




'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		["image_processor", "tokenizer"]
					_UpperCamelCase  	=		"AutoImageProcessor"
					_UpperCamelCase  	=		"AutoTokenizer"



					def __init__(  self    :	Optional[Any]			,					_lowercase    :	Optional[int]=None			,					_lowercase    :	Any=None			,					**_lowercase    :	Optional[int])						->       int:
								A_       		=   None
								if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'			,					UpperCAmelCase_			,					)
											A_       		=   kwargs.pop('feature_extractor')

								A_       		=   image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('You need to specify an `image_processor`.')
								if tokenizer is None:
											raise ValueError('You need to specify a `tokenizer`.')

								super().__init__(UpperCAmelCase_			,					UpperCAmelCase_)
								A_       		=   self.image_processor
								A_       		=   False



					def __call__(  self    :	List[str]			,					*_lowercase    :	Tuple			,					**_lowercase    :	List[str])						->       Optional[int]:
								# For backward compatibility
								if self._in_target_context_manager:
											return self.current_processor(*UpperCAmelCase_			,					**UpperCAmelCase_)

								A_       		=   kwargs.pop('images'			,					UpperCAmelCase_)
								A_       		=   kwargs.pop('text'			,					UpperCAmelCase_)
								if len(UpperCAmelCase_) > 0:
											A_       		=   args[0]
											A_       		=   args[1:]

								if images is None and text is None:
											raise ValueError('You need to specify either an `images` or `text` input to process.')

								if images is not None:
											A_       		=   self.image_processor(UpperCAmelCase_			,					*UpperCAmelCase_			,					**UpperCAmelCase_)
								if text is not None:
											A_       		=   self.tokenizer(UpperCAmelCase_			,					**UpperCAmelCase_)

								if text is None:
											return inputs
								elif images is None:
											return encodings
								else:
											A_       		=   encodings['input_ids']
											return inputs



					def 						__snake_case	(  self    :	List[str]			,					*_lowercase    :	Any			,					**_lowercase    :	str)						->       Optional[int]:
								return self.tokenizer.batch_decode(*UpperCAmelCase_			,					**UpperCAmelCase_)



					def 						__snake_case	(  self    :	Dict			,					*_lowercase    :	Optional[Any]			,					**_lowercase    :	Optional[int])						->       Optional[Any]:
								return self.tokenizer.decode(*UpperCAmelCase_			,					**UpperCAmelCase_)



					@contextmanager
					def 						__snake_case	(  self    :	Tuple)						->       Any:
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your images inputs, or in a separate call.')
								A_       		=   True
								A_       		=   self.tokenizer
								yield
								A_       		=   self.image_processor
								A_       		=   False



					@property
					def 						__snake_case	(  self    :	Optional[int])						->       Any:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,					UpperCAmelCase_			,					)
								return self.image_processor_class



					@property
					def 						__snake_case	(  self    :	Optional[int])						->       Dict:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,					UpperCAmelCase_			,					)
								return self.image_processor










'''simple docstring'''
from unittest.mock import patch

import datasets
from datasets import Dataset





def  lowerCamelCase(       )		->		Any:
			A_       		=   Dataset.from_dict({'col_1': [3, 2, 0, 1]}    )

			with patch('tqdm.auto.tqdm'    ) as mock_tqdm:
						datasets.disable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_not_called()

						mock_tqdm.reset_mock()

						datasets.enable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_called()




