


"""simple docstring"""

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    lowerCamelCase__						(		_a    ):
      a :     Optional[Any]											=		["""image_processor""", """tokenizer"""]
      a :     Tuple											=		"""BlipImageProcessor"""
      a :     List[Any]											=		("""BertTokenizer""", """BertTokenizerFast""")
      def __init__(		self	:	Dict      ,       A_	:	List[str]      ,       A_	:	Any							):






       '''simple docstring'''


       __lowercase									=	False
       super().__init__(A_      ,       A_							)
       __lowercase									=	self.image_processor
      def __call__(		self	:	int      ,       A_	:	ImageInput = None      ,       A_	:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,       A_	:	bool = True      ,       A_	:	Union[bool, str, PaddingStrategy] = False      ,       A_	:	Union[bool, str, TruncationStrategy] = None      ,       A_	:	Optional[int] = None      ,       A_	:	int = 0      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[bool] = None      ,       A_	:	bool = False      ,       A_	:	bool = False      ,       A_	:	bool = False      ,       A_	:	bool = False      ,       A_	:	bool = False      ,       A_	:	bool = True      ,       A_	:	Optional[Union[str, TensorType]] = None      ,       **A_	:	List[Any]      ,       ):






       '''simple docstring'''


       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:
        __lowercase									=	self.tokenizer
        __lowercase									=	self.tokenizer(
            text=A_      ,       add_special_tokens=A_      ,       padding=A_      ,       truncation=A_      ,       max_length=A_      ,       stride=A_      ,       pad_to_multiple_of=A_      ,       return_attention_mask=A_      ,       return_overflowing_tokens=A_      ,       return_special_tokens_mask=A_      ,       return_offsets_mapping=A_      ,       return_token_type_ids=A_      ,       return_length=A_      ,       verbose=A_      ,       return_tensors=A_      ,       **A_      ,       )
        return text_encoding

       # add pixel_values
       __lowercase									=	self.image_processor(A_      ,       return_tensors=A_							)

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

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

       return encoding_image_processor
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       *A_	:	Tuple      ,       **A_	:	Union[str, Any]							):






       '''simple docstring'''


       return self.tokenizer.batch_decode(*A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       *A_	:	Optional[Any]      ,       **A_	:	Union[str, Any]							):






       '''simple docstring'''


       return self.tokenizer.decode(*A_      ,       **A_							)






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	self.tokenizer.model_input_names
       __lowercase									=	self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







 def get_matched_characters(UpperCamelCase__	: str				,   UpperCamelCase__	: str ) -> str:
  __lowercase									=	[]
  __lowercase									=	min(len(_stra )				,   len(_stra ) ) // 2
  for i, l in enumerate(_stra ):
   __lowercase									=	int(max(0				,   i - limit ) )
   __lowercase									=	int(min(i + limit + 1				,   len(_stra ) ) )
   if l in _stra[left:right]:
    matched.append(UpperCamelCase__ )
    __lowercase									=	f'''{_stra[0:_stra.index(UpperCamelCase__ )]} {_stra[_stra.index(UpperCamelCase__ ) + 1:]}'''

  return "".join(UpperCamelCase__ )

 # matching characters
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

 # transposition
 __lowercase									=	(
     len([(ca, ca) for ca, ca in zip(UpperCamelCase__				,   UpperCamelCase__ ) if ca != ca] ) // 2
 )

 if not match_count:
  __lowercase									=	0.0
 else:
  __lowercase									=	(
      1
      / 3
      * (
          match_count / len(UpperCamelCase__ )
          + match_count / len(UpperCamelCase__ )
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 __lowercase									=	0
 for ca, ca in zip(stra[:4]				,   stra[:4] ):
  if ca == ca:
   prefix_len += 1
  else:
   break

 return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler("hello", "world"))
