

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Optional[int]													= (DPMSolverSinglestepScheduler,)
					UpperCAmelCase__:     Optional[int]													= (('''num_inference_steps''', 25),)






					def 						__A    (				self					, **A__    ):
						A__							:					Optional[Any]       =	{
						    """num_train_timesteps""": 1000,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						    """solver_order""": 2,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						    """sample_max_value""": 1.0,
						    """algorithm_type""": """dpmsolver++""",
						    """solver_type""": """midpoint""",
						    """lambda_min_clipped""": -float("""inf"""    ),
						    """variance_type""": None,
						}

						config.update(**A__    )
						return config






					def 						__A    (				self					, A__=0					, **A__    ):
						A__							:					Tuple       =	dict(self.forward_default_kwargs    )
						A__							:					str       =	kwargs.pop("""num_inference_steps"""					, A__    )
						A__							:					Optional[int]       =	self.dummy_sample
						A__							:					Any       =	0.1 * sample
						A__							:					List[Any]       =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							A__							:					List[str]       =	self.get_scheduler_config(**A__    )
							A__							:					Tuple       =	scheduler_class(**A__    )
							scheduler.set_timesteps(A__    )
							# copy over dummy past residuals
							A__							:					List[Any]       =	dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(A__    )
								A__							:					Optional[int]       =	scheduler_class.from_pretrained(A__    )
								new_scheduler.set_timesteps(A__    )
								# copy over dummy past residuals
								A__							:					Union[str, Any]       =	dummy_past_residuals[: new_scheduler.config.solver_order]

							A__     ,     A__							:					Optional[int]       =	sample, sample
							for t in range(A__					, time_step + scheduler.config.solver_order + 1    ):
								A__							:					int       =	scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample
								A__							:					List[str]       =	new_scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample

								assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






					def 						__A    (				self    ):
						pass






					def 						__A    (				self					, A__=0					, **A__    ):
						A__							:					Any       =	dict(self.forward_default_kwargs    )
						A__							:					List[str]       =	kwargs.pop("""num_inference_steps"""					, A__    )
						A__							:					Union[str, Any]       =	self.dummy_sample
						A__							:					int       =	0.1 * sample
						A__							:					Optional[Any]       =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							A__							:					Optional[int]       =	self.get_scheduler_config()
							A__							:					Tuple       =	scheduler_class(**A__    )
							scheduler.set_timesteps(A__    )

							# copy over dummy past residuals (must be after setting timesteps)
							A__							:					Union[str, Any]       =	dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(A__    )
								A__							:					Any       =	scheduler_class.from_pretrained(A__    )
								# copy over dummy past residuals
								new_scheduler.set_timesteps(A__    )

								# copy over dummy past residual (must be after setting timesteps)
								A__							:					Any       =	dummy_past_residuals[: new_scheduler.config.solver_order]

							A__							:					List[Any]       =	scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample
							A__							:					List[Any]       =	new_scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample

							assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






					def 						__A    (				self					, A__=None					, **A__    ):
						if scheduler is None:
							A__							:					int       =	self.scheduler_classes[0]
							A__							:					List[Any]       =	self.get_scheduler_config(**A__    )
							A__							:					Optional[Any]       =	scheduler_class(**A__    )

						A__							:					Union[str, Any]       =	self.scheduler_classes[0]
						A__							:					Optional[Any]       =	self.get_scheduler_config(**A__    )
						A__							:					List[str]       =	scheduler_class(**A__    )

						A__							:					Union[str, Any]       =	10
						A__							:					Tuple       =	self.dummy_model()
						A__							:					Dict       =	self.dummy_sample_deter
						scheduler.set_timesteps(A__    )

						for i, t in enumerate(scheduler.timesteps    ):
							A__							:					Union[str, Any]       =	model(A__					, A__    )
							A__							:					Dict       =	scheduler.step(A__					, A__					, A__    ).prev_sample

						return sample






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
						A__							:					int       =	50
						A__							:					Dict       =	self.dummy_model()
						A__							:					Tuple       =	self.dummy_sample_deter
						scheduler.set_timesteps(A__    )

						# make sure that the first t is uneven
						for i, t in enumerate(scheduler.timesteps[3:]    ):
							A__							:					int       =	model(A__					, A__    )
							A__							:					List[Any]       =	scheduler.step(A__					, A__					, A__    ).prev_sample

						A__							:					str       =	torch.mean(torch.abs(A__    )    )

						assert abs(result_mean.item() - 0.2_5_7_4    ) < 1e-3






					def 						__A    (				self    ):
						for timesteps in [25, 50, 100, 999, 1000]:
							self.check_over_configs(num_train_timesteps=A__    )






					def 						__A    (				self    ):
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						A__							:					Dict       =	DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
						A__							:					Tuple       =	self.full_loop(scheduler=A__    )
						A__							:					Optional[int]       =	torch.mean(torch.abs(A__    )    )

						assert abs(result_mean.item() - 0.2_7_9_1    ) < 1e-3

						A__							:					Optional[int]       =	DEISMultistepScheduler.from_config(scheduler.config    )
						A__							:					Optional[Any]       =	DPMSolverMultistepScheduler.from_config(scheduler.config    )
						A__							:					List[Any]       =	UniPCMultistepScheduler.from_config(scheduler.config    )
						A__							:					int       =	DPMSolverSinglestepScheduler.from_config(scheduler.config    )

