





'''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


_SCREAMING_SNAKE_CASE	 =							"▁"

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

_SCREAMING_SNAKE_CASE	 =							{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_SCREAMING_SNAKE_CASE	 =							{
    "google/pegasus-xsum": 5_12,
}


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Tuple         =					VOCAB_FILES_NAMES

 __lowerCamelCase						:							Optional[Any]         =					VOCAB_FILES_NAMES
 __lowerCamelCase						:							Any         =					PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase						:							List[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase						:							Tuple         =					["input_ids", "attention_mask"]




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase="<pad>"			,     _lowerCAmelCase="</s>"			,     _lowerCAmelCase="<unk>"			,     _lowerCAmelCase="<mask_2>"			,     _lowerCAmelCase="<mask_1>"			,     _lowerCAmelCase=None			,     _lowerCAmelCase=103			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							None:
     _lowerCAmelCase				=      offset
     if additional_special_tokens is not None:
         if not isinstance(_lowerCAmelCase			,     _lowerCAmelCase		):
             raise TypeError(
                 f'''additional_special_tokens should be of type {type(_lowerCAmelCase		)}, but is'''
                 f''' {type(_lowerCAmelCase		)}'''		)

         _lowerCAmelCase				=      (
             ([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(_lowerCAmelCase		)			,     self.offset - 1		)
         ]

         if len(set(_lowerCAmelCase		)		) != len(_lowerCAmelCase		):
             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}.'''		)
         _lowerCAmelCase				=      additional_special_tokens_extended
     else:
         _lowerCAmelCase				=      [mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f'''<unk_{i}>''' for i in range(2			,     self.offset		)]

     _lowerCAmelCase				=      {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=_lowerCAmelCase			,     unk_token=_lowerCAmelCase			,     mask_token=_lowerCAmelCase			,     pad_token=_lowerCAmelCase			,     mask_token_sent=_lowerCAmelCase			,     offset=_lowerCAmelCase			,     additional_special_tokens=_lowerCAmelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **_lowerCAmelCase			,     )
     _lowerCAmelCase				=      mask_token_sent
     _lowerCAmelCase				=      vocab_file
     _lowerCAmelCase				=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(_lowerCAmelCase		)

     # add special tokens to encoder dict
     _lowerCAmelCase				=      {
         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		)}		)

     _lowerCAmelCase				=      {v: k for k, v in self.encoder.items()}




 @property
 def 							_snake_case   (	self		)    ->							int:
     return len(self.sp_model		) + self.offset




 def 							_snake_case   (	self		)    ->							Dict[str, int]:
     _lowerCAmelCase				=      {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		)    ->							Any:
     _lowerCAmelCase				=      self.__dict__.copy()
     _lowerCAmelCase				=      None
     return state




 def __setstate__(	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _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 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[str]:
     return self.sp_model.encode(_lowerCAmelCase			,     out_type=_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							int:
     if token in self.decoder:
         return self.decoder[token]
     elif token in self.added_tokens_decoder:
         return self.added_tokens_decoder[token]
     _lowerCAmelCase				=      self.sp_model.piece_to_id(_lowerCAmelCase		)
     return sp_id + self.offset




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     if index in self.encoder:
         return self.encoder[index]
     elif index in self.added_tokens_encoder:
         return self.added_tokens_encoder[index]
     else:
         _lowerCAmelCase				=      self.sp_model.IdToPiece(index - self.offset		)
     return token




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _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(_lowerCAmelCase		) + token
             _lowerCAmelCase				=      []
         else:
             current_sub_tokens.append(_lowerCAmelCase		)
     out_string += self.sp_model.decode(_lowerCAmelCase		)
     return out_string.strip()




 def 							_snake_case   (	self			,     _lowerCAmelCase=False		)    ->							Union[str, Any]:
     return 1




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = False		)    ->							List[int]:
     if already_has_special_tokens:
         return self._special_token_mask(_lowerCAmelCase		)
     elif token_ids_a is None:
         return self._special_token_mask(_lowerCAmelCase		) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a		) + [1]




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=None		)    ->							List[int]:
     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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							Tuple[str]:
     if not os.path.isdir(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase				=      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:
             _lowerCAmelCase				=      self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase		)

     return (out_vocab_file,)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


