import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a_    					=       logging.get_logger(__name__)





class 		_lowercase						:

 def __init__( self	:							List[str]   ,      snake_case	:							Tuple   ,      snake_case	:							Optional[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					question_encoder
  UpperCamelCase_					:						Any =					generator
  UpperCamelCase_					:						Any =					self.question_encoder

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							List[str]			)   ->						Union[str, Any]:

  """simple docstring"""





  if os.path.isfile(snake_case			):
   raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"			)
  os.makedirs(snake_case   ,      exist_ok=snake_case			)
  UpperCamelCase_					:						Tuple =					os.path.join(snake_case   ,      'question_encoder_tokenizer'			)
  UpperCamelCase_					:						List[Any] =					os.path.join(snake_case   ,      'generator_tokenizer'			)
  self.question_encoder.save_pretrained(snake_case			)
  self.generator.save_pretrained(snake_case			)

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Any   ,      snake_case	:							List[str]   ,      **snake_case	:							Any			)   ->						Optional[int]:

  """simple docstring"""





  from ..auto.tokenization_auto import AutoTokenizer

  UpperCamelCase_					:						List[Any] =					kwargs.pop('config'   ,      snake_case			)

  if config is None:
   UpperCamelCase_					:						List[Any] =					RagConfig.from_pretrained(snake_case			)

  UpperCamelCase_					:						List[str] =					AutoTokenizer.from_pretrained(
      snake_case   ,      config=config.question_encoder   ,      subfolder='question_encoder_tokenizer'			)
  UpperCamelCase_					:						List[str] =					AutoTokenizer.from_pretrained(
      snake_case   ,      config=config.generator   ,      subfolder='generator_tokenizer'			)
  return cls(question_encoder=snake_case   ,      generator=snake_case			)

 def __call__( self	:							int   ,      *snake_case	:							List[Any]   ,      **snake_case	:							int			)   ->						Any:

  """simple docstring"""





  return self.current_tokenizer(*snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      *snake_case	:							Tuple   ,      **snake_case	:							Optional[int]			)   ->						Tuple:

  """simple docstring"""





  return self.generator.batch_decode(*snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      *snake_case	:							List[Any]   ,      **snake_case	:							str			)   ->						Any:

  """simple docstring"""





  return self.generator.decode(*snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					self.question_encoder

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					self.generator


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							List[str]   ,      snake_case	:							Optional[List[str]] = None   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							str = "longest"   ,      snake_case	:							str = None   ,      snake_case	:							bool = True   ,      **snake_case	:							List[str]   ,      )   ->						BatchEncoding:

  """simple docstring"""





  warnings.warn(
      '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
      'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
      'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
      'details'   ,      snake_case   ,      )
  if max_length is None:
   UpperCamelCase_					:						Any =					self.current_tokenizer.model_max_length
  UpperCamelCase_					:						List[str] =					self(
      snake_case   ,      add_special_tokens=snake_case   ,      return_tensors=snake_case   ,      max_length=snake_case   ,      padding=snake_case   ,      truncation=snake_case   ,      **snake_case   ,      )
  if tgt_texts is None:
   return model_inputs
  # Process tgt_texts
  if max_target_length is None:
   UpperCamelCase_					:						Optional[int] =					self.current_tokenizer.model_max_length
  UpperCamelCase_					:						Optional[int] =					self(
      text_target=snake_case   ,      add_special_tokens=snake_case   ,      return_tensors=snake_case   ,      padding=snake_case   ,      max_length=snake_case   ,      truncation=snake_case   ,      **snake_case   ,      )
  UpperCamelCase_					:						Union[str, Any] =					labels['input_ids']
  return model_inputs




def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str	):

 def get_matched_characters(lowerCamelCase					:     str  ,  lowerCamelCase					:     str	) -> str:
  UpperCamelCase_					:						Tuple =					[]
  UpperCamelCase_					:						List[Any] =					min(len(_stra	)  ,  len(_stra	)	) // 2
  for i, l in enumerate(_stra	):
   UpperCamelCase_					:						int =					int(max(0  ,  i - limit	)	)
   UpperCamelCase_					:						Dict =					int(min(i + limit + 1  ,  len(_stra	)	)	)
   if l in _stra[left:right]:
    matched.append(lowerCamelCase	)
    UpperCamelCase_					:						Dict =					F"{_stra[0:_stra.index(lowerCamelCase	)]} {_stra[_stra.index(lowerCamelCase	) + 1:]}"

  return "".join(lowerCamelCase	)

 # matching characters
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					len(lowerCamelCase	)

 # transposition
 UpperCamelCase_					:						int =					(
     len([(ca, ca) for ca, ca in zip(lowerCamelCase  ,  lowerCamelCase	) if ca != ca]	) // 2
 )

 if not match_count:
  UpperCamelCase_					:						Union[str, Any] =					0.0
 else:
  UpperCamelCase_					:						str =					(
      1
      / 3
      * (
          match_count / len(lowerCamelCase	)
          + match_count / len(lowerCamelCase	)
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 UpperCamelCase_					:						Dict =					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'))