						A__							:					Any       =	self.full_loop(scheduler=A__    )
						A__							:					str       =	torch.mean(torch.abs(A__    )    )

						assert abs(result_mean.item() - 0.2_7_9_1    ) < 1e-3






					def 						__A    (				self    ):
						self.check_over_configs(thresholding=A__    )
						for order in [1, 2, 3]:
							for solver_type in ["midpoint", "heun"]:
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    thresholding=A__					, prediction_type=A__					, sample_max_value=A__					, algorithm_type="""dpmsolver++"""					, solver_order=A__					, solver_type=A__					, )






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






					def 						__A    (				self    ):
						for algorithm_type in ["dpmsolver", "dpmsolver++"]:
							for solver_type in ["midpoint", "heun"]:
								for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    solver_order=A__					, solver_type=A__					, prediction_type=A__					, algorithm_type=A__					, )
										A__							:					Optional[Any]       =	self.full_loop(
										    solver_order=A__					, solver_type=A__					, prediction_type=A__					, algorithm_type=A__					, )
										assert not torch.isnan(A__    ).any(), "Samples have nan numbers"






					def 						__A    (				self    ):
						self.check_over_configs(lower_order_final=A__    )
						self.check_over_configs(lower_order_final=A__    )






					def 						__A    (				self    ):
						self.check_over_configs(lambda_min_clipped=-float("""inf"""    )    )
						self.check_over_configs(lambda_min_clipped=-5.1    )






					def 						__A    (				self    ):
						self.check_over_configs(variance_type=A__    )
						self.check_over_configs(variance_type="""learned_range"""    )






					def 						__A    (				self    ):
						for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
							self.check_over_forward(num_inference_steps=A__					, time_step=0    )






					def 						__A    (				self    ):
						A__							:					str       =	self.full_loop()
						A__							:					Dict       =	torch.mean(torch.abs(A__    )    )

						assert abs(result_mean.item() - 0.2_7_9_1    ) < 1e-3






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.full_loop(use_karras_sigmas=A__    )
						A__							:					List[Any]       =	torch.mean(torch.abs(A__    )    )

						assert abs(result_mean.item() - 0.2_2_4_8    ) < 1e-3






					def 						__A    (				self    ):
						A__							:					Tuple       =	self.full_loop(prediction_type="""v_prediction"""    )
						A__							:					Any       =	torch.mean(torch.abs(A__    )    )

						assert abs(result_mean.item() - 0.1_4_5_3    ) < 1e-3






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.full_loop(prediction_type="""v_prediction"""					, use_karras_sigmas=A__    )
						A__							:					str       =	torch.mean(torch.abs(A__    )    )

						assert abs(result_mean.item() - 0.0_6_4_9    ) < 1e-3






					def 						__A    (				self    ):
						A__							:					Any       =	self.scheduler_classes[0]
						A__							:					Union[str, Any]       =	self.get_scheduler_config(thresholding=A__					, dynamic_thresholding_ratio=0    )
						A__							:					str       =	scheduler_class(**A__    )

						A__							:					Dict       =	10
						A__							:					Union[str, Any]       =	self.dummy_model()
						A__							:					Optional[int]       =	self.dummy_sample_deter.half()
						scheduler.set_timesteps(A__    )

						for i, t in enumerate(scheduler.timesteps    ):
							A__							:					str       =	model(A__					, A__    )
							A__							:					Optional[Any]       =	scheduler.step(A__					, A__					, A__    ).prev_sample

						assert sample.dtype == torch.floataa






class 					_a		:


					'''simple docstring'''






					def __init__(				self    ):
						A__							:					str       =	""""""
						A__							:					Any       =	""""""
						A__							:					List[Any]       =	[]






					def 						__A    (				self					, A__					, A__    ):
						if m == -1:
							return n + 1
						elif n == -1:
							return m + 1
						elif self.dp[m][n] > -1:
							return self.dp[m][n]
						else:
							if self.worda[m] == self.worda[n]:
								A__							:					Optional[Any]       =	self.__min_dist_top_down_dp(m - 1					, n - 1    )
							else:
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(A__					, n - 1    )
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(m - 1					, A__    )
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(m - 1					, n - 1    )
								A__							:					List[Any]       =	1 + min(A__					, A__					, A__    )

							return self.dp[m][n]






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Tuple       =	worda
						A__							:					Dict       =	worda
						A__							:					Optional[Any]       =	[[-1 for _ in range(len(A__    )    )] for _ in range(len(A__    )    )]

						return self.__min_dist_top_down_dp(len(A__    ) - 1					, len(A__    ) - 1    )






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Optional[Any]       =	worda
						A__							:					Dict       =	worda
						A__							:					Union[str, Any]       =	len(A__    )
						A__							:					List[str]       =	len(A__    )
						A__							:					int       =	[[0 for _ in range(n + 1    )] for _ in range(m + 1    )]

						for i in range(m + 1    ):
							for j in range(n + 1    ):
								if i == 0:  # first string is empty
									A__							:					Tuple       =	j
								elif j == 0:  # second string is empty
									A__							:					Dict       =	i
								elif worda[i - 1] == worda[j - 1]:  # last characters are equal
									A__							:					str       =	self.dp[i - 1][j - 1]
								else:
									A__							:					Union[str, Any]       =	self.dp[i][j - 1]
									A__							:					str       =	self.dp[i - 1][j]
									A__							:					Union[str, Any]       =	self.dp[i - 1][j - 1]
									A__							:					Tuple       =	1 + min(A__					, A__					, A__    )
						return self.dp[m][n]


if __name__ == "__main__":
	A_ :				Union[str, Any]						 =    EditDistance()

	print('****************** Testing Edit Distance DP Algorithm ******************')
	print()

	A_ :				int						 =    input('Enter the first string: ').strip()
	A_ :				List[str]						 =    input('Enter the second string: ').strip()

	print()
	print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
	print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
	print()
	print('*************** End of Testing Edit Distance DP Algorithm ***************')



