



"""simple docstring"""







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      UpperCAmelCase__		:



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






									'''simple docstring'''

									A         =					question_encoder
									A         =					generator
									A         =					self.question_encoder



		def 					A_		( self  :    Tuple      ,							snake_case  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									if os.path.isfile(_A				):
																raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""				)
									os.makedirs(_A      ,							exist_ok=_A				)
									A         =					os.path.join(_A      ,							'question_encoder_tokenizer'				)
									A         =					os.path.join(_A      ,							'generator_tokenizer'				)
									self.question_encoder.save_pretrained(_A				)
									self.generator.save_pretrained(_A				)



		@classmethod
		def 					A_		( cls  :    Tuple      ,							snake_case  :    Any      ,							**snake_case  :    Union[str, Any]				)			->						Dict:






									'''simple docstring'''

									from ..auto.tokenization_auto import AutoTokenizer

									A         =					kwargs.pop('config'      ,							_A				)

									if config is None:
																A         =					RagConfig.from_pretrained(_A				)

									A         =					AutoTokenizer.from_pretrained(
									    _A      ,							config=config.question_encoder      ,							subfolder='question_encoder_tokenizer'				)
									A         =					AutoTokenizer.from_pretrained(
									    _A      ,							config=config.generator      ,							subfolder='generator_tokenizer'				)
									return cls(question_encoder=_A      ,							generator=_A				)



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






									'''simple docstring'''

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



		def 					A_		( self  :    Any      ,							*snake_case  :    Tuple      ,							**snake_case  :    Dict				)			->						List[Any]:






									'''simple docstring'''

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



		def 					A_		( self  :    List[str]      ,							*snake_case  :    Union[str, Any]      ,							**snake_case  :    List[str]				)			->						List[Any]:






									'''simple docstring'''

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



		def 					A_		( self  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									A         =					self.question_encoder



		def 					A_		( self  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									A         =					self.generator



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Dict = None      ,							snake_case  :    int = None      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    Dict = "longest"      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    str = True      ,							**snake_case  :    Tuple      ,							)			->						str:






									'''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'      ,							_A      ,							)
									if max_length is None:
																A         =					self.current_tokenizer.model_max_length
									A         =					self(
									    _A      ,							add_special_tokens=_A      ,							return_tensors=_A      ,							max_length=_A      ,							padding=_A      ,							truncation=_A      ,							**_A      ,							)
									if tgt_texts is None:
																return model_inputs
									# Process tgt_texts
									if max_target_length is None:
																A         =					self.current_tokenizer.model_max_length
									A         =					self(
									    text_target=_A      ,							add_special_tokens=_A      ,							return_tensors=_A      ,							padding=_A      ,							max_length=_A      ,							truncation=_A      ,							**_A      ,							)
									A         =					labels['input_ids']
									return model_inputs





"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							float:

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

														return "".join(lowerCamelCase__			)

							# matching characters
							A         =					get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					len(lowerCamelCase__			)

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

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

							# common prefix up to 4 characters
							A         =					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'))



