


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


UpperCamelCase					:       Tuple			    =     logging.get_logger(__name__)

UpperCamelCase					:       Optional[Any]			    =     {"""vocab_file""": """spiece.model"""}

UpperCamelCase					:       int			    =     {
    """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
UpperCamelCase					:       Tuple			    =     {
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

UpperCamelCase					:       Optional[Any]			    =     """▁"""



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		VOCAB_FILES_NAMES
  _lowercase									=		PRETRAINED_VOCAB_FILES_MAP
  _lowercase									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase									=		['input_ids', 'attention_mask']

  def __init__( self			:  str				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  Union[str, Any]="</s>"				,				lowerCamelCase__			:  Optional[int]="<unk>"				,				lowerCamelCase__			:  Tuple="<pad>"				,				lowerCamelCase__			:  str=100				,				lowerCamelCase__			:  int=None				,				lowerCamelCase__			:  Optional[Dict[str, Any]] = None				,				lowerCamelCase__			:  Optional[Any]=True				,				**lowerCamelCase__			:  Tuple				,				):
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
              a__	:  Tuple     =   [f'''<extra_id_{i}>''' for i in range(lowerCamelCase__       )]
        elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              a__	:  int     =   len(set(filter(lambda lowerCamelCase__       : bool("extra_id" in str(lowerCamelCase__       )       )				,				lowerCamelCase__       )       )       )
              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__	:  Optional[Any]     =   legacy
        a__	:  List[str]     =   {} if sp_model_kwargs is None else sp_model_kwargs

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

        a__	:  Any     =   vocab_file
        a__	:  List[Any]     =   extra_ids

        a__	:  int     =   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.Load(lowerCamelCase__       )

  @staticmethod
  def    _UpperCamelCase( lowerCamelCase__			:  Tuple				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  List[Any]       ):
        if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
              a__	:  List[Any]     =   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."				,				lowerCamelCase__				,				)

        return max_model_length

  @property
  def    _UpperCamelCase( self			:  List[Any]       ):
        return self.sp_model.get_piece_size() + self._extra_ids

  def    _UpperCamelCase( self			:  str       ):
        a__	:  Tuple     =   {self.convert_ids_to_tokens(lowerCamelCase__       ): i for i in range(self.vocab_size       )}
        vocab.update(self.added_tokens_encoder       )
        return vocab

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None				,				lowerCamelCase__			:  bool = False       ):
        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=lowerCamelCase__				,				token_ids_a=lowerCamelCase__				,				already_has_special_tokens=lowerCamelCase__       )

        # normal case: some special tokens
        if token_ids_a is None:
              return ([0] * len(lowerCamelCase__       )) + [1]
        return ([0] * len(lowerCamelCase__       )) + [1] + ([0] * len(lowerCamelCase__       )) + [1]

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        return list(
            set(filter(lambda lowerCamelCase__       : bool(re.search(r"<extra_id_\d+>"				,				lowerCamelCase__       )       ) is not None				,				self.additional_special_tokens       )       )       )

  def    _UpperCamelCase( self			:  int       ):
        return [self._convert_token_to_id(lowerCamelCase__       ) for token in self.get_sentinel_tokens()]

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  List[int]       ):
        if len(lowerCamelCase__       ) > 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			:  int				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None       ):
        a__	:  str     =   [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			:  str				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None       ):
        a__	:  int     =   self._add_eos_if_not_present(lowerCamelCase__       )
        if token_ids_a is None:
              return token_ids_a
        else:
              a__	:  str     =   self._add_eos_if_not_present(lowerCamelCase__       )
              return token_ids_a + token_ids_a

  def __getstate__( self			:  Any       ):
        a__	:  Tuple     =   self.__dict__.copy()
        a__	:  Optional[Any]     =   None
        return state

  def __setstate__( self			:  int				,				lowerCamelCase__			:  Tuple       ):
        a__	:  int     =   d

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

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

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  "TextInput"				,				**lowerCamelCase__			:  Union[str, Any]       ):
        # 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__	:  Dict     =   SPIECE_UNDERLINE + text.replace(lowerCamelCase__				,				" "       )
        return super().tokenize(lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  Any				,				**lowerCamelCase__			:  Optional[int]       ):
        if not self.legacy:
              a__	:  Optional[Any]     =   text.startswith(lowerCamelCase__       )
              if is_first:
                    a__	:  Optional[int]     =   text[1:]

        a__	:  Dict     =   self.sp_model.encode(lowerCamelCase__				,				out_type=lowerCamelCase__       )

        if not self.legacy and not is_first and not text.startswith(" "       ) and tokens[0].startswith(lowerCamelCase__       ):
              a__	:  Optional[Any]     =   ([tokens[0][1:]] if len(tokens[0]       ) > 1 else []) + tokens[1:]
        return tokens

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  str       ):
        if token.startswith("<extra_id_"       ):
              a__	:  int     =   re.match(r"<extra_id_(\d+)>"				,				lowerCamelCase__       )
              a__	:  Optional[int]     =   int(match.group(1       )       )
              return self.vocab_size - num - 1
        return self.sp_model.piece_to_id(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  Union[str, Any]       ):
        if index < self.sp_model.get_piece_size():
              a__	:  Union[str, Any]     =   self.sp_model.IdToPiece(lowerCamelCase__       )
        else:
              a__	:  Tuple     =   f'''<extra_id_{self.vocab_size - 1 - index}>'''
        return token

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  str       ):
        a__	:  str     =   []
        a__	:  Optional[int]     =   ""
        a__	:  int     =   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(lowerCamelCase__       ) + token
                    a__	:  Dict     =   True
                    a__	:  Optional[int]     =   []
              else:
                    current_sub_tokens.append(lowerCamelCase__       )
                    a__	:  int     =   False
        out_string += self.sp_model.decode(lowerCamelCase__       )
        return out_string.strip()

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Optional[str] = None       ):
        if not os.path.isdir(lowerCamelCase__       ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
              return
        a__	:  int     =   os.path.join(
            lowerCamelCase__				,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

        if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCamelCase__       ) and os.path.isfile(self.vocab_file       ):
              copyfile(self.vocab_file				,				lowerCamelCase__       )
        elif not os.path.isfile(self.vocab_file       ):
              with open(lowerCamelCase__				,				"wb"       ) as fi:
                    a__	:  Optional[int]     =   self.sp_model.serialized_model_proto()
                    fi.write(lowerCamelCase__       )

        return (out_vocab_file,)


def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return x if y == 0 else greatest_common_divisor(_A				,					x % y       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (x * y) // greatest_common_divisor(_A				,					_A       )



def 	__A		(      _A = 20       ):



 """simple docstring"""


 __a							=	1
 for i in range(1				,					n + 1       ):
  __a							=	lcm(_A				,					_A       )
 return g


if __name__ == "__main__":
  print(f'''{solution() = }''')


