




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Optional[Any]										= (DPMSolverMultistepScheduler,)
				SCREAMING_SNAKE_CASE_	:							List[str]										= (("""num_inference_steps""", 25),)





				def 							__UpperCAmelCase							(				self    :					Tuple ,				**__lowerCamelCase    :					int							) ->					Any:
							a   =      {
							    "num_train_timesteps": 10_00,
							    "beta_start": 0.0_001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							    "prediction_type": "epsilon",
							    "thresholding": False,
							    "sample_max_value": 1.0,
							    "algorithm_type": "dpmsolver++",
							    "solver_type": "midpoint",
							    "lower_order_final": False,
							    "lambda_min_clipped": -float("inf"							),
							    "variance_type": None,
							}

							config.update(**__lowerCamelCase							)
							return config





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					Optional[int]=0 ,				**__lowerCamelCase    :					Optional[Any]							) ->					Dict:
							a   =      dict(self.forward_default_kwargs							)
							a   =      kwargs.pop("num_inference_steps" ,				__lowerCamelCase							)
							a   =      self.dummy_sample
							a   =      0.1 * sample
							a   =      [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
										a   =      self.get_scheduler_config(**__lowerCamelCase							)
										a   =      scheduler_class(**__lowerCamelCase							)
										scheduler.set_timesteps(__lowerCamelCase							)
										# copy over dummy past residuals
										a   =      dummy_past_residuals[: scheduler.config.solver_order]

										with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(__lowerCamelCase							)
													a   =      scheduler_class.from_pretrained(__lowerCamelCase							)
													new_scheduler.set_timesteps(__lowerCamelCase							)
													# copy over dummy past residuals
													a   =      dummy_past_residuals[: new_scheduler.config.solver_order]

										a   ,		a   =      sample, sample
										for t in range(__lowerCamelCase ,				time_step + scheduler.config.solver_order + 1							):
													a   =      scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				**__lowerCamelCase							).prev_sample
													a   =      new_scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				**__lowerCamelCase							).prev_sample

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





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					int:
							pass





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					Any=0 ,				**__lowerCamelCase    :					int							) ->					Tuple:
							a   =      dict(self.forward_default_kwargs							)
							a   =      kwargs.pop("num_inference_steps" ,				__lowerCamelCase							)
							a   =      self.dummy_sample
							a   =      0.1 * sample
							a   =      [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
										a   =      self.get_scheduler_config()
										a   =      scheduler_class(**__lowerCamelCase							)
										scheduler.set_timesteps(__lowerCamelCase							)

										# copy over dummy past residuals (must be after setting timesteps)
										a   =      dummy_past_residuals[: scheduler.config.solver_order]

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

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

										a   =      scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				**__lowerCamelCase							).prev_sample
										a   =      new_scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				**__lowerCamelCase							).prev_sample

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





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					str=None ,				**__lowerCamelCase    :					str							) ->					Dict:
							if scheduler is None:
										a   =      self.scheduler_classes[0]
										a   =      self.get_scheduler_config(**__lowerCamelCase							)
										a   =      scheduler_class(**__lowerCamelCase							)

							a   =      10
							a   =      self.dummy_model()
							a   =      self.dummy_sample_deter
							scheduler.set_timesteps(__lowerCamelCase							)

							for i, t in enumerate(scheduler.timesteps							):
										a   =      model(__lowerCamelCase ,				__lowerCamelCase							)
										a   =      scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							).prev_sample

							return sample





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					str:
							a   =      dict(self.forward_default_kwargs							)

							a   =      kwargs.pop("num_inference_steps" ,				__lowerCamelCase							)

							for scheduler_class in self.scheduler_classes:
										a   =      self.get_scheduler_config()
										a   =      scheduler_class(**__lowerCamelCase							)

										a   =      self.dummy_sample
										a   =      0.1 * sample

										if num_inference_steps is not None and hasattr(__lowerCamelCase ,				"set_timesteps"							):
													scheduler.set_timesteps(__lowerCamelCase							)
										elif num_inference_steps is not None and not hasattr(__lowerCamelCase ,				"set_timesteps"							):
													a   =      num_inference_steps

