





'''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__				:   List[str]      =logging.get_logger(__name__)


A__				:   Any      ='''▁'''

A__				:   List[Any]      ={'''vocab_file''': '''spiece.model'''}

A__				:   List[Any]      ={
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

A__				:   str      ={
    '''google/reformer-crime-and-punishment''': 52_42_88,
}




class  UpperCAmelCase     (			snake_case_   ):
      _lowercase:			Optional[Any]						     =   VOCAB_FILES_NAMES
      _lowercase:			Dict						     =   PRETRAINED_VOCAB_FILES_MAP
      _lowercase:			Optional[Any]						     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:			Dict						     =   ['''input_ids''', '''attention_mask''']




      def __init__(			self			: List[Any]			,			__snake_case			: Union[str, Any]			,			__snake_case			: Any="</s>"			,			__snake_case			: Dict="<unk>"			,			__snake_case			: List[str]=[]			,			__snake_case			: Optional[Dict[str, Any]] = None			,			**__snake_case			: Union[str, Any]			,			)				->      None:
            _lowerCAmelCase							     =							{} if sp_model_kwargs is None else sp_model_kwargs

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

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




      @property
      def 				lowercase__    (			self			: Union[str, Any]  )				->      str:
            return self.sp_model.get_piece_size()




      def 				lowercase__    (			self			: Optional[int]  )				->      Dict[str, int]:
            _lowerCAmelCase							     =							{self.convert_ids_to_tokens(__snake_case  ): i for i in range(self.vocab_size  )}
            vocab.update(self.added_tokens_encoder  )
            return vocab




      def __getstate__(			self			: Optional[int]  )				->      List[str]:
            _lowerCAmelCase							     =							self.__dict__.copy()
            _lowerCAmelCase							     =							None
            return state




      def __setstate__(			self			: str			,			__snake_case			: Dict  )				->      Dict:
            _lowerCAmelCase							     =							d

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

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




      def 				lowercase__    (			self			: int			,			__snake_case			: str  )				->      List[str]:
            return self.sp_model.encode(__snake_case			,			out_type=__snake_case  )




      def 				lowercase__    (			self			: str			,			__snake_case			: Any  )				->      Optional[int]:
            return self.sp_model.piece_to_id(__snake_case  )




      def 				lowercase__    (			self			: Optional[Any]			,			__snake_case			: str  )				->      Optional[Any]:
            if index < self.sp_model.get_piece_size():
                  _lowerCAmelCase							     =							self.sp_model.IdToPiece(__snake_case  )
            return token




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: str  )				->      int:
            _lowerCAmelCase							     =							[]
            _lowerCAmelCase							     =							""""""
            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(__snake_case  ) + token
                        _lowerCAmelCase							     =							[]
                  else:
                        current_sub_tokens.append(__snake_case  )
            out_string += self.sp_model.decode(__snake_case  )
            return out_string.strip()




      def 				lowercase__    (			self			: int			,			__snake_case			: str			,			__snake_case			: Optional[str] = None  )				->      Tuple[str]:
            if not os.path.isdir(__snake_case  ):
                  logger.error(f"Vocabulary path ({save_directory}) should be a directory"  )
                  return
            _lowerCAmelCase							     =							os.path.join(
                __snake_case			,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

            return (out_vocab_file,)










'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__:    Dict					    =       logging.get_logger(__name__)

A__:    Tuple					    =       {
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  Tuple	       =							"roc_bert"






      def __init__( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:List[str]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Dict=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[Any]="gelu"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.1     ,						SCREAMING_SNAKE_CASE			:List[Any]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=1e-12     ,						SCREAMING_SNAKE_CASE			:Any=True     ,						SCREAMING_SNAKE_CASE			:List[Any]=0     ,						SCREAMING_SNAKE_CASE			:Optional[int]="absolute"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						SCREAMING_SNAKE_CASE			:int=True     ,						SCREAMING_SNAKE_CASE			:Optional[int]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=9_1_0     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=5_1_2     ,						SCREAMING_SNAKE_CASE			:str=2_4_8_5_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						**SCREAMING_SNAKE_CASE			:Tuple     ,						)       ->					Optional[int]:




             '''simple docstring'''
             _a    : List[str]     					=vocab_size
             _a    : List[str]     					=max_position_embeddings
             _a    : Optional[Any]     					=hidden_size
             _a    : List[Any]     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : int     					=intermediate_size
             _a    : Any     					=hidden_act
             _a    : Dict     					=hidden_dropout_prob
             _a    : int     					=attention_probs_dropout_prob
             _a    : str     					=initializer_range
             _a    : Optional[int]     					=type_vocab_size
             _a    : Any     					=layer_norm_eps
             _a    : Any     					=use_cache
             _a    : Optional[int]     					=enable_pronunciation
             _a    : Optional[Any]     					=enable_shape
             _a    : Optional[Any]     					=pronunciation_embed_dim
             _a    : Tuple     					=pronunciation_vocab_size
             _a    : Union[str, Any]     					=shape_embed_dim
             _a    : Any     					=shape_vocab_size
             _a    : Tuple     					=concat_input
             _a    : List[str]     					=position_embedding_type
             _a    : List[str]     					=classifier_dropout
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)


