"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class lowerCamelCase		(UpperCamelCase__   ):






					'''simple docstring'''


					a 						=		(EulerDiscreteScheduler,)
					a 						=		1_0



					def       lowerCAmelCase_       (    self      :					Tuple ,			**_snake_case      :					List[str]		)				->							Dict:
									SCREAMING_SNAKE_CASE__			=					{
									    "num_train_timesteps": 1100,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**__lowerCamelCase		)
									return config



					def       lowerCAmelCase_       (    self      :					List[str]		)				->							Any:
									for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					Optional[int]		)				->							List[str]:
									for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,			[0.0002, 0.002, 0.02]		):
													self.check_over_configs(beta_start=__lowerCamelCase ,			beta_end=__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					Optional[int]		)				->							Any:
									for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					int		)				->							List[str]:
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					Dict		)				->							str:
									SCREAMING_SNAKE_CASE__			=					self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=					self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__			=					scheduler_class(**__lowerCamelCase		)

									scheduler.set_timesteps(self.num_inference_steps		)

									SCREAMING_SNAKE_CASE__			=					torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=					self.dummy_model()
									SCREAMING_SNAKE_CASE__			=					self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE__			=					sample.to(__lowerCamelCase		)

									for i, t in enumerate(scheduler.timesteps		):
													SCREAMING_SNAKE_CASE__			=					scheduler.scale_model_input(__lowerCamelCase ,			__lowerCamelCase		)

													SCREAMING_SNAKE_CASE__			=					model(__lowerCamelCase ,			__lowerCamelCase		)

													SCREAMING_SNAKE_CASE__			=					scheduler.step(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			generator=__lowerCamelCase		)
													SCREAMING_SNAKE_CASE__			=					output.prev_sample

									SCREAMING_SNAKE_CASE__			=					torch.sum(torch.abs(__lowerCamelCase		)		)
									SCREAMING_SNAKE_CASE__			=					torch.mean(torch.abs(__lowerCamelCase		)		)

									assert abs(result_sum.item() - 10.0807		) < 1e-2
									assert abs(result_mean.item() - 0.0131		) < 1e-3



					def       lowerCAmelCase_       (    self      :					Optional[Any]		)				->							Optional[int]:
									SCREAMING_SNAKE_CASE__			=					self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=					self.get_scheduler_config(prediction_type="v_prediction"		)
									SCREAMING_SNAKE_CASE__			=					scheduler_class(**__lowerCamelCase		)

									scheduler.set_timesteps(self.num_inference_steps		)

									SCREAMING_SNAKE_CASE__			=					torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=					self.dummy_model()
									SCREAMING_SNAKE_CASE__			=					self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE__			=					sample.to(__lowerCamelCase		)

									for i, t in enumerate(scheduler.timesteps		):
													SCREAMING_SNAKE_CASE__			=					scheduler.scale_model_input(__lowerCamelCase ,			__lowerCamelCase		)

													SCREAMING_SNAKE_CASE__			=					model(__lowerCamelCase ,			__lowerCamelCase		)

													SCREAMING_SNAKE_CASE__			=					scheduler.step(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			generator=__lowerCamelCase		)
													SCREAMING_SNAKE_CASE__			=					output.prev_sample

									SCREAMING_SNAKE_CASE__			=					torch.sum(torch.abs(__lowerCamelCase		)		)
									SCREAMING_SNAKE_CASE__			=					torch.mean(torch.abs(__lowerCamelCase		)		)

									assert abs(result_sum.item() - 0.0002		) < 1e-2
									assert abs(result_mean.item() - 2.2676e-06		) < 1e-3



					def       lowerCAmelCase_       (    self      :					Any		)				->							Any:
									SCREAMING_SNAKE_CASE__			=					self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=					self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__			=					scheduler_class(**__lowerCamelCase		)

									scheduler.set_timesteps(self.num_inference_steps ,			device=__lowerCamelCase		)

									SCREAMING_SNAKE_CASE__			=					torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=					self.dummy_model()
									SCREAMING_SNAKE_CASE__			=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE__			=					sample.to(__lowerCamelCase		)

									for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE__			=					scheduler.scale_model_input(__lowerCamelCase ,			__lowerCamelCase		)

													SCREAMING_SNAKE_CASE__			=					model(__lowerCamelCase ,			__lowerCamelCase		)

													SCREAMING_SNAKE_CASE__			=					scheduler.step(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			generator=__lowerCamelCase		)
													SCREAMING_SNAKE_CASE__			=					output.prev_sample

									SCREAMING_SNAKE_CASE__			=					torch.sum(torch.abs(__lowerCamelCase		)		)
									SCREAMING_SNAKE_CASE__			=					torch.mean(torch.abs(__lowerCamelCase		)		)

									assert abs(result_sum.item() - 10.0807		) < 1e-2
									assert abs(result_mean.item() - 0.0131		) < 1e-3



					def       lowerCAmelCase_       (    self      :					Optional[int]		)				->							Tuple:
									SCREAMING_SNAKE_CASE__			=					self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=					self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__			=					scheduler_class(**__lowerCamelCase ,			use_karras_sigmas=__lowerCamelCase		)

									scheduler.set_timesteps(self.num_inference_steps ,			device=__lowerCamelCase		)

									SCREAMING_SNAKE_CASE__			=					torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=					self.dummy_model()
									SCREAMING_SNAKE_CASE__			=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE__			=					sample.to(__lowerCamelCase		)

									for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE__			=					scheduler.scale_model_input(__lowerCamelCase ,			__lowerCamelCase		)

													SCREAMING_SNAKE_CASE__			=					model(__lowerCamelCase ,			__lowerCamelCase		)

													SCREAMING_SNAKE_CASE__			=					scheduler.step(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			generator=__lowerCamelCase		)
													SCREAMING_SNAKE_CASE__			=					output.prev_sample

									SCREAMING_SNAKE_CASE__			=					torch.sum(torch.abs(__lowerCamelCase		)		)
									SCREAMING_SNAKE_CASE__			=					torch.mean(torch.abs(__lowerCamelCase		)		)

									assert abs(result_sum.item() - 124.52_2994_9951_1719		) < 1e-2
									assert abs(result_mean.item() - 0.1_6213_9326_3339_9963		) < 1e-3











def       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str    )->str:
       assert x is not None
       assert y is not None

       A__		   =		len(UpperCamelCase__    )
       A__		   =		len(UpperCamelCase__    )

       # declaring the array for storing the dp values
       A__		   =		[[0] * (n + 1) for _ in range(m + 1    )]  # noqa: E741

       for i in range(1					,	m + 1    ):
              for j in range(1					,	n + 1    ):
                     A__		   =		1 if x[i - 1] == y[j - 1] else 0

                     A__		   =		max(l[i - 1][j]					,	l[i][j - 1]					,	l[i - 1][j - 1] + match    )

       A__		   =		''''''
       A__   ,      A__		   =		m, n
       while i > 0 and j > 0:
              A__		   =		1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            A__		   =		x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
       a__: Tuple							   =						'AGGTAB'
       a__: Tuple							   =						'GXTXAYB'
       a__: List[Any]							   =						4
       a__: Optional[Any]							   =						'GTAB'

       a__   ,     a__: List[str]							   =						longest_common_subsequence(a, b)
       print('len =', ln, ', sub-sequence =', subseq)
       import doctest

       doctest.testmod()





