



'''simple docstring'''




from ..utils import DummyObject, requires_backends






class 						_UpperCamelCase  (							metaclass=lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Optional[Any]         =		['''tensorflow_text''']

			def __init__(							self					:			Optional[Any]							,					*lowerCAmelCase__					:			str							,					**lowerCAmelCase__					:			int						):





					"""simple docstring"""






					requires_backends(self							,					["""tensorflow_text"""]						)




'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		['''image_processor''', '''tokenizer''']
			_A				:		Union[str, Any]         =		'''AutoImageProcessor'''
			_A				:		Optional[Any]         =		'''AutoTokenizer'''

			def __init__(							self					:			Tuple							,					lowerCAmelCase__					:			Tuple=None							,					lowerCAmelCase__					:			Union[str, Any]=None							,					**lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""							,					lowerCAmelCase__							,					)
							__SCREAMING_SNAKE_CASE	:		str   	=		kwargs.pop("""feature_extractor"""						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		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__(lowerCAmelCase__							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		Dict   	=		False

			def __call__(							self					:			List[str]							,					*lowerCAmelCase__					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor(*lowerCAmelCase__							,					**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		kwargs.pop("""images"""							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		kwargs.pop("""text"""							,					lowerCAmelCase__						)
					if len(lowerCAmelCase__						) > 0:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		args[0]
							__SCREAMING_SNAKE_CASE	:		int   	=		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:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor(lowerCAmelCase__							,					*lowerCAmelCase__							,					**lowerCAmelCase__						)
					if text is not None:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.tokenizer(lowerCAmelCase__							,					**lowerCAmelCase__						)

					if text is None:
							return inputs
					elif images is None:
							return encodings
					else:
							__SCREAMING_SNAKE_CASE	:		int   	=		encodings["""input_ids"""]
							return inputs

			def UpperCamelCase__ (							self					:			Any							,					*lowerCAmelCase__					:			Any							,					**lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					return self.tokenizer.batch_decode(*lowerCAmelCase__							,					**lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int							,					*lowerCAmelCase__					:			str							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					return self.tokenizer.decode(*lowerCAmelCase__							,					**lowerCAmelCase__						)

			@contextmanager
			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					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."""						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		True
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.tokenizer
					yield
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		False

			@property
			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,					lowerCAmelCase__							,					)
					return self.image_processor_class



			@property
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,					lowerCAmelCase__							,					)
					return self.image_processor