


import numpy as np







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.array:
  return 1 / (1 + np.exp(-vector				))







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.array:
  return vector * sigmoid(1.702 * vector				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       ['image_processor', 'tokenizer']
     __lowercase				:							str	   =       'AutoImageProcessor'
     __lowercase				:							Dict	   =       'AutoTokenizer'

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

       snake_case__						= 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   )
       snake_case__						= self.image_processor
       snake_case__						= False

     def __call__(					self:Optional[int]     ,		*_a:str     ,		**_a:int   ):
       # For backward compatibility
       if self._in_target_context_manager:
         return self.current_processor(*_a     ,		**_a   )

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		*_a:Union[str, Any]     ,		**_a:Any   ):
       return self.tokenizer.batch_decode(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		*_a:Union[str, Any]     ,		**_a:Optional[int]   ):
       return self.tokenizer.decode(*_a     ,		**_a   )

     @contextmanager
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       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__						= True
       snake_case__						= self.tokenizer
       yield
       snake_case__						= self.image_processor
       snake_case__						= False

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Dict     ,		_a:Dict=False     ,		_a:Optional[int]=None   ):
       if added_vocab is None:
         snake_case__						= self.tokenizer.get_added_vocab()

       snake_case__						= {}

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

           snake_case__						= 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 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,		_a     ,		)
       return self.image_processor

