"""simple docstring"""



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_UpperCamelCase         =		{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

_UpperCamelCase         =		{
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

_UpperCamelCase         =		{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class 	__UpperCAmelCase						(lowerCAmelCase__    ):







   '''simple docstring'''
   _UpperCamelCase     :	Optional[int]				    =							VOCAB_FILES_NAMES
   _UpperCamelCase     :	Optional[int]				    =							PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase     :	Union[str, Any]				    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase     :	Union[str, Any]				    =							['input_ids', 'attention_mask', 'token_type_ids']
   _UpperCamelCase     :	Optional[Any]				    =							DebertaTokenizer





   def __init__( self      ,			snake_case_=None      ,			snake_case_=None      ,			snake_case_=None      ,			snake_case_="replace"      ,			snake_case_="[CLS]"      ,			snake_case_="[SEP]"      ,			snake_case_="[SEP]"      ,			snake_case_="[CLS]"      ,			snake_case_="[UNK]"      ,			snake_case_="[PAD]"      ,			snake_case_="[MASK]"      ,			snake_case_=False      ,			**snake_case_      ,			):







          '''simple docstring'''


          super().__init__(
              _lowerCamelCase      ,			_lowerCamelCase      ,			tokenizer_file=_lowerCamelCase      ,			errors=_lowerCamelCase      ,			bos_token=_lowerCamelCase      ,			eos_token=_lowerCamelCase      ,			unk_token=_lowerCamelCase      ,			sep_token=_lowerCamelCase      ,			cls_token=_lowerCamelCase      ,			pad_token=_lowerCamelCase      ,			mask_token=_lowerCamelCase      ,			add_prefix_space=_lowerCamelCase      ,			**_lowerCamelCase      ,			)
          A__					:    str														= kwargs.pop("""add_bos_token"""      ,			_lowerCamelCase       )

          A__					:    Tuple														= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
          if pre_tok_state.get("""add_prefix_space"""      ,			_lowerCamelCase       ) != add_prefix_space:
                 A__					:    Optional[int]														= getattr(_lowerCamelCase      ,			pre_tok_state.pop("""type"""       )       )
                 A__					:    Dict														= add_prefix_space
                 A__					:    int														= pre_tok_class(**_lowerCamelCase       )

          A__					:    Dict														= add_prefix_space





   @property
   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          if self._mask_token is None:
                 if self.verbose:
                        logger.error("""Using mask_token, but it is not set yet."""       )
                 return None
          return str(self._mask_token       )





   @mask_token.setter
   def 		lowerCamelCase				( self      ,			snake_case_       ):







          '''simple docstring'''


          A__					:    Tuple														= AddedToken(_lowerCamelCase      ,			lstrip=_lowerCamelCase      ,			rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase      ,			_lowerCamelCase       ) else value
          A__					:    List[str]														= value





   def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = None       ):







          '''simple docstring'''


          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          A__					:    int														= [self.cls_token_id]
          A__					:    Union[str, Any]														= [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep





   def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = None       ):







          '''simple docstring'''


          A__					:    List[Any]														= [self.sep_token_id]
          A__					:    int														= [self.cls_token_id]

          if token_ids_a is None:
                 return len(cls + token_ids_a + sep       ) * [0]
          return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]





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







          '''simple docstring'''


          A__					:    Union[str, Any]														= kwargs.get("""is_split_into_words"""      ,			_lowerCamelCase       )
          assert self.add_prefix_space or not is_split_into_words, (
              F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*_lowerCamelCase      ,			**_lowerCamelCase       )





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







          '''simple docstring'''


          A__					:    Tuple														= kwargs.get("""is_split_into_words"""      ,			_lowerCamelCase       )

          assert self.add_prefix_space or not is_split_into_words, (
              F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*_lowerCamelCase      ,			**_lowerCamelCase       )





   def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = None       ):







          '''simple docstring'''


          A__					:    List[str]														= self._tokenizer.model.save(_lowerCamelCase      ,			name=_lowerCamelCase       )
          return tuple(_lowerCamelCase       )





   def 		lowerCamelCase				( self      ,			snake_case_       ):







          '''simple docstring'''


          A__					:    Tuple														= []
          for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(_lowerCamelCase      ,			add_special_tokens=_lowerCamelCase       ) + [self.eos_token_id]       )

          if len(_lowerCamelCase       ) > self.model_max_length:
                 A__					:    Any														= input_ids[-self.model_max_length :]
          return input_ids






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							A__					:    str														= len(lowerCAmelCase       )
							A__					:    List[Any]														= [0] * length
							A__					:    Union[str, Any]														= [[False for i in range(lowerCAmelCase       )] for j in range(lowerCAmelCase       )]
							for i, c in enumerate(lowerCAmelCase       ):
														A__					:    Dict														= i
														for j in range(i + 1       ):
																					if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																												A__					:    List[str]														= True
																												A__					:    Optional[Any]														= min(lowerCAmelCase ,     0 if j == 0 else (cut[j - 1] + 1)       )
														A__					:    Optional[int]														= mincut
							return cut[length - 1]


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





