
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




class 					_SCREAMING_SNAKE_CASE  (      snake_case__ ):
      _A						:		List[str]    	=     ["""image_processor""", """tokenizer"""]
      _A						:		List[Any]    	=     """BlipImageProcessor"""
      _A						:		Union[str, Any]    	=     ("""BertTokenizer""", """BertTokenizerFast""")
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				False
          super().__init__(UpperCAmelCase_		,	UpperCAmelCase_ )
          snake_case__						=				self.image_processor
      def __call__(			self		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = True		,	lowerCamelCase = False		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = 0		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = False		,	lowerCamelCase = True		,	lowerCamelCase = None		,	**lowerCamelCase		,	):



          if images is None and text is None:
              raise ValueError("You have to specify either images or text." )

          # Get only text
          if images is None:
              snake_case__						=				self.tokenizer
              snake_case__						=				self.tokenizer(
                  text=UpperCAmelCase_		,	add_special_tokens=UpperCAmelCase_		,	padding=UpperCAmelCase_		,	truncation=UpperCAmelCase_		,	max_length=UpperCAmelCase_		,	stride=UpperCAmelCase_		,	pad_to_multiple_of=UpperCAmelCase_		,	return_attention_mask=UpperCAmelCase_		,	return_overflowing_tokens=UpperCAmelCase_		,	return_special_tokens_mask=UpperCAmelCase_		,	return_offsets_mapping=UpperCAmelCase_		,	return_token_type_ids=UpperCAmelCase_		,	return_length=UpperCAmelCase_		,	verbose=UpperCAmelCase_		,	return_tensors=UpperCAmelCase_		,	**UpperCAmelCase_		,	)
              return text_encoding

          # add pixel_values
          snake_case__						=				self.image_processor(UpperCAmelCase_		,	return_tensors=UpperCAmelCase_ )

          if text is not None:
              snake_case__						=				self.tokenizer(
                  text=UpperCAmelCase_		,	add_special_tokens=UpperCAmelCase_		,	padding=UpperCAmelCase_		,	truncation=UpperCAmelCase_		,	max_length=UpperCAmelCase_		,	stride=UpperCAmelCase_		,	pad_to_multiple_of=UpperCAmelCase_		,	return_attention_mask=UpperCAmelCase_		,	return_overflowing_tokens=UpperCAmelCase_		,	return_special_tokens_mask=UpperCAmelCase_		,	return_offsets_mapping=UpperCAmelCase_		,	return_token_type_ids=UpperCAmelCase_		,	return_length=UpperCAmelCase_		,	verbose=UpperCAmelCase_		,	return_tensors=UpperCAmelCase_		,	**UpperCAmelCase_		,	)
          else:
              snake_case__						=				None

          if text_encoding is not None:
              encoding_image_processor.update(UpperCAmelCase_ )

          return encoding_image_processor
      def A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
          return self.tokenizer.batch_decode(*UpperCAmelCase_		,	**UpperCAmelCase_ )
      def A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
          return self.tokenizer.decode(*UpperCAmelCase_		,	**UpperCAmelCase_ )



      @property
      def A_			(			self ):
          snake_case__						=				self.tokenizer.model_input_names
          snake_case__						=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )


from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						,					snake_case__						=				set(__lowerCAmelCase							), [start]

    while stack:
        snake_case__						=				stack.pop()
        explored.add(__lowerCAmelCase							)
        # Differences from BFS:
        # 1) pop last element instead of first one
        # 2) add adjacent elements to stack without exploring them
        for adj in reversed(graph[v]							):
            if adj not in explored:
                stack.append(__lowerCAmelCase							)
    return explored


__magic_name__            =  {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, '''A'''))



