


'''simple docstring'''






import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
 from ...tokenization_utils_base import TextInput
from ...utils import logging


a_   :      Tuple				    =      logging.get_logger(__name__)

a_   :      Dict				    =      {"vocab_file": "spiece.model"}

a_   :      Tuple				    =      {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
a_   :      Optional[int]				    =      {
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}

a_   :      List[str]				    =      "▁"




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__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__=1_00 ,			__magic_name__=None ,			__magic_name__ = None ,			__magic_name__=True ,			**__magic_name__ ,			)   ->							None:
      # Add extra_ids to the special token list
      if extra_ids > 0 and additional_special_tokens is None:
           _a      =							[f'<extra_id_{i}>' for i in range(__magic_name__  )]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           _a      =							len(set(filter(lambda __magic_name__  : bool('extra_id' in str(__magic_name__  )  ) ,			__magic_name__  )  )  )
           if extra_tokens != extra_ids:
                raise ValueError(
                    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                    ' tokens'  )
      if legacy:
           logger.warning_once(
               f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'  )

      _a      =							legacy
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			pad_token=__magic_name__ ,			extra_ids=__magic_name__ ,			additional_special_tokens=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			legacy=__magic_name__ ,			**__magic_name__ ,			)

      _a      =							vocab_file
      _a      =							extra_ids

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(__magic_name__  )
 @staticmethod
 def 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           _a      =							TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
           elif init_max_model_length is None:
                warnings.warn(
                    'This tokenizer was incorrectly instantiated with a model max length of'
                    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                    f' {pretrained_model_name_or_path} automatically truncating your input to'
                    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                    ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,			__magic_name__ ,			)

      return max_model_length
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      return self.sp_model.get_piece_size() + self._extra_ids
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _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__ ,			__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__  )

      # normal case: some special tokens
      if token_ids_a is None:
           return ([0] * len(__magic_name__  )) + [1]
      return ([0] * len(__magic_name__  )) + [1] + ([0] * len(__magic_name__  )) + [1]
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return list(
          set(filter(lambda __magic_name__  : bool(re.search(r'<extra_id_\d+>' ,			__magic_name__  )  ) is not None ,			self.additional_special_tokens  )  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      return [self._convert_token_to_id(__magic_name__  ) for token in self.get_sentinel_tokens()]
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[int]:
      if len(__magic_name__  ) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
               ' eos tokens being added.'  )
           return token_ids
      else:
           return token_ids + [self.eos_token_id]
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:
      _a      =							[self.eos_token_id]

      if token_ids_a is None:
           return len(token_ids_a + eos  ) * [0]
      return len(token_ids_a + eos + token_ids_a + eos  ) * [0]
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:
      _a      =							self._add_eos_if_not_present(__magic_name__  )
      if token_ids_a is None:
           return token_ids_a
      else:
           _a      =							self._add_eos_if_not_present(__magic_name__  )
           return token_ids_a + token_ids_a
 def __getstate__(	self  )   ->							str:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							Tuple:
      _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 ,			__magic_name__ ,			**__magic_name__  )   ->							List[str]:
      # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
      # the beginning of the text
      if not self.legacy:
           _a      =							SPIECE_UNDERLINE + text.replace(__magic_name__ ,			' '  )
      return super().tokenize(__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			**__magic_name__  )   ->							str:
      if not self.legacy:
           _a      =							text.startswith(__magic_name__  )
           if is_first:
                _a      =							text[1:]

      _a      =							self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )

      if not self.legacy and not is_first and not text.startswith(' '  ) and tokens[0].startswith(__magic_name__  ):
           _a      =							([tokens[0][1:]] if len(tokens[0]  ) > 1 else []) + tokens[1:]
      return tokens
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Tuple:
      if token.startswith('<extra_id_'  ):
           _a      =							re.match(r'<extra_id_(\d+)>' ,			__magic_name__  )
           _a      =							int(match.group(1  )  )
           return self.vocab_size - num - 1
      return self.sp_model.piece_to_id(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[Any]:
      if index < self.sp_model.get_piece_size():
           _a      =							self.sp_model.IdToPiece(__magic_name__  )
      else:
           _a      =							f'<extra_id_{self.vocab_size - 1 - index}>'
      return token
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							int:
      _a      =							[]
      _a      =							''
      _a      =							False
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                if not prev_is_special:
                     out_string += " "
                out_string += self.sp_model.decode(__magic_name__  ) + token
                _a      =							True
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
                _a      =							False
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()




 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__					:int						)				->					int:




     '''simple docstring'''



     assert (
         isinstance(lowerCAmelCase__			,  lowerCAmelCase__						) and number_of_steps > 0
     ), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
     if number_of_steps == 1:
          return 1
     _a ,				_a      =							1, 1
     for _ in range(number_of_steps - 1						):
          _a ,				_a      =							current + previous, current
     return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()