										# copy over dummy past residuals (must be done after set_timesteps)
										a   =      [residual + 0.2, residual + 0.15, residual + 0.10]
										a   =      dummy_past_residuals[: scheduler.config.solver_order]

										a   =      scheduler.timesteps[5]
										a   =      scheduler.timesteps[6]

										a   =      scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				**__lowerCamelCase							).prev_sample
										a   =      scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				**__lowerCamelCase							).prev_sample

										self.assertEqual(output_a.shape ,				sample.shape							)
										self.assertEqual(output_a.shape ,				output_a.shape							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[int]:
							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[Any]:
							self.check_over_configs(thresholding=__lowerCamelCase							)
							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=__lowerCamelCase ,				prediction_type=__lowerCamelCase ,				sample_max_value=__lowerCamelCase ,				algorithm_type="dpmsolver++" ,				solver_order=__lowerCamelCase ,				solver_type=__lowerCamelCase ,				)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Dict:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Union[str, Any]:
							for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
										for solver_type in ["midpoint", "heun"]:
													for order in [1, 2, 3]:
																for prediction_type in ["epsilon", "sample"]:
																			if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																						if order == 3:
																									continue
																			else:
																						self.check_over_configs(
																						    solver_order=__lowerCamelCase ,				solver_type=__lowerCamelCase ,				prediction_type=__lowerCamelCase ,				algorithm_type=__lowerCamelCase ,				)
																			a   =      self.full_loop(
																			    solver_order=__lowerCamelCase ,				solver_type=__lowerCamelCase ,				prediction_type=__lowerCamelCase ,				algorithm_type=__lowerCamelCase ,				)
																			assert not torch.isnan(__lowerCamelCase							).any(), "Samples have nan numbers"





				def 							__UpperCAmelCase							(				self    :					str							) ->					Tuple:
							self.check_over_configs(lower_order_final=__lowerCamelCase							)
							self.check_over_configs(lower_order_final=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							self.check_over_configs(lambda_min_clipped=-float("inf"							)							)
							self.check_over_configs(lambda_min_clipped=-5.1							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					List[Any]:
							self.check_over_configs(variance_type=__lowerCamelCase							)
							self.check_over_configs(variance_type="learned_range"							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Optional[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
										self.check_over_forward(num_inference_steps=__lowerCamelCase ,				time_step=0							)





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Optional[int]:
							a   =      self.full_loop()
							a   =      torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_mean.item() - 0.3_301							) < 1e-3





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[Any]:
							a   =      self.full_loop(thresholding=__lowerCamelCase ,				dynamic_thresholding_ratio=0.87 ,				sample_max_value=0.5							)
							a   =      torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_mean.item() - 1.1_364							) < 1e-3





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Any:
							a   =      self.full_loop(prediction_type="v_prediction"							)
							a   =      torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_mean.item() - 0.2_251							) < 1e-3





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Optional[int]:
							a   =      self.full_loop(prediction_type="v_prediction" ,				use_karras_sigmas=__lowerCamelCase							)
							a   =      torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_mean.item() - 0.2_096							) < 1e-3





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Dict:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							a   =      DPMSolverMultistepScheduler(**self.get_scheduler_config()							)
							a   =      self.full_loop(scheduler=__lowerCamelCase							)
							a   =      torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_mean.item() - 0.3_301							) < 1e-3

							a   =      DPMSolverSinglestepScheduler.from_config(scheduler.config							)
							a   =      UniPCMultistepScheduler.from_config(scheduler.config							)
							a   =      DEISMultistepScheduler.from_config(scheduler.config							)
							a   =      DPMSolverMultistepScheduler.from_config(scheduler.config							)

							a   =      self.full_loop(scheduler=__lowerCamelCase							)
							a   =      torch.mean(torch.abs(__lowerCamelCase							)							)

							assert abs(result_mean.item() - 0.3_301							) < 1e-3





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Tuple:
							a   =      self.scheduler_classes[0]
							a   =      self.get_scheduler_config(thresholding=__lowerCamelCase ,				dynamic_thresholding_ratio=0							)
							a   =      scheduler_class(**__lowerCamelCase							)

							a   =      10
							a   =      self.dummy_model()
							a   =      self.dummy_sample_deter.half()
							scheduler.set_timesteps(__lowerCamelCase							)

							for i, t in enumerate(scheduler.timesteps							):
										a   =      model(__lowerCamelCase ,				__lowerCamelCase							)
										a   =      scheduler.step(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							).prev_sample

							assert sample.dtype == torch.floataa





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				**__lowerCamelCase    :					int							) ->					str:
							for scheduler_class in self.scheduler_classes:
										a   =      self.get_scheduler_config(**__lowerCamelCase							)
										a   =      scheduler_class(**__lowerCamelCase							)

										scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
										assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps












def       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






