
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 		__lowerCamelCase     (   _SCREAMING_SNAKE_CASE		):
							lowerCamelCase__:				List[Any]      =							(EulerAncestralDiscreteScheduler,)
							lowerCamelCase__:				int      =							10



							def     A__ (     self	,				**__snake_case							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     {
								    "num_train_timesteps": 1_1_0_0,
								    "beta_start": 0.00_01,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**_SCREAMING_SNAKE_CASE							)
								return config



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE							)



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]	,				[0.00_02, 0.0_02, 0.02]							):
									self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE	,				beta_end=_SCREAMING_SNAKE_CASE							)



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE							)



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE							)



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     self.scheduler_classes[0]
								UpperCAmelCase:					Optional[Any] 						=     self.get_scheduler_config()
								UpperCAmelCase:					str 						=     scheduler_class(**_SCREAMING_SNAKE_CASE							)

								scheduler.set_timesteps(self.num_inference_steps							)

								UpperCAmelCase:					int 						=     torch.manual_seed(0							)

								UpperCAmelCase:					Any 						=     self.dummy_model()
								UpperCAmelCase:					Union[str, Any] 						=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								UpperCAmelCase:					Tuple 						=     sample.to(_SCREAMING_SNAKE_CASE							)

								for i, t in enumerate(scheduler.timesteps							):
									UpperCAmelCase:					List[str] 						=     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)

									UpperCAmelCase:					Union[str, Any] 						=     model(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)

									UpperCAmelCase:					Optional[Any] 						=     scheduler.step(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				generator=_SCREAMING_SNAKE_CASE							)
									UpperCAmelCase:					Union[str, Any] 						=     output.prev_sample

								UpperCAmelCase:					List[Any] 						=     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE							)							)
								UpperCAmelCase:					Tuple 						=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE							)							)

								assert abs(result_sum.item() - 1_5_2.3_1_9_2							) < 1e-2
								assert abs(result_mean.item() - 0.19_83							) < 1e-3



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     self.scheduler_classes[0]
								UpperCAmelCase:					Dict 						=     self.get_scheduler_config(prediction_type="v_prediction"							)
								UpperCAmelCase:					List[str] 						=     scheduler_class(**_SCREAMING_SNAKE_CASE							)

								scheduler.set_timesteps(self.num_inference_steps							)

								UpperCAmelCase:					List[Any] 						=     torch.manual_seed(0							)

								UpperCAmelCase:					List[Any] 						=     self.dummy_model()
								UpperCAmelCase:					Optional[Any] 						=     self.dummy_sample_deter * scheduler.init_noise_sigma
								UpperCAmelCase:					Optional[int] 						=     sample.to(_SCREAMING_SNAKE_CASE							)

								for i, t in enumerate(scheduler.timesteps							):
									UpperCAmelCase:					Dict 						=     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)

									UpperCAmelCase:					Union[str, Any] 						=     model(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)

									UpperCAmelCase:					Optional[int] 						=     scheduler.step(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				generator=_SCREAMING_SNAKE_CASE							)
									UpperCAmelCase:					Optional[Any] 						=     output.prev_sample

								UpperCAmelCase:					int 						=     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE							)							)
								UpperCAmelCase:					Dict 						=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE							)							)

								assert abs(result_sum.item() - 1_0_8.4_4_3_9							) < 1e-2
								assert abs(result_mean.item() - 0.14_12							) < 1e-3




							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     self.scheduler_classes[0]
								UpperCAmelCase:					Optional[Any] 						=     self.get_scheduler_config()
								UpperCAmelCase:					List[Any] 						=     scheduler_class(**_SCREAMING_SNAKE_CASE							)

								scheduler.set_timesteps(self.num_inference_steps	,				device=_SCREAMING_SNAKE_CASE							)
								UpperCAmelCase:					Any 						=     torch.manual_seed(0							)

								UpperCAmelCase:					List[str] 						=     self.dummy_model()
								UpperCAmelCase:					Dict 						=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								UpperCAmelCase:					Dict 						=     sample.to(_SCREAMING_SNAKE_CASE							)

								for t in scheduler.timesteps:
									UpperCAmelCase:					str 						=     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)

									UpperCAmelCase:					Any 						=     model(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							)

									UpperCAmelCase:					int 						=     scheduler.step(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				generator=_SCREAMING_SNAKE_CASE							)
									UpperCAmelCase:					Union[str, Any] 						=     output.prev_sample

								UpperCAmelCase:					Union[str, Any] 						=     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE							)							)
								UpperCAmelCase:					Union[str, Any] 						=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE							)							)

								assert abs(result_sum.item() - 1_5_2.3_1_9_2							) < 1e-2
								assert abs(result_mean.item() - 0.19_83							) < 1e-3




def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






	a_     			=   len(UpperCamelCase	)
	a_     			=   [0] * length
	a_     			=   [[False for i in range(UpperCamelCase	)] for j in range(UpperCamelCase	)]
	for i, c in enumerate(UpperCamelCase	):
		a_     			=   i
		for j in range(i + 1	):
			if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
				a_     			=   True
				a_     			=   min(UpperCamelCase     ,					0 if j == 0 else (cut[j - 1] + 1)	)
		a_     			=   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}')