

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A     						=      {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      ['''CamembertTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      ['''CamembertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CamembertForCausalLM''',
     '''CamembertForMaskedLM''',
     '''CamembertForMultipleChoice''',
     '''CamembertForQuestionAnswering''',
     '''CamembertForSequenceClassification''',
     '''CamembertForTokenClassification''',
     '''CamembertModel''',
     '''CamembertPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCamembertForCausalLM''',
     '''TFCamembertForMaskedLM''',
     '''TFCamembertForMultipleChoice''',
     '''TFCamembertForQuestionAnswering''',
     '''TFCamembertForSequenceClassification''',
     '''TFCamembertForTokenClassification''',
     '''TFCamembertModel''',
     '''TFCamembertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_camembert import CamembertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_camembert_fast import CamembertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_camembert import (
      CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      CamembertForCausalLM,
      CamembertForMaskedLM,
      CamembertForMultipleChoice,
      CamembertForQuestionAnswering,
      CamembertForSequenceClassification,
      CamembertForTokenClassification,
      CamembertModel,
      CamembertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_camembert import (
      TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCamembertForCausalLM,
      TFCamembertForMaskedLM,
      TFCamembertForMultipleChoice,
      TFCamembertForQuestionAnswering,
      TFCamembertForSequenceClassification,
      TFCamembertForTokenClassification,
      TFCamembertModel,
      TFCamembertPreTrainedModel,
  )

else:
 import sys

 __A     						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




def  snake_case_(_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     						=      input('''Enter the string: ''').strip()
 __A     						=      find_minimum_partitions(s)
 print(f'''Minimum number of partitions required for the \'{s}\' is {ans}''')

