


'''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


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

a_   :      Optional[Any]				    =      "▁"

a_   :      int				    =      {"vocab_file": "sentencepiece.bpe.model"}

a_   :      List[str]				    =      {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

a_   :      Optional[int]				    =      {
    "facebook/xglm-564M": 2_0_4_8,
}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="</s>" ,			__magic_name__="<s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      # Compatibility with the original tokenizer
      _a      =							7
      _a      =							[f'<madeupword{i}>' for i in range(self.num_madeup_words  )]

      _a      =							kwargs.get('additional_special_tokens' ,			[]  )
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			sep_token=__magic_name__ ,			cls_token=__magic_name__ ,			pad_token=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(str(__magic_name__  )  )
      _a      =							vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      _a      =							1

      # Mimic fairseq token-to-id alignment for the first 4 token
      _a      =							{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

      _a      =							len(self.sp_model  )
      _a      =							{f'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words  )}
      self.fairseq_tokens_to_ids.update(__magic_name__  )

      _a      =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(	self  )   ->							Dict:
      _a      =							self.__dict__.copy()
      _a      =							None
      _a      =							self.sp_model.serialized_model_proto()
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							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.LoadFromSerializedProto(self.sp_model_proto  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:

      if token_ids_a is None:
           return [self.sep_token_id] + token_ids_a
      _a      =							[self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = False  )   ->							List[int]:

      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__magic_name__ ,			token_ids_a=__magic_name__ ,			already_has_special_tokens=__magic_name__  )

      if token_ids_a is None:
           return [1] + ([0] * len(__magic_name__  ))
      return [1] + ([0] * len(__magic_name__  )) + [1, 1] + ([0] * len(__magic_name__  ))
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:
      _a      =							[self.sep_token_id]

      if token_ids_a is None:
           return len(sep + token_ids_a  ) * [0]
      return len(sep + token_ids_a + sep + sep + token_ids_a  ) * [0]
 @property
 def 							__UpperCAmelCase						(	self  )   ->							int:
      return len(self.sp_model  ) + self.fairseq_offset + self.num_madeup_words
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
      _a      =							self.sp_model.PieceToId(__magic_name__  )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[int]:
      _a      =							''.join(__magic_name__  ).replace(__magic_name__ ,			' '  ).strip()
      return out_string




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(__magic_name__  ) and os.path.isfile(self.vocab_file  ):
           copyfile(self.vocab_file ,			__magic_name__  )
      elif not os.path.isfile(self.vocab_file  ):
           with open(__magic_name__ ,			'wb'  ) as fi:
                _a      =							self.sp_model.serialized_model_proto()
                fi.write(__magic_name__  )

      return (out_vocab_file,)





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list[int]			,  lowerCAmelCase__					:list[int]						)				->					None:




     '''simple docstring'''



     _a      =							len(lowerCAmelCase__						)
     print('The following activities are selected:'						)

     # The first activity is always selected
     _a      =							0
     print(lowerCAmelCase__			,  end=','						)

     # Consider rest of the activities
     for j in range(lowerCAmelCase__						):
          # 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(lowerCAmelCase__			,  end=','						)
               _a      =							j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a_   :      List[str]				    =      [1, 3, 0, 5, 8, 5]
 a_   :      str				    =      [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)

