'''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


snake_case__					:      Any				      =  logging.get_logger(__name__)

snake_case__					:      List[str]				      =  {'''vocab_file''': '''spiece.model'''}

snake_case__					:      List[str]				      =  {
    '''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
snake_case__					:      Optional[int]				      =  {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

snake_case__					:      List[str]				      =  '''▁'''






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:List[str]						   =		VOCAB_FILES_NAMES
  lowerCamelCase_		:Any						   =		PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase_		:Dict						   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase_		:str						   =		['''input_ids''', '''attention_mask''']


  def __init__(			self				,		snake_case_				,		snake_case_="</s>"				,		snake_case_="<unk>"				,		snake_case_="<pad>"				,		snake_case_=1_0_0				,		snake_case_=None				,		snake_case_ = None				,		snake_case_=True				,		**snake_case_				,		):




       '''simple docstring'''

       if extra_ids > 0 and additional_special_tokens is None:
            UpperCAmelCase_							:       List[str]       =				[F'''<extra_id_{i}>''' for i in range(snake_case_						)]
       elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra_id special tokens
            UpperCAmelCase_							:       Any       =				len(set(filter(lambda snake_case_						: bool('extra_id' in str(snake_case_						)						)				,		snake_case_						)						)						)
            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'						)

       UpperCAmelCase_							:       List[str]       =				legacy
       UpperCAmelCase_							:       Dict       =				{} if sp_model_kwargs is None else sp_model_kwargs

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

       UpperCAmelCase_							:       int       =				vocab_file
       UpperCAmelCase_							:       Tuple       =				extra_ids

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


  @staticmethod
  def _UpperCamelCase					(			snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
            UpperCAmelCase_							:       Optional[int]       =				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.'				,		snake_case_				,		)

       return max_model_length


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self.sp_model.get_piece_size() + self._extra_ids


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				{self.convert_ids_to_tokens(snake_case_						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None				,		snake_case_ = False						):




       '''simple docstring'''

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

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return list(
           set(filter(lambda snake_case_						: bool(re.search(R'<extra_id_\d+>'				,		snake_case_						)						) is not None				,		self.additional_special_tokens						)						)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return [self._convert_token_to_id(snake_case_						) for token in self.get_sentinel_tokens()]


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       if len(snake_case_						) > 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				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				[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				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self._add_eos_if_not_present(snake_case_						)
       if token_ids_a is None:
            return token_ids_a
       else:
            UpperCAmelCase_							:       Optional[Any]       =				self._add_eos_if_not_present(snake_case_						)
            return token_ids_a + token_ids_a


  def __getstate__(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.__dict__.copy()
       UpperCAmelCase_							:       Optional[Any]       =				None
       return state


  def __setstate__(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				d

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

       UpperCAmelCase_							:       Union[str, Any]       =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(self.vocab_file						)


  def _UpperCamelCase					(			self				,		snake_case_				,		**snake_case_						):




       '''simple docstring'''

       if not self.legacy:
            UpperCAmelCase_							:       Dict       =				SPIECE_UNDERLINE + text.replace(snake_case_				,		' '						)
       return super().tokenize(snake_case_				,		**snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_				,		**snake_case_						):




       '''simple docstring'''

       if not self.legacy:
            UpperCAmelCase_							:       Optional[int]       =				text.startswith(snake_case_						)
            if is_first:
                 UpperCAmelCase_							:       Any       =				text[1:]

       UpperCAmelCase_							:       Optional[Any]       =				self.sp_model.encode(snake_case_				,		out_type=snake_case_						)

       if not self.legacy and not is_first and not text.startswith(' '						) and tokens[0].startswith(snake_case_						):
            UpperCAmelCase_							:       Union[str, Any]       =				([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
       return tokens


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       if token.startswith('<extra_id_'						):
            UpperCAmelCase_							:       Dict       =				re.match(R'<extra_id_(\d+)>'				,		snake_case_						)
            UpperCAmelCase_							:       str       =				int(match.group(1						)						)
            return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       if index < self.sp_model.get_piece_size():
            UpperCAmelCase_							:       str       =				self.sp_model.IdToPiece(snake_case_						)
       else:
            UpperCAmelCase_							:       List[str]       =				F'''<extra_id_{self.vocab_size - 1 - index}>'''
       return token


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				[]
       UpperCAmelCase_							:       Union[str, Any]       =				''
       UpperCAmelCase_							:       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(snake_case_						) + token
                 UpperCAmelCase_							:       List[str]       =				True
                 UpperCAmelCase_							:       Optional[int]       =				[]
            else:
                 current_sub_tokens.append(snake_case_						)
                 UpperCAmelCase_							:       Any       =				False
       out_string += self.sp_model.decode(snake_case_						)
       return out_string.strip()






  def _UpperCamelCase					(			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_							:       int       =				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_							:       List[str]       =				self.sp_model.serialized_model_proto()
                 fi.write(snake_case_						)

       return (out_vocab_file,)







'''simple docstring'''


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				[0] * len(lowerCamelCase_       )

     for i in range(1     ,					len(lowerCamelCase_       )       ):
          # use last results for better performance - dynamic programming
          UpperCAmelCase_							:       List[Any]       =				prefix_result[i - 1]
          while j > 0 and input_string[i] != input_string[j]:
               UpperCAmelCase_							:       str       =				prefix_result[j - 1]

          if input_string[i] == input_string[j]:
               j += 1
          UpperCAmelCase_							:       Any       =				j

     return prefix_result


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     return max(prefix_function(lowerCamelCase_       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






