





import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE_		     = {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

SCREAMING_SNAKE_CASE_		     = {
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			List[str]				=    VOCAB_FILES_NAMES
     __snake_case					:			Any				=    PRETRAINED_VOCAB_FILES_MAP
     __snake_case					:			int				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case					:			Dict				=    ["input_ids", "attention_mask"]
     __snake_case					:			List[int]				=    []






     def __init__(							self		:					Dict		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Any="<unk>"		,lowerCamelCase__		:					int="<s>"		,lowerCamelCase__		:					Any="</s>"		,lowerCamelCase__		:					Optional[Any]="<pad>"		,lowerCamelCase__		:					Tuple="[SEP]"		,lowerCamelCase__		:					List[Any]="[MASK]"		,lowerCamelCase__		:					List[str]="[CLS]"		,lowerCamelCase__		:					Optional[Dict[str, Any]] = None		,**lowerCamelCase__		:					Dict		,)  ->	None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__		,lowerCamelCase__ ) else bos_token
           SCREAMING_SNAKE_CASE	=		AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__		,lowerCamelCase__ ) else eos_token
           SCREAMING_SNAKE_CASE	=		AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__		,lowerCamelCase__ ) else unk_token
           SCREAMING_SNAKE_CASE	=		AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__		,lowerCamelCase__ ) else pad_token
           SCREAMING_SNAKE_CASE	=		AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__		,lowerCamelCase__ ) else cls_token
           SCREAMING_SNAKE_CASE	=		AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__		,lowerCamelCase__ ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           SCREAMING_SNAKE_CASE	=		AddedToken(lowerCamelCase__		,lstrip=lowerCamelCase__		,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__		,lowerCamelCase__ ) else mask_token

           SCREAMING_SNAKE_CASE	=		{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=lowerCamelCase__		,eos_token=lowerCamelCase__		,unk_token=lowerCamelCase__		,pad_token=lowerCamelCase__		,sep_token=lowerCamelCase__		,mask_token=lowerCamelCase__		,cls_token=lowerCamelCase__		,sp_model_kwargs=self.sp_model_kwargs		,**lowerCamelCase__		,)

           SCREAMING_SNAKE_CASE	=		vocab_file

           SCREAMING_SNAKE_CASE	=		spm.SentencePieceProcessor(**self.sp_model_kwargs )
           self.sp_model.Load(lowerCamelCase__ )






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	int:




           '''simple docstring'''


           return self.sp_model.get_piece_size()






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		{self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )}
           vocab.update(self.added_tokens_encoder )
           return vocab






     def __getstate__(							self		:					List[str] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.__dict__.copy()
           SCREAMING_SNAKE_CASE	=		None
           return state






     def __setstate__(							self		:					Any		,lowerCamelCase__		:					List[str] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		d

           # for backward compatibility
           if not hasattr(self		,"""sp_model_kwargs""" ):
                 SCREAMING_SNAKE_CASE	=		{}

           SCREAMING_SNAKE_CASE	=		spm.SentencePieceProcessor(**self.sp_model_kwargs )
           self.sp_model.Load(self.vocab_file )






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




           '''simple docstring'''


           return self.sp_model.encode(lowerCamelCase__		,out_type=lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					int )  ->	List[str]:




           '''simple docstring'''


           return self.sp_model.piece_to_id(lowerCamelCase__ )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.sp_model.IdToPiece(lowerCamelCase__ )
           return token






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					str )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		[]
           SCREAMING_SNAKE_CASE	=		""""""
           SCREAMING_SNAKE_CASE	=		False
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       if not prev_is_special:
                             out_string += " "
                       out_string += self.sp_model.decode(lowerCamelCase__ ) + token
                       SCREAMING_SNAKE_CASE	=		True
                       SCREAMING_SNAKE_CASE	=		[]
                 else:
                       current_sub_tokens.append(lowerCamelCase__ )
                       SCREAMING_SNAKE_CASE	=		False
           out_string += self.sp_model.decode(lowerCamelCase__ )
           return out_string.strip()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					List[int]		,lowerCamelCase__		:					bool = False		,lowerCamelCase__		:					bool = None		,lowerCamelCase__		:					bool = True		,**lowerCamelCase__		:					List[Any]		,)  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		kwargs.pop("""use_source_tokenizer"""		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		self.convert_ids_to_tokens(lowerCamelCase__		,skip_special_tokens=lowerCamelCase__ )

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           SCREAMING_SNAKE_CASE	=		[]
           SCREAMING_SNAKE_CASE	=		[]
           for token in filtered_tokens:
                 if skip_special_tokens and token in self.all_special_ids:
                       continue
                 if token in self.added_tokens_encoder:
                       if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(lowerCamelCase__ ) )
                             SCREAMING_SNAKE_CASE	=		[]
                       sub_texts.append(lowerCamelCase__ )
                 else:
                       current_sub_text.append(lowerCamelCase__ )
           if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(lowerCamelCase__ ) )

           # Mimic the behavior of the Rust tokenizer:
           # No space before [MASK] and [SEP]
           if spaces_between_special_tokens:
                 SCREAMING_SNAKE_CASE	=		re.sub(R""" (\[(MASK|SEP)\])"""		,R"""\1"""		,""" """.join(lowerCamelCase__ ) )
           else:
                 SCREAMING_SNAKE_CASE	=		"""""".join(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                 SCREAMING_SNAKE_CASE	=		self.clean_up_tokenization(lowerCamelCase__ )
                 return clean_text
           else:
                 return text






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Optional[str] = None )  ->	Tuple[str]:




           '''simple docstring'''


           if not os.path.isdir(lowerCamelCase__ ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
                 return
           SCREAMING_SNAKE_CASE	=		os.path.join(
               lowerCamelCase__		,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

           if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase__ ) and os.path.isfile(self.vocab_file ):
                 copyfile(self.vocab_file		,lowerCamelCase__ )
           elif not os.path.isfile(self.vocab_file ):
                 with open(lowerCamelCase__		,"""wb""" ) as fi:
                       SCREAMING_SNAKE_CASE	=		self.sp_model.serialized_model_proto()
                       fi.write(lowerCamelCase__ )

           return (out_vocab_file,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					List[int]		,lowerCamelCase__		:					Optional[List[int]] = None )  ->	List[int]:




           '''simple docstring'''


           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           SCREAMING_SNAKE_CASE	=		[self.cls_token_id]
           SCREAMING_SNAKE_CASE	=		[self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					List[int]		,lowerCamelCase__		:					Optional[List[int]] = None		,lowerCamelCase__		:					bool = False )  ->	List[int]:




           '''simple docstring'''


           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowerCamelCase__		,token_ids_a=lowerCamelCase__		,already_has_special_tokens=lowerCamelCase__ )

           if token_ids_a is None:
                 return [1] + ([0] * len(lowerCamelCase__ )) + [1]
           return [1] + ([0] * len(lowerCamelCase__ )) + [1] + ([0] * len(lowerCamelCase__ )) + [1]






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					List[int]		,lowerCamelCase__		:					Optional[List[int]] = None )  ->	List[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		[self.sep_token_id]
           SCREAMING_SNAKE_CASE	=		[self.cls_token_id]
           if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


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

            return "".join(_SCREAMING_SNAKE_CASE			)

      # matching characters
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)

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

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

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE	=		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"""))


