


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


UpperCAmelCase_				:			str   =	'▁'

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

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

UpperCAmelCase_				:			List[str]   =	{
    'google/pegasus-xsum': 512,
}


UpperCAmelCase_				:			str   =	logging.get_logger(__name__)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					List[Any]					       =						VOCAB_FILES_NAMES

      snake_case__    :					Tuple					       =						VOCAB_FILES_NAMES
      snake_case__    :					Union[str, Any]					       =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :					Any					       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :					Tuple					       =						['''input_ids''', '''attention_mask''']
      def __init__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="<pad>"					,							SCREAMING_SNAKE_CASE__    :	Tuple="</s>"					,							SCREAMING_SNAKE_CASE__    :	str="<unk>"					,							SCREAMING_SNAKE_CASE__    :	Optional[int]="<mask_2>"					,							SCREAMING_SNAKE_CASE__    :	Optional[int]="<mask_1>"					,							SCREAMING_SNAKE_CASE__    :	str=None					,							SCREAMING_SNAKE_CASE__    :	int=1_0_3					,							SCREAMING_SNAKE_CASE__    :	Optional[Dict[str, Any]] = None					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					,							)		->      None:
             a_ :		List[str]							=  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_ :		int							=  (
                        ([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_ :		Dict							=  additional_special_tokens_extended
             else:
                    a_ :		Union[str, Any]							=  [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_ :		Optional[int]							=  {} 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_ :		Optional[Any]							=  mask_token_sent
             a_ :		Optional[int]							=  vocab_file
             a_ :		Any							=  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 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      int:
             return len(self.sp_model					) + self.offset
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Dict[str, int]:
             a_ :		Tuple							=  {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    :	Tuple					)		->      Tuple:
             a_ :		int							=  self.__dict__.copy()
             a_ :		Optional[Any]							=  None
             return state
      def __setstate__(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	Any					)		->      List[str]:
             a_ :		Optional[int]							=  d

             # for backward compatibility
             if not hasattr(self					,							'sp_model_kwargs'					):
                    a_ :		List[Any]							=  {}

             a_ :		Optional[int]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.Load(self.vocab_file					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	str					)		->      List[str]:
             return self.sp_model.encode(SCREAMING_SNAKE_CASE__					,							out_type=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	str					)		->      int:
             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 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	int					)		->      str:
             if index in self.encoder:
                    return self.encoder[index]
             elif index in self.added_tokens_encoder:
                    return self.added_tokens_encoder[index]
             else:
                    a_ :		Dict							=  self.sp_model.IdToPiece(index - self.offset					)
             return token
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      List[Any]:
             a_ :		str							=  []
             a_ :		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(SCREAMING_SNAKE_CASE__					) + token
                           a_ :		int							=  []
                    else:
                           current_sub_tokens.append(SCREAMING_SNAKE_CASE__					)
             out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__					)
             return out_string.strip()
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	int=False					)		->      Any:
             return 1
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Dict					)		->      Union[str, Any]:
             a_ :		Union[str, Any]							=  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 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	List					,							SCREAMING_SNAKE_CASE__    :	Optional[List] = None					,							SCREAMING_SNAKE_CASE__    :	bool = False					)		->      List[int]:
             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 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=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 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[str] = None					)		->      Tuple[str]:
             if not os.path.isdir(SCREAMING_SNAKE_CASE__					):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
                    return
             a_ :		Optional[int]							=  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_ :		Union[str, Any]							=  self.sp_model.serialized_model_proto()
                           fi.write(SCREAMING_SNAKE_CASE__					)

             return (out_vocab_file,)






def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		int:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input must be an integer'    )
       if input_num <= 0:
              raise ValueError('Input must be positive'    )
       return sum(
           divisor for divisor in range(1  ,   input_num // 2 + 1    ) if input_num % divisor == 0    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


