



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     :  Any					     =       logging.get_logger(__name__)

A     :  Any					     =       {'''vocab_file''': '''spiece.model'''}

A     :  Optional[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
A     :  List[str]					     =       {
    '''t5-small''': 5_12,
    '''t5-base''': 5_12,
    '''t5-large''': 5_12,
    '''t5-3b''': 5_12,
    '''t5-11b''': 5_12,
}

A     :  str					     =       '''▁'''


class   lowerCamelCase					(UpperCAmelCase__					):
  """simple docstring"""







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







  def __init__(							self :       Any  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       List[Any]="</s>"  ,     __magic_name__ :       Any="<unk>"  ,     __magic_name__ :       Dict="<pad>"  ,     __magic_name__ :       int=100  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Optional[Dict[str, Any]] = None  ,     __magic_name__ :       Tuple=True  ,     **__magic_name__ :       Tuple  ,     )  ->	None:






        if extra_ids > 0 and additional_special_tokens is None:
              SCREAMING_SNAKE_CASE_          =  [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
              SCREAMING_SNAKE_CASE_          =  len(set(filter(lambda __magic_name__   : 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"   )

        SCREAMING_SNAKE_CASE_          =  legacy
        SCREAMING_SNAKE_CASE_          =  {} 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  ,     )

        SCREAMING_SNAKE_CASE_          =  vocab_file
        SCREAMING_SNAKE_CASE_          =  extra_ids

        SCREAMING_SNAKE_CASE_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.Load(__lowerCAmelCase   )







  @staticmethod
  def    __A     (							__magic_name__ :       str  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Optional[int]   )  ->	Any:






        if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
              SCREAMING_SNAKE_CASE_          =  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    __A     (							self :       int   )  ->	Union[str, Any]:
        return self.sp_model.get_piece_size() + self._extra_ids







  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  {self.convert_ids_to_tokens(__lowerCAmelCase   ): i for i in range(self.vocab_size   )}
        vocab.update(self.added_tokens_encoder   )
        return vocab







  def    __A     (							self :       Tuple  ,     __magic_name__ :       List[int]  ,     __magic_name__ :       Optional[List[int]] = None  ,     __magic_name__ :       bool = False   )  ->	List[int]:






        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    __A     (							self :       Dict   )  ->	int:
        return list(
            set(filter(lambda __magic_name__   : bool(re.search(r"<extra_id_\d+>"  ,     __lowerCAmelCase   )   ) is not None  ,     self.additional_special_tokens   )   )   )







  def    __A     (							self :       str   )  ->	List[str]:
        return [self._convert_token_to_id(__lowerCAmelCase   ) for token in self.get_sentinel_tokens()]







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       List[int]   )  ->	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    __A     (							self :       Dict  ,     __magic_name__ :       List[int]  ,     __magic_name__ :       Optional[List[int]] = None   )  ->	List[int]:
        SCREAMING_SNAKE_CASE_          =  [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    __A     (							self :       List[str]  ,     __magic_name__ :       List[int]  ,     __magic_name__ :       Optional[List[int]] = None   )  ->	List[int]:
        SCREAMING_SNAKE_CASE_          =  self._add_eos_if_not_present(__lowerCAmelCase   )
        if token_ids_a is None:
              return token_ids_a
        else:
              SCREAMING_SNAKE_CASE_          =  self._add_eos_if_not_present(__lowerCAmelCase   )
              return token_ids_a + token_ids_a







  def __getstate__(							self :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.__dict__.copy()
        SCREAMING_SNAKE_CASE_          =  None
        return state







  def __setstate__(							self :       str  ,     __magic_name__ :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  d

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

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







  def    __A     (							self :       Any  ,     __magic_name__ :       "TextInput"  ,     **__magic_name__ :       Dict   )  ->	List[str]:






        if not self.legacy:
              SCREAMING_SNAKE_CASE_          =  SPIECE_UNDERLINE + text.replace(__lowerCAmelCase  ,     " "   )
        return super().tokenize(__lowerCAmelCase  ,     **__lowerCAmelCase   )







  def    __A     (							self :       List[Any]  ,     __magic_name__ :       Tuple  ,     **__magic_name__ :       Dict   )  ->	Dict:






        if not self.legacy:
              SCREAMING_SNAKE_CASE_          =  text.startswith(__lowerCAmelCase   )
              if is_first:
                    SCREAMING_SNAKE_CASE_          =  text[1:]

        SCREAMING_SNAKE_CASE_          =  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   ):
              SCREAMING_SNAKE_CASE_          =  ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
        return tokens







  def    __A     (							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	Union[str, Any]:






        if token.startswith("<extra_id_"   ):
              SCREAMING_SNAKE_CASE_          =  re.match(r"<extra_id_(\d+)>"  ,     __lowerCAmelCase   )
              SCREAMING_SNAKE_CASE_          =  int(match.group(1   )   )
              return self.vocab_size - num - 1
        return self.sp_model.piece_to_id(__lowerCAmelCase   )







  def    __A     (							self :       List[Any]  ,     __magic_name__ :       Optional[Any]   )  ->	str:






        if index < self.sp_model.get_piece_size():
              SCREAMING_SNAKE_CASE_          =  self.sp_model.IdToPiece(__lowerCAmelCase   )
        else:
              SCREAMING_SNAKE_CASE_          =  F'''<extra_id_{self.vocab_size - 1 - index}>'''
        return token







  def    __A     (							self :       Dict  ,     __magic_name__ :       str   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  []
        SCREAMING_SNAKE_CASE_          =  ""
        SCREAMING_SNAKE_CASE_          =  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
                    SCREAMING_SNAKE_CASE_          =  True
                    SCREAMING_SNAKE_CASE_          =  []
              else:
                    current_sub_tokens.append(__lowerCAmelCase   )
                    SCREAMING_SNAKE_CASE_          =  False
        out_string += self.sp_model.decode(__lowerCAmelCase   )
        return out_string.strip()







  def    __A     (							self :       Dict  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[str] = None   )  ->	Tuple[str]:






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

        return (out_vocab_file,)



from __future__ import annotations


def      a__ (		__UpperCamelCase				):

      # preprocessing the first row
      for i in range(1     ,      len(matrix[0]				)				):
            matrix[0][i] += matrix[0][i - 1]

      # preprocessing the first column
      for i in range(1     ,      len(__UpperCamelCase				)				):
            matrix[i][0] += matrix[i - 1][0]

      # updating the path cost for current position
      for i in range(1     ,      len(__UpperCamelCase				)				):
            for j in range(1     ,      len(matrix[0]				)				):
                  matrix[i][j] += min(matrix[i - 1][j]     ,      matrix[i][j - 1]				)

      return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()


