
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


__lowerCamelCase							=       logging.get_logger(__name__)

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

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

__lowerCamelCase							=       {"""bert_for_seq_generation""": 5_12}






class   UpperCAmelCase		(						A_ ):
   A__					:		Tuple						     =     VOCAB_FILES_NAMES
   A__					:		Optional[Any]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Tuple						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		List[int]						     =     []
   A__					:		str						     =     ["input_ids", "attention_mask"]



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


          '''simple docstring'''


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

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

          snake_case       :   Tuple							=      vocab_file

          snake_case       :   Optional[int]							=      spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(snake_case__ )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	int:


          '''simple docstring'''


          return self.sp_model.get_piece_size()



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


          '''simple docstring'''


          snake_case       :   Tuple							=      {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[Any] )		->	Optional[int]:


          '''simple docstring'''


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



   def __setstate__(self		:					List[Any]    ,						snake_case__		:					Dict )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      d

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

          snake_case       :   Tuple							=      spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(self.vocab_file )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					List[str] )		->	str:


          '''simple docstring'''


          return self.sp_model.piece_to_id(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Dict )		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.sp_model.IdToPiece(snake_case__ )
          return token



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   str							=      []
          snake_case       :   Tuple							=      ""
          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
                        snake_case       :   str							=      []
                 else:
                        current_sub_tokens.append(snake_case__ )
          out_string += self.sp_model.decode(snake_case__ )
          return out_string.strip()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          if not os.path.isdir(snake_case__ ):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                 return
          snake_case       :   List[Any]							=      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:
                        snake_case       :   int							=      self.sp_model.serialized_model_proto()
                        fi.write(snake_case__ )

          return (out_vocab_file,)







__lowerCamelCase							=       {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_5818,
}


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : float    ):
       if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
              snake_case       :   List[Any]							=      (
                  f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                  f"""Valid values are: {', '.join(__lowerCamelCase    )}"""
              )
              raise ValueError(__lowerCamelCase    )
       return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





