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
__lowercase =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__... | 119 | 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,
AutoModelForPreTraining,
AutoModel... | 119 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase = {
'''configuration_owlvit''': [
... | 119 | 1 |
'''simple docstring'''
from math import ceil
def __UpperCamelCase ( lowercase__ : int = 10_01 ):
'''simple docstring'''
__lowercase =1
for i in range(1, int(ceil(n / 2.0 ) ) ):
__lowercase =2 * i + 1
__lowercase =2 * i... | 119 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
Up... | 119 | 1 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 119 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( lowercase__ : float, lowercase__ : float, lowercase__ : float ):
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and on... | 119 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : list[int] ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
__lowercase =sum(lowercase__ ) / len(lowercase__ ) # Calculate ... | 119 | 1 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
if not isinstance(lowercase__, lowercase__ ):
raise TypeError('only integers accepted as input' )
else:
__lowercase =str(abs(lowercase__ ) )
... | 119 |
'''simple docstring'''
import string
def __UpperCamelCase ( lowercase__ : str ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
__lowercase =''
for symbol in message:
if symbol in string.ascii_uppercase:
... | 119 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from util... | 119 |
'''simple docstring'''
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... | 119 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, ... | 119 |
'''simple docstring'''
class lowerCAmelCase :
def __init__( self : List[Any] , __lowercase : str , __lowercase : Any , __lowercase : str ):
"""simple docstring"""
__lowercase =name
__lowercase ... | 119 | 1 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : Optional[Any], lowercase__ : Union[str, Any] ):
'''simple docstring'''
__lowercase =''
for i in table:
res += inp[i - 1]
return res
def __UpperCamelCase ( lowercase__ ... | 119 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils... | 119 | 1 |
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