

'''simple docstring'''





import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def 							_lowerCAmelCase (      __magic_name__	:    Optional[int]      ,   __magic_name__	:    List[Any]	)							->     Optional[Any]:
						assert isinstance(__magic_name__      ,   __magic_name__	)
						assert dataset.num_rows == 4
						assert dataset.num_columns == 1
						assert dataset.column_names == ["text"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('''keep_in_memory'''      ,   [False, True]	)
def 							_lowerCAmelCase (      __magic_name__	:    Dict      ,   __magic_name__	:    str      ,   __magic_name__	:    Dict	)							->     Union[str, Any]:
						lowercase   :       str								=tmp_path / '''cache'''
						lowercase   :       int								={'''text''': '''string'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowercase   :       List[Any]								=TextDatasetReader(__magic_name__      ,   cache_dir=__magic_name__      ,   keep_in_memory=__magic_name__	).read()
						_check_text_dataset(__magic_name__      ,   __magic_name__	)






@pytest.mark.parametrize(
    '''features'''      ,   [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]      ,   )
def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    Optional[int]	)							->     str:
						lowercase   :       Union[str, Any]								=tmp_path / '''cache'''
						lowercase   :       str								={'''text''': '''string'''}
						lowercase   :       int								=features.copy() if features else default_expected_features
						lowercase   :       Tuple								=(
						    Features({feature: Value(__magic_name__	) for feature, dtype in features.items()}	) if features is not None else None
						)
						lowercase   :       List[Any]								=TextDatasetReader(__magic_name__      ,   features=__magic_name__      ,   cache_dir=__magic_name__	).read()
						_check_text_dataset(__magic_name__      ,   __magic_name__	)






@pytest.mark.parametrize('''split'''      ,   [None, NamedSplit('''train'''	), '''train''', '''test''']	)
def 							_lowerCAmelCase (      __magic_name__	:    str      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    Union[str, Any]	)							->     Any:
						lowercase   :       Tuple								=tmp_path / '''cache'''
						lowercase   :       Any								={'''text''': '''string'''}
						lowercase   :       List[str]								=TextDatasetReader(__magic_name__      ,   cache_dir=__magic_name__      ,   split=__magic_name__	).read()
						_check_text_dataset(__magic_name__      ,   __magic_name__	)
						assert dataset.split == split if split else "train"






@pytest.mark.parametrize('''path_type'''      ,   [str, list]	)
def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    int	)							->     Union[str, Any]:
						if issubclass(__magic_name__      ,   __magic_name__	):
												lowercase   :       Tuple								=text_path
						elif issubclass(__magic_name__      ,   __magic_name__	):
												lowercase   :       List[Any]								=[text_path]
						lowercase   :       Tuple								=tmp_path / '''cache'''
						lowercase   :       Optional[int]								={'''text''': '''string'''}
						lowercase   :       List[str]								=TextDatasetReader(__magic_name__      ,   cache_dir=__magic_name__	).read()
						_check_text_dataset(__magic_name__      ,   __magic_name__	)






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    str      ,   __magic_name__	:    Dict=("train",)	)							->     Tuple:
						assert isinstance(__magic_name__      ,   __magic_name__	)
						for split in splits:
												lowercase   :       Tuple								=dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 1
												assert dataset.column_names == ["text"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('''keep_in_memory'''      ,   [False, True]	)
def 							_lowerCAmelCase (      __magic_name__	:    Dict      ,   __magic_name__	:    Dict      ,   __magic_name__	:    str	)							->     int:
						lowercase   :       Tuple								=tmp_path / '''cache'''
						lowercase   :       str								={'''text''': '''string'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowercase   :       Optional[Any]								=TextDatasetReader({'''train''': text_path}      ,   cache_dir=__magic_name__      ,   keep_in_memory=__magic_name__	).read()
						_check_text_datasetdict(__magic_name__      ,   __magic_name__	)






@pytest.mark.parametrize(
    '''features'''      ,   [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]      ,   )
def 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    Dict      ,   __magic_name__	:    Any	)							->     Dict:
						lowercase   :       Optional[int]								=tmp_path / '''cache'''
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						lowercase   :       List[Any]								={'''text''': '''string'''}
						lowercase   :       List[Any]								=features.copy() if features else default_expected_features
						lowercase   :       Union[str, Any]								=(
						    Features({feature: Value(__magic_name__	) for feature, dtype in features.items()}	) if features is not None else None
						)
						lowercase   :       Dict								=TextDatasetReader({'''train''': text_path}      ,   features=__magic_name__      ,   cache_dir=__magic_name__	).read()
						_check_text_datasetdict(__magic_name__      ,   __magic_name__	)







@pytest.mark.parametrize('''split'''      ,   [None, NamedSplit('''train'''	), '''train''', '''test''']	)
def 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    Tuple	)							->     List[Any]:
						if split:
												lowercase   :       List[Any]								={split: text_path}
						else:
												lowercase   :       Optional[int]								='''train'''
												lowercase   :       str								={'''train''': text_path, '''test''': text_path}
						lowercase   :       Dict								=tmp_path / '''cache'''
						lowercase   :       Tuple								={'''text''': '''string'''}
						lowercase   :       Optional[int]								=TextDatasetReader(__magic_name__      ,   cache_dir=__magic_name__	).read()
						_check_text_datasetdict(__magic_name__      ,   __magic_name__      ,   splits=list(path.keys()	)	)
						assert all(dataset[split].split == split for split in path.keys()	)








'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


UpperCamelCase_							=					logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Union[str, Any]		,				*UpperCAmelCase__    :		Dict		,				**UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										warnings.warn(
										    '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
										    ''' use CLIPImageProcessor instead.'''		,				UpperCAmelCase__		,				)
										super().__init__(*UpperCAmelCase__		,				**UpperCAmelCase__      )





