


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Dict       =		['''image_processor''', '''tokenizer''']
      snake_case__       :  List[Any]       =		'''AutoImageProcessor'''
      snake_case__       :  Union[str, Any]       =		'''AutoTokenizer'''
      def __init__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=None  ,							SCREAMING_SNAKE_CASE__       : str=None  ,							**SCREAMING_SNAKE_CASE__       : Tuple  )	->    List[Any]:
             a_      :	str							=						None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'  ,							SCREAMING_SNAKE_CASE__  ,							)
                    a_      :	int							=						kwargs.pop('feature_extractor'  )

             a_      :	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__(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						self.image_processor
             a_      :	Union[str, Any]							=						False
      def __call__( self       : int  ,							*SCREAMING_SNAKE_CASE__       : str  ,							**SCREAMING_SNAKE_CASE__       : Dict  )	->    Union[str, Any]:
             # For backward compatibility
             if self._in_target_context_manager:
                    return self.current_processor(*SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						kwargs.pop('images'  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						kwargs.pop('text'  ,							SCREAMING_SNAKE_CASE__  )
             if len(SCREAMING_SNAKE_CASE__  ) > 0:
                    a_      :	int							=						args[0]
                    a_      :	List[Any]							=						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_      :	Optional[int]							=						self.image_processor(SCREAMING_SNAKE_CASE__  ,							*SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
             if text is not None:
                    a_      :	Dict							=						self.tokenizer(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

             if text is None:
                    return inputs
             elif images is None:
                    return encodings
             else:
                    a_      :	List[str]							=						encodings['input_ids']
                    return inputs
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							*SCREAMING_SNAKE_CASE__       : List[str]  ,							**SCREAMING_SNAKE_CASE__       : Tuple  )	->    Union[str, Any]:
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							*SCREAMING_SNAKE_CASE__       : Tuple  ,							**SCREAMING_SNAKE_CASE__       : Union[str, Any]  )	->    str:
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
      @contextmanager
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Union[str, 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_      :	Optional[Any]							=						True
             a_      :	Dict							=						self.tokenizer
             yield
             a_      :	Optional[Any]							=						self.image_processor
             a_      :	List[str]							=						False
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Any=False  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=None  )	->    int:
             if added_vocab is None:
                    a_      :	Optional[Any]							=						self.tokenizer.get_added_vocab()

             a_      :	List[str]							=						{}

             while tokens:
                    a_      :	Optional[Any]							=						re.search(r'<s_(.*?)>'  ,							SCREAMING_SNAKE_CASE__  ,							re.IGNORECASE  )
                    if start_token is None:
                           break
                    a_      :	Optional[int]							=						start_token.group(1  )
                    a_      :	Any							=						re.search(rF"""</s_{key}>"""  ,							SCREAMING_SNAKE_CASE__  ,							re.IGNORECASE  )
                    a_      :	Optional[Any]							=						start_token.group()
                    if end_token is None:
                           a_      :	str							=						tokens.replace(SCREAMING_SNAKE_CASE__  ,							''  )
                    else:
                           a_      :	int							=						end_token.group()
                           a_      :	List[Any]							=						re.escape(SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						re.escape(SCREAMING_SNAKE_CASE__  )
                           a_      :	List[str]							=						re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""  ,							SCREAMING_SNAKE_CASE__  ,							re.IGNORECASE  )
                           if content is not None:
                                  a_      :	Optional[Any]							=						content.group(1  ).strip()
                                  if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                         a_      :	Tuple							=						self.tokenajson(SCREAMING_SNAKE_CASE__  ,							is_inner_value=SCREAMING_SNAKE_CASE__  ,							added_vocab=SCREAMING_SNAKE_CASE__  )
                                         if value:
                                                if len(SCREAMING_SNAKE_CASE__  ) == 1:
                                                       a_      :	Tuple							=						value[0]
                                                a_      :	Any							=						value
                                  else:  # leaf nodes
                                         a_      :	Dict							=						[]
                                         for leaf in content.split(r'<sep/>'  ):
                                                a_      :	Dict							=						leaf.strip()
                                                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                       a_      :	Dict							=						leaf[1:-2]  # for categorical special tokens
                                                output[key].append(SCREAMING_SNAKE_CASE__  )
                                         if len(output[key]  ) == 1:
                                                a_      :	Tuple							=						output[key][0]

                           a_      :	Tuple							=						tokens[tokens.find(SCREAMING_SNAKE_CASE__  ) + len(SCREAMING_SNAKE_CASE__  ) :].strip()
                           if tokens[:6] == r"<sep/>":  # non-leaf nodes
                                  return [output] + self.tokenajson(tokens[6:]  ,							is_inner_value=SCREAMING_SNAKE_CASE__  ,							added_vocab=SCREAMING_SNAKE_CASE__  )

             if len(SCREAMING_SNAKE_CASE__  ):
                    return [output] if is_inner_value else output
             else:
                    return [] if is_inner_value else {"text_sequence": tokens}
      @property
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,							SCREAMING_SNAKE_CASE__  ,							)
             return self.image_processor_class

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






def  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	list:

       """simple docstring"""

       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__A       )       )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
   __import__('doctest').testmod()


