






import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				Union[str, Any]            =    (VQDiffusionScheduler,)






						def 					UpperCamelCase__					(		self   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  {
												    'num_vec_classes': 40_97,
												    'num_train_timesteps': 1_00,
												}

												config.update(**_UpperCamelCase      )
												return config






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  4
												lowerCAmelCase__			  =  8
												lowerCAmelCase__			  =  8

												lowerCAmelCase__			  =  torch.randint(0   ,  _UpperCamelCase   ,  (batch_size, height * width)      )

												return sample






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												assert False






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												def model(_UpperCamelCase   ,  _UpperCamelCase   ,  *_UpperCamelCase      ):
																		lowerCAmelCase__  ,				lowerCAmelCase__			  =  sample.shape
																		lowerCAmelCase__			  =  torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
																		lowerCAmelCase__			  =  F.log_softmax(logits.double()   ,  dim=1      ).float()
																		return return_value

												return model






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												for timesteps in [2, 5, 1_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=_UpperCamelCase      )






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												for num_vec_classes in [5, 1_00, 10_00, 40_00]:
																		self.check_over_configs(num_vec_classes=_UpperCamelCase      )






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												for t in [0, 50, 99]:
																		self.check_over_forward(time_step=_UpperCamelCase      )







						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												pass













def 		_UpperCamelCase      (			UpperCamelCase_				:    str						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len(UpperCamelCase_						)
						lowerCAmelCase__			  =  [0] * length
						lowerCAmelCase__			  =  [[False for i in range(UpperCamelCase_						)] for j in range(UpperCamelCase_						)]
						for i, c in enumerate(UpperCamelCase_						):
												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(UpperCamelCase_			,				0 if j == 0 else (cut[j - 1] + 1)						)
												lowerCAmelCase__			  =  mincut
						return cut[length - 1]


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





