

import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__a						:Tuple            = logging.get_logger(__name__)

@dataclass
class      _a				:




      """simple docstring"""

      _lowerCamelCase					: str
      _lowerCamelCase					: str
      _lowerCamelCase					: Optional[str]					=					None
      _lowerCamelCase					: Optional[str]					=					None





      def 		__A  (      self       :							Optional[int]  ):
            return json.dumps(dataclasses.asdict(self  )	, indent=2  ) + "\n"




@dataclass(frozen=snake_case_							)
class      _a				:




      """simple docstring"""

      _lowerCamelCase					: List[int]
      _lowerCamelCase					: Optional[List[int]]					=					None
      _lowerCamelCase					: Optional[List[int]]					=					None
      _lowerCamelCase					: Optional[Union[int, float]]					=					None





      def 		__A  (      self       :							List[str]  ):
            return json.dumps(dataclasses.asdict(self  )  ) + "\n"




class      _a				:




      """simple docstring"""

      def 		__A  (      self       :							Any	, UpperCAmelCase       :							Any  ):
            raise NotImplementedError()





      def 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]  ):
            raise NotImplementedError()





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							List[str]  ):
            raise NotImplementedError()





      def 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							str  ):
            raise NotImplementedError()





      def 		__A  (      self       :							int  ):
            raise NotImplementedError()





      def 		__A  (      self       :							Tuple	, UpperCAmelCase       :							Any  ):
            if len(self.get_labels()  ) > 1:
                  A_	     =							self.get_labels()[int(example.label  )]
            return example







      @classmethod
      def 		__A  (      cls       :							str	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Any=None  ):
            with open(UpperCAmelCase	, "r"	, encoding="utf-8-sig"  ) as f:
                  return list(csv.reader(UpperCAmelCase	, delimiter="\t"	, quotechar=UpperCAmelCase  )  )




class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							List[str]	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							List[Any]=None	, UpperCAmelCase       :							Tuple="classification"	, UpperCAmelCase       :							Tuple=False  ):
            A_	     =							[] if labels is None else labels
            A_	     =							[] if examples is None else examples
            A_	     =							mode
            A_	     =							verbose





      def __len__(      self       :							Tuple  ):
            return len(self.examples  )





      def __getitem__(      self       :							Union[str, Any]	, UpperCAmelCase       :							int  ):
            if isinstance(UpperCAmelCase	, UpperCAmelCase  ):
                  return SingleSentenceClassificationProcessor(labels=self.labels	, examples=self.examples[idx]  )
            return self.examples[idx]





      @classmethod
      def 		__A  (      cls       :							Tuple	, UpperCAmelCase       :							int	, UpperCAmelCase       :							Optional[int]=""	, UpperCAmelCase       :							Tuple=0	, UpperCAmelCase       :							Optional[Any]=1	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							Union[str, Any]=False	, **UpperCAmelCase       :							Optional[Any]  ):
            A_	     =							cls(**UpperCAmelCase  )
            processor.add_examples_from_csv(
                UpperCAmelCase	, split_name=UpperCAmelCase	, column_label=UpperCAmelCase	, column_text=UpperCAmelCase	, column_id=UpperCAmelCase	, skip_first_row=UpperCAmelCase	, overwrite_labels=UpperCAmelCase	, overwrite_examples=UpperCAmelCase	, )
            return processor





      @classmethod
      def 		__A  (      cls       :							Dict	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[int]=None	, **UpperCAmelCase       :							int  ):
            A_	     =							cls(**UpperCAmelCase  )
            processor.add_examples(UpperCAmelCase	, labels=UpperCAmelCase  )
            return processor





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Union[str, Any]=""	, UpperCAmelCase       :							Optional[int]=0	, UpperCAmelCase       :							List[str]=1	, UpperCAmelCase       :							Any=None	, UpperCAmelCase       :							Union[str, Any]=False	, UpperCAmelCase       :							List[str]=False	, UpperCAmelCase       :							Optional[int]=False	, ):
            A_	     =							self._read_tsv(UpperCAmelCase  )
            if skip_first_row:
                  A_	     =							lines[1:]
            A_	     =							[]
            A_	     =							[]
            A_	     =							[]
            for i, line in enumerate(UpperCAmelCase  ):
                  texts.append(line[column_text]  )
                  labels.append(line[column_label]  )
                  if column_id is not None:
                        ids.append(line[column_id]  )
                  else:
                        A_	     =							f'''{split_name}-{i}''' if split_name else str(UpperCAmelCase  )
                        ids.append(UpperCAmelCase  )

            return self.add_examples(
                UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, overwrite_labels=UpperCAmelCase	, overwrite_examples=UpperCAmelCase  )





