'''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_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			{'vocab_file': 'spiece.model'}

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

SCREAMING_SNAKE_CASE_	   =			{'bert_for_seq_generation': 5_12}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					VOCAB_FILES_NAMES
  __lowerCAmelCase				      =					PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase				      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase				      =					[]
  __lowerCAmelCase				      =					["""input_ids""", """attention_mask"""]







  def __init__(							self   ,    snake_case_   ,    snake_case_="<s>"   ,    snake_case_="</s>"   ,    snake_case_="<unk>"   ,    snake_case_="<pad>"   ,    snake_case_="<::::>"   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= {} 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_   ,    )

    __UpperCAmelCase:					Tuple		= vocab_file

    __UpperCAmelCase:					Optional[Any]		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(snake_case_					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.sp_model.get_piece_size()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= {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					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= self.__dict__.copy()
    __UpperCAmelCase:					List[str]		= None
    return state







  def __setstate__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= d

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

    __UpperCAmelCase:					Optional[Any]		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(self.vocab_file					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.sp_model.piece_to_id(snake_case_					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= self.sp_model.IdToPiece(snake_case_					)
    return token







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= []
    __UpperCAmelCase:					Any		= """"""
    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
        __UpperCAmelCase:					int		= []
      else:
        current_sub_tokens.append(snake_case_					)
    out_string += self.sp_model.decode(snake_case_					)
    return out_string.strip()







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None					):

    '''simple docstring'''







    if not os.path.isdir(snake_case_					):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
      return
    __UpperCAmelCase:					Tuple		= 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:
        __UpperCAmelCase:					Any		= self.sp_model.serialized_model_proto()
        fi.write(snake_case_					)

    return (out_vocab_file,)
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)