
"""simple docstring"""


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 					A_			(  A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     (KDPMaAncestralDiscreteScheduler,)
							SCREAMING_SNAKE_CASE_			   =     10





							def    UpperCAmelCase__							(						self						:Tuple  ,							**lowerCamelCase_						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  ={
										    'num_train_timesteps': 1_100,
										    'beta_start': 0.00_01,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										}

										config.update(**lowerCamelCase_ )
										return config





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										for timesteps in [10, 50, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase_ )





							def    UpperCAmelCase__							(						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=lowerCamelCase_  ,							beta_end=lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

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





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										if torch_device == "mps":
													return
										lowerCamelCase__			:  Any  =self.scheduler_classes[0]
										lowerCamelCase__			:  int  =self.get_scheduler_config()
										lowerCamelCase__			:  Union[str, Any]  =scheduler_class(**lowerCamelCase_ )

										scheduler.set_timesteps(self.num_inference_steps )

										lowerCamelCase__			:  int  =torch.manual_seed(0 )

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

										for i, t in enumerate(scheduler.timesteps ):
													lowerCamelCase__			:  int  =scheduler.scale_model_input(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Optional[Any]  =model(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Optional[Any]  =scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							generator=lowerCamelCase_ )
													lowerCamelCase__			:  List[Any]  =output.prev_sample

										lowerCamelCase__			:  Union[str, Any]  =torch.sum(torch.abs(lowerCamelCase_ ) )
										lowerCamelCase__			:  Any  =torch.mean(torch.abs(lowerCamelCase_ ) )

										assert abs(result_sum.item() - 13_849.3_877 ) < 1e-2
										assert abs(result_mean.item() - 18.03_31 ) < 5e-3





							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

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





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										if torch_device == "mps":
													return
										lowerCamelCase__			:  List[str]  =self.scheduler_classes[0]
										lowerCamelCase__			:  Dict  =self.get_scheduler_config(prediction_type='v_prediction' )
										lowerCamelCase__			:  Dict  =scheduler_class(**lowerCamelCase_ )

										scheduler.set_timesteps(self.num_inference_steps )

										lowerCamelCase__			:  Any  =self.dummy_model()
										lowerCamelCase__			:  List[Any]  =self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase__			:  List[str]  =sample.to(lowerCamelCase_ )

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

										for i, t in enumerate(scheduler.timesteps ):
													lowerCamelCase__			:  Tuple  =scheduler.scale_model_input(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Any  =model(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Optional[Any]  =scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							generator=lowerCamelCase_ )
													lowerCamelCase__			:  List[Any]  =output.prev_sample

										lowerCamelCase__			:  Union[str, Any]  =torch.sum(torch.abs(lowerCamelCase_ ) )
										lowerCamelCase__			:  str  =torch.mean(torch.abs(lowerCamelCase_ ) )

										assert abs(result_sum.item() - 328.9_970 ) < 1e-2
										assert abs(result_mean.item() - 0.42_84 ) < 1e-3





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										if torch_device == "mps":
													return
										lowerCamelCase__			:  int  =self.scheduler_classes[0]
										lowerCamelCase__			:  Tuple  =self.get_scheduler_config()
										lowerCamelCase__			:  Optional[int]  =scheduler_class(**lowerCamelCase_ )

										scheduler.set_timesteps(self.num_inference_steps  ,							device=lowerCamelCase_ )
										lowerCamelCase__			:  List[str]  =torch.manual_seed(0 )

										lowerCamelCase__			:  int  =self.dummy_model()
										lowerCamelCase__			:  Any  =self.dummy_sample_deter.to(lowerCamelCase_ ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
													lowerCamelCase__			:  Dict  =scheduler.scale_model_input(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  Tuple  =model(lowerCamelCase_  ,							lowerCamelCase_ )

													lowerCamelCase__			:  List[str]  =scheduler.step(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							generator=lowerCamelCase_ )
													lowerCamelCase__			:  Optional[Any]  =output.prev_sample

										lowerCamelCase__			:  List[str]  =torch.sum(torch.abs(lowerCamelCase_ ) )
										lowerCamelCase__			:  List[Any]  =torch.mean(torch.abs(lowerCamelCase_ ) )

										assert abs(result_sum.item() - 13_849.3_818 ) < 1e-1
										assert abs(result_mean.item() - 18.03_31 ) < 1e-3

"""simple docstring"""


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


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