      def 		__A  (      self       :							int	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							List[str]=None	, UpperCAmelCase       :							Optional[int]=False	, UpperCAmelCase       :							Dict=False  ):
            if labels is not None and len(UpperCAmelCase  ) != len(UpperCAmelCase  ):
                  raise ValueError(
                      f'''Text and labels have mismatched lengths {len(UpperCAmelCase  )} and {len(UpperCAmelCase  )}'''  )
            if ids is not None and len(UpperCAmelCase  ) != len(UpperCAmelCase  ):
                  raise ValueError(f'''Text and ids have mismatched lengths {len(UpperCAmelCase  )} and {len(UpperCAmelCase  )}'''  )
            if ids is None:
                  A_	     =							[None] * len(UpperCAmelCase  )
            if labels is None:
                  A_	     =							[None] * len(UpperCAmelCase  )
            A_	     =							[]
            A_	     =							set()
            for text_or_text_and_label, label, guid in zip(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  ):
                  if isinstance(UpperCAmelCase	, (tuple, list)  ) and label is None:
                        A_     ,    A_	     =							text_or_text_and_label
                  else:
                        A_	     =							text_or_text_and_label
                  added_labels.add(UpperCAmelCase  )
                  examples.append(InputExample(guid=UpperCAmelCase	, text_a=UpperCAmelCase	, text_b=UpperCAmelCase	, label=UpperCAmelCase  )  )

            # Update examples
            if overwrite_examples:
                  A_	     =							examples
            else:
                  self.examples.extend(UpperCAmelCase  )

            # Update labels
            if overwrite_labels:
                  A_	     =							list(UpperCAmelCase  )
            else:
                  A_	     =							list(set(self.labels  ).union(UpperCAmelCase  )  )

            return self.examples







      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Tuple=None	, UpperCAmelCase       :							List[Any]=False	, UpperCAmelCase       :							Optional[int]=0	, UpperCAmelCase       :							Any=True	, UpperCAmelCase       :							Tuple=None	, ):
            if max_length is None:
                  A_	     =							tokenizer.max_len

            A_	     =							{label: i for i, label in enumerate(self.labels  )}

            A_	     =							[]
            for ex_index, example in enumerate(self.examples  ):
                  if ex_index % 10000 == 0:
                        logger.info(f'''Tokenizing example {ex_index}'''  )

                  A_	     =							tokenizer.encode(
                      example.text_a	, add_special_tokens=UpperCAmelCase	, max_length=min(UpperCAmelCase	, tokenizer.max_len  )	, )
                  all_input_ids.append(UpperCAmelCase  )

            A_	     =							max(len(UpperCAmelCase  ) for input_ids in all_input_ids  )

            A_	     =							[]
            for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase	, self.examples  )  ):
                  if ex_index % 10000 == 0:
                        logger.info(f'''Writing example {ex_index}/{len(self.examples  )}'''  )
                  # The mask has 1 for real tokens and 0 for padding tokens. Only real
                  # tokens are attended to.
                  A_	     =							[1 if mask_padding_with_zero else 0] * len(UpperCAmelCase  )

                  # Zero-pad up to the sequence length.
                  A_	     =							batch_length - len(UpperCAmelCase  )
                  if pad_on_left:
                        A_	     =							([pad_token] * padding_length) + input_ids
                        A_	     =							([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                  else:
                        A_	     =							input_ids + ([pad_token] * padding_length)
                        A_	     =							attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

                  if len(UpperCAmelCase  ) != batch_length:
                        raise ValueError(f'''Error with input length {len(UpperCAmelCase  )} vs {batch_length}'''  )
                  if len(UpperCAmelCase  ) != batch_length:
                        raise ValueError(f'''Error with input length {len(UpperCAmelCase  )} vs {batch_length}'''  )

                  if self.mode == "classification":
                        A_	     =							label_map[example.label]
                  elif self.mode == "regression":
                        A_	     =							float(example.label  )
                  else:
                        raise ValueError(self.mode  )

                  if ex_index < 5 and self.verbose:
                        logger.info("*** Example ***"  )
                        logger.info(f'''guid: {example.guid}'''  )
                        logger.info(f'''input_ids: {" ".join([str(UpperCAmelCase  ) for x in input_ids]  )}'''  )
                        logger.info(f'''attention_mask: {" ".join([str(UpperCAmelCase  ) for x in attention_mask]  )}'''  )
                        logger.info(f'''label: {example.label} (id = {label})'''  )

                  features.append(InputFeatures(input_ids=UpperCAmelCase	, attention_mask=UpperCAmelCase	, label=UpperCAmelCase  )  )

            if return_tensors is None:
                  return features
            elif return_tensors == "tf":
                  if not is_tf_available():
                        raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"  )
                  import tensorflow as tf

                  def gen():
                        for ex in features:
                              yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

                  A_	     =							tf.data.Dataset.from_generator(
                      UpperCAmelCase	, ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	, ({"input_ids": tf.TensorShape([None]  ), "attention_mask": tf.TensorShape([None]  )}, tf.TensorShape([]  ))	, )
                  return dataset
            elif return_tensors == "pt":
                  if not is_torch_available():
                        raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"  )
                  import torch
                  from torch.utils.data import TensorDataset

                  A_	     =							torch.tensor([f.input_ids for f in features]	, dtype=torch.long  )
                  A_	     =							torch.tensor([f.attention_mask for f in features]	, dtype=torch.long  )
                  if self.mode == "classification":
                        A_	     =							torch.tensor([f.label for f in features]	, dtype=torch.long  )
                  elif self.mode == "regression":
                        A_	     =							torch.tensor([f.label for f in features]	, dtype=torch.float  )

                  A_	     =							TensorDataset(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  )
                  return dataset
            else:
                  raise ValueError("return_tensors should be one of 'tf' or 'pt'"  )


def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

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


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