





"""simple docstring"""


def 				a__      (	_UpperCAmelCase						)     ->	int:

 '''simple docstring'''






 snake_case__     				=					len(UpperCAmelCase_						)
 snake_case__     				=					[0] * length
 snake_case__     				=					[[False for i in range(UpperCAmelCase_						)] for j in range(UpperCAmelCase_						)]
 for i, c in enumerate(UpperCAmelCase_						):
  snake_case__     				=					i
  for j in range(i + 1						):
   if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
    snake_case__     				=					True
    snake_case__     				=					min(UpperCAmelCase_						,  0 if j == 0 else (cut[j - 1] + 1)						)
  snake_case__     				=					mincut
 return cut[length - 1]


if __name__ == "__main__":
   a__       :  int       		=					input("""Enter the string: """).strip()
   a__       :  int       		=					find_minimum_partitions(s)
   print(f'''Minimum number of partitions required for the \'{s}\' is {ans}''')







'''simple docstring'''





import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  a		:	Optional[int]       =      None


a		:	Optional[Any]       =      logging.get_logger(__name__)


a		:	Union[str, Any]       =      '''▁'''

a		:	Any       =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:	List[Any]       =      {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

a		:	Any       =      {
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}




class      UpperCamelCase       (	snake_case__       ):
  a     :str          =					VOCAB_FILES_NAMES
  a     :List[Any]          =					PRETRAINED_VOCAB_FILES_MAP
  a     :Optional[int]          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     :Any          =					['input_ids', 'attention_mask']
  a     :int          =					ReformerTokenizer



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase="</s>"		,     __lowerCAmelCase="<unk>"		,     __lowerCAmelCase=[]		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(
          __lowerCAmelCase		,     tokenizer_file=__lowerCAmelCase		,     eos_token=__lowerCAmelCase		,     unk_token=__lowerCAmelCase		,     additional_special_tokens=__lowerCAmelCase		,     **__lowerCAmelCase		,     )

      _a				:							List[str]							       =      vocab_file
      _a				:							Optional[int]							       =      False if not self.vocab_file else True



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      if not self.can_save_slow_tokenizer:
          raise ValueError(
              """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
              """tokenizer.""" )

      if not os.path.isdir(__lowerCAmelCase ):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
          return
      _a				:							List[str]							       =      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 ):
          copyfile(self.vocab_file		,     __lowerCAmelCase )

      return (out_vocab_file,)


