



'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					(DPMSolverSinglestepScheduler,)
	__A			  =					(('''num_inference_steps''', 25),)







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							{
							    """num_train_timesteps""": 1000,
							    """beta_start""": 0.0001,
							    """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""",
							    """lambda_min_clipped""": -float("""inf"""						),
							    """variance_type""": None,
							}

							config.update(**_lowerCAmelCase						)
							return config







	def   _a   (    self ,				_lowerCAmelCase=0 ,				**_lowerCAmelCase						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							dict(self.forward_default_kwargs						)
							lowercase        =							kwargs.pop("""num_inference_steps""" ,				_lowerCAmelCase						)
							lowercase        =							self.dummy_sample
							lowercase        =							0.1 * sample
							lowercase        =							[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

													lowercase     ,   lowercase        =							sample, sample
													for t in range(_lowerCAmelCase ,				time_step + scheduler.config.solver_order + 1						):
																			lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						).prev_sample
																			lowercase        =							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   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							pass







	def   _a   (    self ,				_lowerCAmelCase=0 ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							lowercase        =							dict(self.forward_default_kwargs						)
							lowercase        =							kwargs.pop("""num_inference_steps""" ,				_lowerCAmelCase						)
							lowercase        =							self.dummy_sample
							lowercase        =							0.1 * sample
							lowercase        =							[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													lowercase        =							self.get_scheduler_config()
													lowercase        =							scheduler_class(**_lowerCAmelCase						)
													scheduler.set_timesteps(_lowerCAmelCase						)

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_lowerCAmelCase						)
																			lowercase        =							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)
																			lowercase        =							dummy_past_residuals[: new_scheduler.config.solver_order]

													lowercase        =							scheduler.step(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				**_lowerCAmelCase						).prev_sample
													lowercase        =							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   _a   (    self ,				_lowerCAmelCase=None ,				**_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							if scheduler is None:
													lowercase        =							self.scheduler_classes[0]
													lowercase        =							self.get_scheduler_config(**_lowerCAmelCase						)
													lowercase        =							scheduler_class(**_lowerCAmelCase						)

							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config(**_lowerCAmelCase						)
							lowercase        =							scheduler_class(**_lowerCAmelCase						)

							lowercase        =							10
							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter
							scheduler.set_timesteps(_lowerCAmelCase						)

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

							return sample







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							lowercase        =							50
							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter
							scheduler.set_timesteps(_lowerCAmelCase						)

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

							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.2574						) < 1E-3







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							lowercase        =							DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							lowercase        =							self.full_loop(scheduler=_lowerCAmelCase						)
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.2791						) < 1E-3

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

							lowercase        =							self.full_loop(scheduler=_lowerCAmelCase						)
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.2791						) < 1E-3







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							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   _a   (    self						)    ->    str:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							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=_lowerCAmelCase ,				solver_type=_lowerCAmelCase ,				prediction_type=_lowerCAmelCase ,				algorithm_type=_lowerCAmelCase ,				)
																															lowercase        =							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   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							self.check_over_configs(lower_order_final=_lowerCAmelCase						)
							self.check_over_configs(lower_order_final=_lowerCAmelCase						)







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							self.check_over_configs(variance_type=_lowerCAmelCase						)
							self.check_over_configs(variance_type="""learned_range"""						)







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							self.full_loop()
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.2791						) < 1E-3







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.full_loop(use_karras_sigmas=_lowerCAmelCase						)
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.2248						) < 1E-3







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.full_loop(prediction_type="""v_prediction"""						)
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.1453						) < 1E-3







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							self.full_loop(prediction_type="""v_prediction""" ,				use_karras_sigmas=_lowerCAmelCase						)
							lowercase        =							torch.mean(torch.abs(_lowerCAmelCase						)						)

							assert abs(result_mean.item() - 0.0649						) < 1E-3







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config(thresholding=_lowerCAmelCase ,				dynamic_thresholding_ratio=0						)
							lowercase        =							scheduler_class(**_lowerCAmelCase						)

							lowercase        =							10
							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter.half()
							scheduler.set_timesteps(_lowerCAmelCase						)

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

							assert sample.dtype == torch.floataa







'''simple docstring'''


def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							[]
						lowercase        =							1

						while len(lowercase_						) < 1E6:
												constant.append(str(lowercase_						)						)
												i += 1

						lowercase        =							"""""".join(lowercase_						)

						return (
						    int(constant[0]						)
						    * int(constant[9]						)
						    * int(constant[99]						)
						    * int(constant[999]						)
						    * int(constant[9999]						)
						    * int(constant[9_9999]						)
						    * int(constant[99_9999]						)
						)


if __name__ == "__main__":
						print(solution())


