

"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  (DEISMultistepScheduler,)
				SCREAMING_SNAKE_CASE_   :      Dict         =  (("""num_inference_steps""", 25),)

				def 							lowercase_     (	self							:				str     ,  **UpperCamelCase__							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			{
						    "num_train_timesteps": 1_0_0_0,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "solver_order": 2,
						}

						config.update(**UpperCamelCase__)
						return config

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Optional[Any]=0     ,  **UpperCamelCase__							:				Any)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			dict(self.forward_default_kwargs)
						__lowerCAmelCase:       Optional[Any]          =			kwargs.pop("num_inference_steps"     ,  UpperCamelCase__)
						__lowerCAmelCase:       str          =			self.dummy_sample
						__lowerCAmelCase:       Any          =			0.1 * sample
						__lowerCAmelCase:       Any          =			[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
								__lowerCAmelCase:       Optional[Any]          =			self.get_scheduler_config(**UpperCamelCase__)
								__lowerCAmelCase:       Dict          =			scheduler_class(**UpperCamelCase__)
								scheduler.set_timesteps(UpperCamelCase__)
								# copy over dummy past residuals
								__lowerCAmelCase:       int          =			dummy_past_residuals[: scheduler.config.solver_order]

								with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(UpperCamelCase__)
										__lowerCAmelCase:       Optional[int]          =			scheduler_class.from_pretrained(UpperCamelCase__)
										new_scheduler.set_timesteps(UpperCamelCase__)
										# copy over dummy past residuals
										__lowerCAmelCase:       Tuple          =			dummy_past_residuals[: new_scheduler.config.solver_order]

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

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

				def 							lowercase_     (	self							:				List[str])->   Dict:



						'''simple docstring'''


						pass

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Optional[int]=0     ,  **UpperCamelCase__							:				List[Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			dict(self.forward_default_kwargs)
						__lowerCAmelCase:       Tuple          =			kwargs.pop("num_inference_steps"     ,  UpperCamelCase__)
						__lowerCAmelCase:       str          =			self.dummy_sample
						__lowerCAmelCase:       Optional[Any]          =			0.1 * sample
						__lowerCAmelCase:       Optional[Any]          =			[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
								__lowerCAmelCase:       Union[str, Any]          =			self.get_scheduler_config()
								__lowerCAmelCase:       Optional[int]          =			scheduler_class(**UpperCamelCase__)
								scheduler.set_timesteps(UpperCamelCase__)

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

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

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

								__lowerCAmelCase:       List[str]          =			scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__).prev_sample
								__lowerCAmelCase:       Union[str, Any]          =			new_scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__).prev_sample

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

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				str=None     ,  **UpperCamelCase__							:				Tuple)->   List[str]:



						'''simple docstring'''


						if scheduler is None:
								__lowerCAmelCase:       Tuple          =			self.scheduler_classes[0]
								__lowerCAmelCase:       Tuple          =			self.get_scheduler_config(**UpperCamelCase__)
								__lowerCAmelCase:       Tuple          =			scheduler_class(**UpperCamelCase__)

						__lowerCAmelCase:       Dict          =			self.scheduler_classes[0]
						__lowerCAmelCase:       List[Any]          =			self.get_scheduler_config(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			scheduler_class(**UpperCamelCase__)

						__lowerCAmelCase:       Optional[Any]          =			1_0
						__lowerCAmelCase:       Optional[Any]          =			self.dummy_model()
						__lowerCAmelCase:       Optional[Any]          =			self.dummy_sample_deter
						scheduler.set_timesteps(UpperCamelCase__)

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

						return sample

				def 							lowercase_     (	self							:				List[str])->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			dict(self.forward_default_kwargs)

						__lowerCAmelCase:       Optional[Any]          =			kwargs.pop("num_inference_steps"     ,  UpperCamelCase__)

						for scheduler_class in self.scheduler_classes:
								__lowerCAmelCase:       Optional[Any]          =			self.get_scheduler_config()
								__lowerCAmelCase:       Union[str, Any]          =			scheduler_class(**UpperCamelCase__)

								__lowerCAmelCase:       Optional[Any]          =			self.dummy_sample
								__lowerCAmelCase:       List[str]          =			0.1 * sample

								if num_inference_steps is not None and hasattr(UpperCamelCase__     ,  "set_timesteps"):
										scheduler.set_timesteps(UpperCamelCase__)
								elif num_inference_steps is not None and not hasattr(UpperCamelCase__     ,  "set_timesteps"):
										__lowerCAmelCase:       List[Any]          =			num_inference_steps

