





"""simple docstring"""


from typing import TYPE_CHECKING

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


a__    :   str    						= {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__    :   str    						= ["""OpenAIGPTTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__    :   List[Any]    						= [
       """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """OpenAIGPTDoubleHeadsModel""",
       """OpenAIGPTForSequenceClassification""",
       """OpenAIGPTLMHeadModel""",
       """OpenAIGPTModel""",
       """OpenAIGPTPreTrainedModel""",
       """load_tf_weights_in_openai_gpt""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__    :   int    						= [
       """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFOpenAIGPTDoubleHeadsModel""",
       """TFOpenAIGPTForSequenceClassification""",
       """TFOpenAIGPTLMHeadModel""",
       """TFOpenAIGPTMainLayer""",
       """TFOpenAIGPTModel""",
       """TFOpenAIGPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

   a__    :   Dict    						= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''







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


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

