




'''simple docstring'''






def 		a_(			lowerCamelCase   )					->int:






       '''simple docstring'''




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


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










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowercase						:   int      						=							{
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Any      						=							['''XLMProphetNetTokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Tuple      						=							[
          '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XLMProphetNetDecoder''',
          '''XLMProphetNetEncoder''',
          '''XLMProphetNetForCausalLM''',
          '''XLMProphetNetForConditionalGeneration''',
          '''XLMProphetNetModel''',
          '''XLMProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase						:   Optional[Any]      						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




