"""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


__snake_case             =   logging.get_logger(__name__)

__snake_case             =   {'vocab_file': 'spiece.model'}

__snake_case             =   {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

__snake_case             =   {'bert_for_seq_generation': 512}







class _lowerCAmelCase    (				_lowerCamelCase     ):
   __UpperCAmelCase			:	int							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase			:	int							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:	Dict							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:	List[Any]							  =  []
   __UpperCAmelCase			:	int							  =  ['''input_ids''', '''attention_mask''']







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__="<s>"						,     UpperCamelCase__="</s>"						,     UpperCamelCase__="<unk>"						,     UpperCamelCase__="<pad>"						,     UpperCamelCase__="<::::>"						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Dict				 =   {} if sp_model_kwargs is None else sp_model_kwargs

       # Add extra_ids to the special token list
       super().__init__(
           bos_token=UpperCamelCase__						,     eos_token=UpperCamelCase__						,     unk_token=UpperCamelCase__						,     pad_token=UpperCamelCase__						,     sep_token=UpperCamelCase__						,     sp_model_kwargs=self.sp_model_kwargs						,     **UpperCamelCase__						,     )

       snake_case   :				Optional[int]				 =   vocab_file

       snake_case   :				str				 =   spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(UpperCamelCase__						)







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       return self.sp_model.get_piece_size()







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				int				 =   {self.convert_ids_to_tokens(UpperCamelCase__						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab







   def __getstate__(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				int				 =   self.__dict__.copy()
       snake_case   :				Optional[Any]				 =   None
       return state







   def __setstate__(     self						,     UpperCamelCase__						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				str				 =   d

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

       snake_case   :				List[Any]				 =   spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(self.vocab_file						)







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

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       return self.sp_model.piece_to_id(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   self.sp_model.IdToPiece(UpperCamelCase__						)
       return token







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Dict				 =   []
       snake_case   :				List[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(UpperCamelCase__						) + token
               snake_case   :				Dict				 =   []
           else:
               current_sub_tokens.append(UpperCamelCase__						)
       out_string += self.sp_model.decode(UpperCamelCase__						)
       return out_string.strip()







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		List[str]:

       '''simple docstring'''




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

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

       return (out_vocab_file,)









"""simple docstring"""



from __future__ import annotations

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  list[float]			)     ->						Any:


    """simple docstring"""





    return np.maximum(0						,							lowercase			)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]





