





'''simple docstring'''



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Tuple     =    (DEISMultistepScheduler,)
	a      :			List[str]     =    (("num_inference_steps", 25),)


	def   _UpperCAmelCase       (self       ,**_lowerCamelCase	)							->							int:







				'''simple docstring'''





				__lowercase			  =							{
				    '''num_train_timesteps''': 1000,
				    '''beta_start''': 0.0_0_0_1,
				    '''beta_end''': 0.0_2,
				    '''beta_schedule''': '''linear''',
				    '''solver_order''': 2,
				}

				config.update(**_lowerCamelCase	)
				return config


	def   _UpperCAmelCase       (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 + 0.1_5, residual + 0.1_0]

				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   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self       ,_lowerCamelCase=0       ,**_lowerCamelCase	)							->							str:







				'''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 + 0.1_5, residual + 0.1_0]

				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   _UpperCAmelCase       (self       ,_lowerCamelCase=None       ,**_lowerCamelCase	)							->							int:







				'''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   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							dict(self.forward_default_kwargs	)

				__lowercase			  =							kwargs.pop('''num_inference_steps'''       ,_lowerCamelCase	)

				for scheduler_class in self.scheduler_classes:
							__lowercase			  =							self.get_scheduler_config()
							__lowercase			  =							scheduler_class(**_lowerCamelCase	)

							__lowercase			  =							self.dummy_sample
							__lowercase			  =							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'''	):
										__lowercase			  =							num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							__lowercase			  =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
							__lowercase			  =							dummy_past_residuals[: scheduler.config.solver_order]

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

							__lowercase			  =							scheduler.step(_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,**_lowerCamelCase	).prev_sample
							__lowercase			  =							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[int]:







				'''simple docstring'''





				__lowercase			  =							DEISMultistepScheduler(**self.get_scheduler_config()	)
				__lowercase			  =							self.full_loop(scheduler=_lowerCamelCase	)
				__lowercase			  =							torch.mean(torch.abs(_lowerCamelCase	)	)

				assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1E-3

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

				__lowercase			  =							self.full_loop(scheduler=_lowerCamelCase	)
				__lowercase			  =							torch.mean(torch.abs(_lowerCamelCase	)	)

				assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1E-3


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				self.check_over_configs(thresholding=_lowerCamelCase	)
				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=_lowerCamelCase       ,prediction_type=_lowerCamelCase       ,sample_max_value=_lowerCamelCase       ,algorithm_type='''deis'''       ,solver_order=_lowerCamelCase       ,solver_type=_lowerCamelCase       ,)


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self	)							->							str:







				'''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=_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   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				self.check_over_configs(lower_order_final=_lowerCamelCase	)
				self.check_over_configs(lower_order_final=_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							int:







				'''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   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							self.full_loop()
				__lowercase			  =							torch.mean(torch.abs(_lowerCamelCase	)	)

				assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1E-3


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							self.full_loop(prediction_type='''v_prediction'''	)
				__lowercase			  =							torch.mean(torch.abs(_lowerCamelCase	)	)

				assert abs(result_mean.item() - 0.0_9_1	) < 1E-3


	def   _UpperCAmelCase       (self	)							->							int:







				'''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'''



import numpy as np




def 				_lowerCAmelCase  (			lowerCamelCase_     :					np.array			):
			return 1 / (1 + np.exp(-vector			))




def 				_lowerCAmelCase  (			lowerCamelCase_     :					np.array			):
			return vector * sigmoid(1.7_02 * vector			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





