




'''simple docstring'''




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


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









'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__          =						logging.get_logger(__name__)

snake_case__          =						{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					'qdqbert'







						def __init__(     self      :       Union[str, Any]							, _lowerCamelCase      :       List[Any]=30522							, _lowerCamelCase      :       str=768							, _lowerCamelCase      :       Optional[Any]=12							, _lowerCamelCase      :       str=12							, _lowerCamelCase      :       List[Any]=3072							, _lowerCamelCase      :       Union[str, Any]="gelu"							, _lowerCamelCase      :       Dict=0.1							, _lowerCamelCase      :       Union[str, Any]=0.1							, _lowerCamelCase      :       Optional[int]=512							, _lowerCamelCase      :       Dict=2							, _lowerCamelCase      :       Optional[Any]=0.02							, _lowerCamelCase      :       List[Any]=1E-12							, _lowerCamelCase      :       Tuple=True							, _lowerCamelCase      :       Tuple=1							, _lowerCamelCase      :       Optional[Any]=0							, _lowerCamelCase      :       Optional[int]=2							, **_lowerCamelCase      :       List[str]							, ):





											"""simple docstring"""







											super().__init__(pad_token_id=_lowerCamelCase							, bos_token_id=_lowerCamelCase							, eos_token_id=_lowerCamelCase							, **_lowerCamelCase    )

											A_ :     Optional[Any] =							vocab_size
											A_ :     str =							max_position_embeddings
											A_ :     List[str] =							hidden_size
											A_ :     str =							num_hidden_layers
											A_ :     List[str] =							num_attention_heads
											A_ :     List[str] =							intermediate_size
											A_ :     int =							hidden_act
											A_ :     Tuple =							hidden_dropout_prob
											A_ :     Optional[Any] =							attention_probs_dropout_prob
											A_ :     Dict =							initializer_range
											A_ :     Optional[int] =							type_vocab_size
											A_ :     Any =							layer_norm_eps
											A_ :     Optional[int] =							use_cache



