



"""simple docstring"""

import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Optional[int]	      =	["""image_processor""", """tokenizer"""]
 _lowerCAmelCase     :    Optional[int]	      =	"""AutoImageProcessor"""
 _lowerCAmelCase     :    Any	      =	"""AutoTokenizer"""

 def __init__(  self	: Union[str, Any]						,  lowercase_	: List[str]=None						,  lowercase_	: List[str]=None						,  **lowercase_	: Any       ):
     snake_case_				:  Optional[Any]       =			None
     if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''						,  lowercase_						,  )
         snake_case_				:  List[str]       =			kwargs.pop('''feature_extractor'''       )

     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__(lowercase_						,  lowercase_       )
     snake_case_				:  str       =			self.image_processor
     snake_case_				:  Any       =			False

 def __call__(  self	: Tuple						,  *lowercase_	: int						,  **lowercase_	: List[Any]       ):
     # For backward compatibility
     if self._in_target_context_manager:
         return self.current_processor(*lowercase_						,  **lowercase_       )

     snake_case_				:  Optional[Any]       =			kwargs.pop('''images'''						,  lowercase_       )
     snake_case_				:  Any       =			kwargs.pop('''text'''						,  lowercase_       )
     if len(lowercase_       ) > 0:
         snake_case_				:  Tuple       =			args[0]
         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:
         snake_case_				:  int       =			self.image_processor(lowercase_						,  *lowercase_						,  **lowercase_       )
     if text is not None:
         snake_case_				:  Any       =			self.tokenizer(lowercase_						,  **lowercase_       )

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

 def 		_snake_case      (  self	: List[str]						,  *lowercase_	: Optional[int]						,  **lowercase_	: int       ):
     return self.tokenizer.batch_decode(*lowercase_						,  **lowercase_       )

 def 		_snake_case      (  self	: List[str]						,  *lowercase_	: Tuple						,  **lowercase_	: List[str]       ):
     return self.tokenizer.decode(*lowercase_						,  **lowercase_       )

 @contextmanager
 def 		_snake_case      (  self	: Dict       ):
     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.'''       )
     snake_case_				:  str       =			True
     snake_case_				:  int       =			self.tokenizer
     yield
     snake_case_				:  Tuple       =			self.image_processor
     snake_case_				:  Tuple       =			False

 def 		_snake_case      (  self	: List[Any]						,  lowercase_	: List[str]						,  lowercase_	: str=False						,  lowercase_	: Optional[Any]=None       ):
     if added_vocab is None:
         snake_case_				:  str       =			self.tokenizer.get_added_vocab()

     snake_case_				:  Union[str, Any]       =			{}

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

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

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

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






 @property
 def 		_snake_case      (  self	: Optional[int]       ):
     warnings.warn(
         '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,  lowercase_						,  )
     return self.image_processor










"""simple docstring"""

def __lowercase    (							_a					):
    snake_case_				:  Optional[Any]       =			int(_a					)
    if decimal in (0, 1):  # Exit cases for the recursion
        return str(_a					)
    snake_case_,			snake_case_				:  Optional[int]       =			divmod(_a	,    2					)
    return binary_recursive(_a					) + str(_a					)




def __lowercase    (							_a					):
    snake_case_				:  Dict       =			str(_a					).strip()
    if not number:
        raise ValueError('''No input value was provided'''					)
    snake_case_				:  List[str]       =			'''-''' if number.startswith('''-'''					) else ''''''
    snake_case_				:  Optional[Any]       =			number.lstrip('''-'''					)
    if not number.isnumeric():
        raise ValueError('''Input value is not an integer'''					)
    return f"{negative}0b{binary_recursive(int(_a					)					)}"


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

     testmod()





