





"""simple docstring"""






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  ["""image_processor""", """tokenizer"""]
       lowerCAmelCase__						 =  """AutoImageProcessor"""
       lowerCAmelCase__						 =  """AutoTokenizer"""






       def __init__(   self    ,  a__=None    ,  a__=None    ,  **a__     ):
              _UpperCAmelCase     =     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     =     kwargs.pop('feature_extractor'     )

              _UpperCAmelCase     =     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__(a__    ,  a__     )
              _UpperCAmelCase     =     self.image_processor
              _UpperCAmelCase     =     False






       def __call__(   self    ,  *a__    ,  **a__     ):
              # For backward compatibility
              if self._in_target_context_manager:
                     return self.current_processor(*a__    ,  **a__     )

              _UpperCAmelCase     =     kwargs.pop('images'    ,  a__     )
              _UpperCAmelCase     =     kwargs.pop('text'    ,  a__     )
              if len(a__     ) > 0:
                     _UpperCAmelCase     =     args[0]
                     _UpperCAmelCase     =     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:
                     _UpperCAmelCase     =     self.image_processor(a__    ,  *a__    ,  **a__     )
              if text is not None:
                     _UpperCAmelCase     =     self.tokenizer(a__    ,  **a__     )

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






       def 							__A (   self    ,  *a__    ,  **a__     ):
              return self.tokenizer.batch_decode(*a__    ,  **a__     )






       def 							__A (   self    ,  *a__    ,  **a__     ):
              return self.tokenizer.decode(*a__    ,  **a__     )






       @contextmanager
       def 							__A (   self     ):
              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.'     )
              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     self.tokenizer
              yield
              _UpperCAmelCase     =     self.image_processor
              _UpperCAmelCase     =     False






       def 							__A (   self    ,  a__    ,  a__=False    ,  a__=None     ):
              if added_vocab is None:
                     _UpperCAmelCase     =     self.tokenizer.get_added_vocab()

              _UpperCAmelCase     =     {}

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

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

              if len(a__     ):
                     return [output] if is_inner_value else output
              else:
                     return [] if is_inner_value else {"text_sequence": tokens}






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









"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       if a < 0 or b < 0:
              raise ValueError('the value of both inputs must be positive'							)

       _UpperCAmelCase     =     str(bin(SCREAMING_SNAKE_CASE							)							)[2:]  # remove the leading "0b"
       _UpperCAmelCase     =     str(bin(SCREAMING_SNAKE_CASE							)							)[2:]  # remove the leading "0b"

       _UpperCAmelCase     =     max(len(SCREAMING_SNAKE_CASE							),len(SCREAMING_SNAKE_CASE							)							)

       return "0b" + "".join(
           str(int(char_a == '1' and char_b == '1'							)							)
           for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE							),b_binary.zfill(SCREAMING_SNAKE_CASE							)							)							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