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

								__lowerCAmelCase:       Any          =			scheduler.timesteps[5]
								__lowerCAmelCase:       str          =			scheduler.timesteps[6]

								__lowerCAmelCase:       Optional[Any]          =			scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__).prev_sample
								__lowerCAmelCase:       str          =			scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__).prev_sample

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

				def 							lowercase_     (	self							:				Any)->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			DEISMultistepScheduler(**self.get_scheduler_config())
						__lowerCAmelCase:       Dict          =			self.full_loop(scheduler=UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			torch.mean(torch.abs(UpperCamelCase__))

						assert abs(result_mean.item() - 0.23916) < 1e-3

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

						__lowerCAmelCase:       Dict          =			self.full_loop(scheduler=UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			torch.mean(torch.abs(UpperCamelCase__))

						assert abs(result_mean.item() - 0.23916) < 1e-3

				def 							lowercase_     (	self							:				Union[str, Any])->   int:



						'''simple docstring'''


						for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
								self.check_over_configs(num_train_timesteps=UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   Union[str, Any]:



						'''simple docstring'''


						self.check_over_configs(thresholding=UpperCamelCase__)
						for order in [1, 2, 3]:
								for solver_type in ["logrho"]:
										for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(
														    thresholding=UpperCamelCase__     ,  prediction_type=UpperCamelCase__     ,  sample_max_value=UpperCamelCase__     ,  algorithm_type="deis"     ,  solver_order=UpperCamelCase__     ,  solver_type=UpperCamelCase__     ,  )

				def 							lowercase_     (	self							:				List[Any])->   List[str]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Union[str, Any])->   List[Any]:



						'''simple docstring'''


						for algorithm_type in ["deis"]:
								for solver_type in ["logrho"]:
										for order in [1, 2, 3]:
												for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(
														    solver_order=UpperCamelCase__     ,  solver_type=UpperCamelCase__     ,  prediction_type=UpperCamelCase__     ,  algorithm_type=UpperCamelCase__     ,  )
														__lowerCAmelCase:       List[Any]          =			self.full_loop(
														    solver_order=UpperCamelCase__     ,  solver_type=UpperCamelCase__     ,  prediction_type=UpperCamelCase__     ,  algorithm_type=UpperCamelCase__     ,  )
														assert not torch.isnan(UpperCamelCase__).any(), "Samples have nan numbers"

				def 							lowercase_     (	self							:				str)->   str:



						'''simple docstring'''


						self.check_over_configs(lower_order_final=UpperCamelCase__)
						self.check_over_configs(lower_order_final=UpperCamelCase__)

				def 							lowercase_     (	self							:				Any)->   Optional[int]:



						'''simple docstring'''


						for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
								self.check_over_forward(num_inference_steps=UpperCamelCase__     ,  time_step=0)

				def 							lowercase_     (	self							:				str)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.full_loop()
						__lowerCAmelCase:       List[str]          =			torch.mean(torch.abs(UpperCamelCase__))

						assert abs(result_mean.item() - 0.23916) < 1e-3

				def 							lowercase_     (	self							:				Dict)->   int:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.full_loop(prediction_type="v_prediction")
						__lowerCAmelCase:       Optional[int]          =			torch.mean(torch.abs(UpperCamelCase__))

						assert abs(result_mean.item() - 0.091) < 1e-3




				def 							lowercase_     (	self							:				int)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			self.scheduler_classes[0]
						__lowerCAmelCase:       int          =			self.get_scheduler_config(thresholding=UpperCamelCase__     ,  dynamic_thresholding_ratio=0)
						__lowerCAmelCase:       Dict          =			scheduler_class(**UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			1_0
						__lowerCAmelCase:       str          =			self.dummy_model()
						__lowerCAmelCase:       int          =			self.dummy_sample_deter.half()
						scheduler.set_timesteps(UpperCamelCase__)

						for i, t in enumerate(scheduler.timesteps):
								__lowerCAmelCase:       List[Any]          =			model(UpperCamelCase__     ,  UpperCamelCase__)
								__lowerCAmelCase:       Tuple          =			scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__).prev_sample

						assert sample.dtype == torch.floataa





"""simple docstring"""


from numpy import exp, pi, sqrt






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE = 0.0					, __SCREAMING_SNAKE_CASE = 1.0 )	->	int:
		return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()


