

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class    __UpperCAmelCase	(			__snake_case		):
   UpperCamelCase  	=	["""image_processor""", """tokenizer"""]
   UpperCamelCase  	=	"""AutoImageProcessor"""
   UpperCamelCase  	=	"""AutoTokenizer"""



   def __init__(				self				:	List[Any],						__A				:	int=None,						__A				:	Optional[int]=None,						**__A				:	str		):
     UpperCAmelCase			:				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.''',						__A,						)
       UpperCAmelCase			:				str										=					kwargs.pop('''feature_extractor'''		)

     UpperCAmelCase			:				Optional[int]										=					image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
       raise ValueError('''You have to specify an image_processor.'''		)
     if tokenizer is None:
       raise ValueError('''You have to specify a tokenizer.'''		)

     super().__init__(__A,						__A		)
     UpperCAmelCase			:				Optional[int]										=					self.image_processor



   def __call__(				self				:	Dict,						__A				:	Optional[Any]=None,						__A				:	str=None,						__A				:	int=None,						**__A				:	List[str]		):

     if text is None and images is None:
       raise ValueError('''You have to specify either text or images. Both cannot be none.'''		)

     if text is not None:
       UpperCAmelCase			:				List[Any]										=					self.tokenizer(__A,						return_tensors=__A,						**__A		)

     if images is not None:
       UpperCAmelCase			:				List[Any]										=					self.image_processor(__A,						return_tensors=__A,						**__A		)

     if text is not None and images is not None:
       UpperCAmelCase			:				Dict										=					image_features.pixel_values
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**__A		),						tensor_type=__A		)



   def 			__magic_name__       (				self				:	str,						*__A				:	Optional[Any],						**__A				:	int		):
     return self.tokenizer.batch_decode(*__A,						**__A		)



   def 			__magic_name__       (				self				:	List[Any],						*__A				:	List[Any],						**__A				:	List[str]		):
     return self.tokenizer.decode(*__A,						**__A		)



   @property
   def 			__magic_name__       (				self				:	List[str]		):
     UpperCAmelCase			:				Tuple										=					self.tokenizer.model_input_names
     UpperCAmelCase			:				List[Any]										=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



   @property
   def 			__magic_name__       (				self				:	str		):
     warnings.warn(
         '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',						__A,						)
     return self.image_processor_class





   @property
   def 			__magic_name__       (				self				:	List[Any]		):
     warnings.warn(
         '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',						__A,						)
     return self.image_processor













"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterator
from typing import Any







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Optional[int]					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	data
												a__ 							=	None





							def __str__( self:  str     ):

												'''simple docstring'''

												return F'{self.data}'







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  int     ):

												'''simple docstring'''

												a__ 							=	None
												a__ 							=	None




							def __iter__( self:  Tuple     ):

												'''simple docstring'''

												a__ 							=	self.front
												while node:
																	yield node.data
																	a__ 							=	node.next




							def __len__( self:  Union[str, Any]     ):

												'''simple docstring'''

												return len(tuple(iter(self     )     )     )




							def __str__( self:  Any     ):

												'''simple docstring'''

												return " <- ".join(str(__A     ) for item in self     )




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												return len(self     ) == 0




							def 			lowercase		( self:  Tuple					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	Node(__A     )
												if self.is_empty():
																	a__ 							=	a__ 							=	node
												else:
																	assert isinstance(self.rear					,		__A     )
																	a__ 							=	node
																	a__ 							=	node




							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												if self.is_empty():
																	raise IndexError('''dequeue from empty queue'''     )
												assert isinstance(self.front					,		__A     )
												a__ 							=	self.front
												a__ 							=	self.front.next
												if self.front is None:
																	a__ 							=	None
												return node.data





							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												a__ 							=	a__ 							=	None


if __name__ == "__main__":
	from doctest import testmod

	testmod()


