

'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_       						=							logging.get_logger(__name__)


A_       						=							"▁"

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

A_       						=							{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

A_       						=							{
    "google/reformer-crime-and-punishment": 52_42_88,
}





class 							_snake_case (       lowerCAmelCase__						):
   _A       :     Tuple       =					VOCAB_FILES_NAMES
   _A       :     List[Any]       =					PRETRAINED_VOCAB_FILES_MAP
   _A       :     Union[str, Any]       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A       :     Dict       =					["input_ids", "attention_mask"]
   def __init__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]="</s>"     ,SCREAMING_SNAKE_CASE__			:      List[str]="<unk>"     ,SCREAMING_SNAKE_CASE__			:      Tuple=[]     ,SCREAMING_SNAKE_CASE__			:      List[Any] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     SCREAMING_SNAKE_CASE:int						      =					{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=_lowerCamelCase     ,unk_token=_lowerCamelCase     ,additional_special_tokens=_lowerCamelCase     ,sp_model_kwargs=self.sp_model_kwargs     ,**_lowerCamelCase     ,)

     SCREAMING_SNAKE_CASE:int						      =					vocab_file
     SCREAMING_SNAKE_CASE:Tuple						      =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(_lowerCamelCase							)
   @property
   def     __UpperCamelCase						( self			:      str							):
     return self.sp_model.get_piece_size()
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					{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[Any]							):
     SCREAMING_SNAKE_CASE:Dict						      =					self.__dict__.copy()
     SCREAMING_SNAKE_CASE:int						      =					None
     return state
   def __setstate__( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					d

     # for backward compatibility
     if not hasattr(self     ,"sp_model_kwargs"							):
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{}

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(self.vocab_file							)
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     return self.sp_model.encode(_lowerCamelCase     ,out_type=_lowerCamelCase							)
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     return self.sp_model.piece_to_id(_lowerCamelCase							)
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):





     if index < self.sp_model.get_piece_size():
       SCREAMING_SNAKE_CASE:Optional[int]						      =					self.sp_model.IdToPiece(_lowerCamelCase							)
     return token
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Tuple							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					[]
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					""
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         out_string += self.sp_model.decode(_lowerCamelCase							) + token
         SCREAMING_SNAKE_CASE:Dict						      =					[]
       else:
         current_sub_tokens.append(_lowerCamelCase							)
     out_string += self.sp_model.decode(_lowerCamelCase							)
     return out_string.strip()







   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Dict = None							):





     if not os.path.isdir(_lowerCamelCase							):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
       return
     SCREAMING_SNAKE_CASE:Tuple						      =					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:Optional[Any]						      =					self.sp_model.serialized_model_proto()
         fi.write(_lowerCamelCase							)

     return (out_vocab_file,)









'''simple docstring'''

from ..utils import DummyObject, requires_backends





class 							_snake_case (       metaclass=_a						):
   _A       :     Any       =					['''torch''', '''torchsde''']
   def __init__( self			:      Any     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     requires_backends(self     ,["torch", "torchsde"]							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Dict     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)







   @classmethod
   def     __UpperCamelCase						( cls			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)






