






"""simple docstring"""



import torch

from transformers import AutoTokenizer

class 	a					(       a_     ):







    def __init__(      self							,		_lowerCamelCase	):
      lowercase							   =	AutoTokenizer.from_pretrained(_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):

      if isinstance(_lowerCamelCase							,		_lowerCamelCase	) and all(isinstance(_x							,		_lowerCamelCase	) for _x in x	):
        lowercase							   =	None
        for l in x:
          lowercase							   =	self.tokenizer(
              _lowerCamelCase							,		padding='max_length'							,		max_length=3_8_4							,		truncation=_lowerCamelCase							,		return_tensors='pt'							,		)
          lowercase							   =	torch.tensor([len(_lowerCamelCase	)]	)
          if d is not None:
            for k in d.keys():
              lowercase							   =	torch.cat((d[k], t[k])							,		0	)
          else:
            lowercase							   =	t

        lowercase							   =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'	)	)
        lowercase							   =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'	)	)

      elif isinstance(_lowerCamelCase							,		_lowerCamelCase	) and all(isinstance(_x							,		_lowerCamelCase	) for _x in x	):
        lowercase							   =	self.tokenizer(
            _lowerCamelCase							,		padding='max_length'							,		max_length=3_8_4							,		truncation=_lowerCamelCase							,		return_tensors='pt'							,		)

      else:
        raise Exception(
            'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
            ' strings` for supports are supported.'	)

      return d







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=0.7_0	):
      lowercase							   =	[]
      for idx in range(len(W_query['input_ids']	)	):
        lowercase							   =	lowercase							   =	range(p_start.shape[1]	)

        lowercase							   =	[]
        for start_id in start_indexes:
          for end_id in end_indexes:
            if start_id < end_id:
              output.append(
                  (
                      start_id,
                      end_id,
                      p_start[idx][start_id].item(),
                      p_end[idx][end_id].item(),
                  )	)

        output.sort(key=lambda _lowerCamelCase	: (tup[2] * tup[3])							,		reverse=_lowerCamelCase	)
        lowercase							   =	[]
        for k in range(len(_lowerCamelCase	)	):
          if output[k][2] * output[k][3] >= thresh:
            lowercase   ,					lowercase							   =	output[k][0], output[k][1]
            lowercase							   =	self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]	)
            temp.append((decoded, output[k][2] * output[k][3])	)

        final_outputs.append(_lowerCamelCase	)

      return final_outputs










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  str   ):





  '''simple docstring'''





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


if __name__ == "__main__":
    _UpperCamelCase		:  List[Any]						=		input('Enter the string: ').strip()
    _UpperCamelCase		:  List[Any]						=		find_minimum_partitions(s)
    print(F'''Minimum number of partitions required for the \'{s}\' is {ans}''')


