
'''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


lowercase     :							Optional[int]       						=     '▁'

lowercase     :							Dict       						=     {'vocab_file': 'spiece.model'}

lowercase     :							Any       						=     {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

lowercase     :							Any       						=     {
    'google/pegasus-xsum': 5_12,
}


lowercase     :							Optional[int]       						=     logging.get_logger(__name__)





class 					A							(				__snake_case		):
     __magic_name__          =						VOCAB_FILES_NAMES

     __magic_name__          =						VOCAB_FILES_NAMES
     __magic_name__          =						PRETRAINED_VOCAB_FILES_MAP
     __magic_name__          =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__          =						['''input_ids''', '''attention_mask''']





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE="<pad>"		,     SCREAMING_SNAKE_CASE="</s>"		,     SCREAMING_SNAKE_CASE="<unk>"		,     SCREAMING_SNAKE_CASE="<mask_2>"		,     SCREAMING_SNAKE_CASE="<mask_1>"		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=103		,     SCREAMING_SNAKE_CASE = None		,     **SCREAMING_SNAKE_CASE		,     )					->							None:






         """simple docstring"""






         A			:  Tuple   =		offset
         if additional_special_tokens is not None:
             if not isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 raise TypeError(
                     F'additional_special_tokens should be of type {type(SCREAMING_SNAKE_CASE      )}, but is'
                     F' {type(SCREAMING_SNAKE_CASE      )}'      )

             A			:  List[Any]   =		(
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 F'<unk_{i}>' for i in range(len(SCREAMING_SNAKE_CASE      )		,     self.offset - 1      )
             ]

             if len(set(SCREAMING_SNAKE_CASE      )      ) != len(SCREAMING_SNAKE_CASE      ):
                 raise ValueError(
                     '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                     F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'      )
             A			:  Union[str, Any]   =		additional_special_tokens_extended
         else:
             A			:  Tuple   =		[mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [F'<unk_{i}>' for i in range(2		,     self.offset      )]

         A			:  List[Any]   =		{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=SCREAMING_SNAKE_CASE		,     unk_token=SCREAMING_SNAKE_CASE		,     mask_token=SCREAMING_SNAKE_CASE		,     pad_token=SCREAMING_SNAKE_CASE		,     mask_token_sent=SCREAMING_SNAKE_CASE		,     offset=SCREAMING_SNAKE_CASE		,     additional_special_tokens=SCREAMING_SNAKE_CASE		,     sp_model_kwargs=self.sp_model_kwargs		,     **SCREAMING_SNAKE_CASE		,     )
         A			:  Dict   =		mask_token_sent
         A			:  str   =		vocab_file
         A			:  Optional[int]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
         self.sp_model.Load(SCREAMING_SNAKE_CASE      )

         # add special tokens to encoder dict
         A			:  Dict[int, str]   =		{
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }      )

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1		,     self.offset - 1      )}      )

         A			:  Dict[str, int]   =		{v: k for k, v in self.encoder.items()}





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return len(self.sp_model      ) + self.offset





     def 							__lowerCAmelCase				(				self      )					->							Dict[str, int]:






         """simple docstring"""






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





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






         """simple docstring"""






         A			:  Optional[int]   =		self.__dict__.copy()
         A			:  str   =		None
         return state





     def __setstate__(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Optional[int]   =		d

         # for backward compatibility
         if not hasattr(self		,     '''sp_model_kwargs'''      ):
             A			:  Any   =		{}

         A			:  Any   =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
         self.sp_model.Load(self.vocab_file      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							List[str]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         A			:  str   =		self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE      )
         return sp_id + self.offset





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             A			:  List[Any]   =		self.sp_model.IdToPiece(index - self.offset      )
         return token





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Tuple   =		[]
         A			:  int   =		''''''
         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(SCREAMING_SNAKE_CASE      ) + token
                 A			:  str   =		[]
             else:
                 current_sub_tokens.append(SCREAMING_SNAKE_CASE      )
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      )
         return out_string.strip()





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE=False      )					->							str:






         """simple docstring"""






         return 1





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Any:






         """simple docstring"""






         A			:  str   =		set(self.all_special_ids      )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = None		,     SCREAMING_SNAKE_CASE = False      )					->							List[int]:






         """simple docstring"""






         if already_has_special_tokens:
             return self._special_token_mask(SCREAMING_SNAKE_CASE      )
         elif token_ids_a is None:
             return self._special_token_mask(SCREAMING_SNAKE_CASE      ) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=None      )					->							List[int]:






         """simple docstring"""






         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = None      )					->							Tuple[str]:






         """simple docstring"""






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

         if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE      ) and os.path.isfile(self.vocab_file      ):
             copyfile(self.vocab_file		,     SCREAMING_SNAKE_CASE      )
         elif not os.path.isfile(self.vocab_file      ):
             with open(SCREAMING_SNAKE_CASE		,     '''wb'''      ) as fi:
                 A			:  List[Any]   =		self.sp_model.serialized_model_proto()
                 fi.write(SCREAMING_SNAKE_CASE      )

         return (out_vocab_file,)






'''simple docstring'''




lowercase     :							dict[str, float]       						=     {
    "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": 1.6_02_17_66_34e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_58_18,
}







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
        A			:  int   =		(
            F'Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'
            F'Valid values are: {", ".join(snake_case__		)}'
        )
        raise ValueError(snake_case__		)
    return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




