



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


a_      :      Tuple    =     '▁'

a_      :      Optional[Any]    =     {'vocab_file': 'spiece.model'}

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

a_      :      int    =     {
    'google/pegasus-xsum': 5_12,
}


a_      :      Tuple    =     logging.get_logger(__name__)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    int      						=					VOCAB_FILES_NAMES

 _lowercase   :    Optional[Any]      						=					VOCAB_FILES_NAMES
 _lowercase   :    Dict      						=					PRETRAINED_VOCAB_FILES_MAP
 _lowercase   :    Optional[Any]      						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowercase   :    int      						=					['''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=1_0_3		,				SCREAMING_SNAKE_CASE = None		,				**SCREAMING_SNAKE_CASE		,				) ->			None:
  a__												= 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__												= (
       ([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__												= additional_special_tokens_extended
  else:
   a__												= [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__												= {} 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__												= mask_token_sent
  a__												= vocab_file
  a__												= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
  self.sp_model.Load(SCREAMING_SNAKE_CASE      )

  # add special tokens to encoder dict
  a__												= {
      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__												= {v: k for k, v in self.encoder.items()}

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

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

 def __setstate__(	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= d

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

  a__												= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
  self.sp_model.Load(self.vocab_file      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  return self.sp_model.encode(SCREAMING_SNAKE_CASE		,				out_type=SCREAMING_SNAKE_CASE      )

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

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			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__												= self.sp_model.IdToPiece(index - self.offset      )
  return token

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= []
  a__												= ''''''
  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__												= []
   else:
    current_sub_tokens.append(SCREAMING_SNAKE_CASE      )
  out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      )
  return out_string.strip()

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=False      ) ->			Dict:
  return 1

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  a__												= 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 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = 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 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=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 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			Tuple[str]:
  if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
   logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
   return
  a__												= 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__												= self.sp_model.serialized_model_proto()
    fi.write(SCREAMING_SNAKE_CASE      )

  return (out_vocab_file,)










a_      :      str    =     6_55_21






def     __a					(		__UpperCAmelCase       ):
 a__												= 1
 a__												= 0
 for plain_chr in plain_text:
  a__												= (a + ord(__UpperCAmelCase       )) % MOD_ADLER
  a__												= (b + a) % MOD_ADLER
 return (b << 16) | a





