





'''simple docstring'''




def        _lowerCAmelCase			(  __snake_case  : str ,							__snake_case  : str			)     ->     float:

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

          return "".join(__snake_case			)

     # matching characters
     __A						: Union[str, Any]      =       get_matched_characters(__snake_case ,							__snake_case			)
     __A						: str      =       get_matched_characters(__snake_case ,							__snake_case			)
     __A						: Tuple      =       len(__snake_case			)

     # transposition
     __A						: Optional[int]      =       (
         len([(ca, ca) for ca, ca in zip(__snake_case ,							__snake_case			) if ca != ca]			) // 2
     )

     if not match_count:
          __A						: List[Any]      =       0.0
     else:
          __A						: int      =       (
              1
              / 3
              * (
                  match_count / len(__snake_case			)
                  + match_count / len(__snake_case			)
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						: Any      =       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'''))






'''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  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       ['''image_processor''', '''tokenizer''']
     lowerCAmelCase					=       '''BlipImageProcessor'''
     lowerCAmelCase					=       ('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       False
          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Optional[int]      =       self.image_processor


     def __call__(						self						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = 0						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''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:
               __A						: int      =       self.tokenizer
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
               return text_encoding

          # add pixel_values
          __A						: List[Any]      =       self.image_processor(_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          else:
               __A						: int      =       None

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

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






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


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.tokenizer.model_input_names
          __A						: Tuple      =       self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))