


"""simple docstring"""
def 						lowercase				(	_snake_case		: str	)	->int:
	"""simple docstring"""





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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       Dict							   =							input("""Enter the string: """).strip()
			SCREAMING_SNAKE_CASE					:       str							   =							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_tf_available, is_torch_available


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
