
'''simple docstring'''


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		int	      =			(DDIMParallelScheduler,)
			lowerCamelCase			:		Optional[int]	      =			(("eta", 0.0), ("num_inference_steps", 50))

			def        UpperCAmelCase__ (self			,   **A      ):
								lowerCamelCase_					:      List[Any]     =		{
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.00_01,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**A      )
								return config

			def        UpperCAmelCase__ (self			,   **A      ):
								lowerCamelCase_					:      int     =		self.scheduler_classes[0]
								lowerCamelCase_					:      List[Any]     =		self.get_scheduler_config(**A      )
								lowerCamelCase_					:      str     =		scheduler_class(**A      )

								lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		1_0, 0.0

								lowerCamelCase_					:      Dict     =		self.dummy_model()
								lowerCamelCase_					:      str     =		self.dummy_sample_deter

								scheduler.set_timesteps(A      )

								for t in scheduler.timesteps:
													lowerCamelCase_					:      List[Any]     =		model(A			,   A      )
													lowerCamelCase_					:      List[str]     =		scheduler.step(A			,   A			,   A			,   A      ).prev_sample

								return sample

			def        UpperCAmelCase__ (self      ):
								for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=A      )

			def        UpperCAmelCase__ (self      ):
								for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=A      )

								lowerCamelCase_					:      Union[str, Any]     =		self.scheduler_classes[0]
								lowerCamelCase_					:      Dict     =		self.get_scheduler_config(steps_offset=1      )
								lowerCamelCase_					:      str     =		scheduler_class(**A      )
								scheduler.set_timesteps(5      )
								assert torch.equal(scheduler.timesteps			,   torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]      )      )

			def        UpperCAmelCase__ (self      ):
								for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,   [0.0_02, 0.02, 0.2, 2]      ):
													self.check_over_configs(beta_start=A			,   beta_end=A      )

			def        UpperCAmelCase__ (self      ):
								for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=A      )

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

			def        UpperCAmelCase__ (self      ):
								for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=A      )

			def        UpperCAmelCase__ (self      ):
								for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=A      )

			def        UpperCAmelCase__ (self      ):
								for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=A      )

			def        UpperCAmelCase__ (self      ):
								self.check_over_configs(thresholding=A      )
								for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=A			,   prediction_type=A			,   sample_max_value=A			,   )

			def        UpperCAmelCase__ (self      ):
								for t in [1, 1_0, 4_9]:
													self.check_over_forward(time_step=A      )

			def        UpperCAmelCase__ (self      ):
								for t, num_inference_steps in zip([1, 1_0, 5_0]			,   [1_0, 5_0, 5_0_0]      ):
													self.check_over_forward(time_step=A			,   num_inference_steps=A      )

			def        UpperCAmelCase__ (self      ):
								for t, eta in zip([1, 1_0, 4_9]			,   [0.0, 0.5, 1.0]      ):
													self.check_over_forward(time_step=A			,   eta=A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		self.scheduler_classes[0]
								lowerCamelCase_					:      str     =		self.get_scheduler_config()
								lowerCamelCase_					:      Tuple     =		scheduler_class(**A      )

								assert torch.sum(torch.abs(scheduler._get_variance(0			,   0      ) - 0.0      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_2_0			,   4_0_0      ) - 0.1_47_71      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_8_0			,   9_6_0      ) - 0.3_24_60      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0			,   0      ) - 0.0      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			,   4_8_6      ) - 0.0_09_79      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			,   9_9_8      ) - 0.02      )      ) < 1E-5

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		self.scheduler_classes[0]
								lowerCamelCase_					:      Tuple     =		self.get_scheduler_config()
								lowerCamelCase_					:      List[Any]     =		scheduler_class(**A      )

								lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		1_0, 0.0
								scheduler.set_timesteps(A      )

								lowerCamelCase_					:      int     =		self.dummy_model()
								lowerCamelCase_					:      str     =		self.dummy_sample_deter
								lowerCamelCase_					:      Any     =		self.dummy_sample_deter + 0.1
								lowerCamelCase_					:      Union[str, Any]     =		self.dummy_sample_deter - 0.1

								lowerCamelCase_					:      Tuple     =		samplea.shape[0]
								lowerCamelCase_					:      Optional[Any]     =		torch.stack([samplea, samplea, samplea]			,   dim=0      )
								lowerCamelCase_					:      Optional[Any]     =		torch.arange(A      )[0:3, None].repeat(1			,   A      )

								lowerCamelCase_					:      List[Any]     =		model(samples.flatten(0			,   1      )			,   timesteps.flatten(0			,   1      )      )
								lowerCamelCase_					:      Union[str, Any]     =		scheduler.batch_step_no_noise(A			,   timesteps.flatten(0			,   1      )			,   samples.flatten(0			,   1      )			,   A      )

								lowerCamelCase_					:      Dict     =		torch.sum(torch.abs(A      )      )
								lowerCamelCase_					:      Optional[int]     =		torch.mean(torch.abs(A      )      )

								assert abs(result_sum.item() - 11_47.79_04      ) < 1E-2
								assert abs(result_mean.item() - 0.49_82      ) < 1E-3

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		self.full_loop()

								lowerCamelCase_					:      Tuple     =		torch.sum(torch.abs(A      )      )
								lowerCamelCase_					:      Tuple     =		torch.mean(torch.abs(A      )      )

								assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
								assert abs(result_mean.item() - 0.22_39_67      ) < 1E-3

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		self.full_loop(prediction_type='''v_prediction'''      )

								lowerCamelCase_					:      str     =		torch.sum(torch.abs(A      )      )
								lowerCamelCase_					:      List[Any]     =		torch.mean(torch.abs(A      )      )

								assert abs(result_sum.item() - 52.53_02      ) < 1E-2
								assert abs(result_mean.item() - 0.06_84      ) < 1E-3

			def        UpperCAmelCase__ (self      ):
								# We specify different beta, so that the first alpha is 0.99
								lowerCamelCase_					:      List[str]     =		self.full_loop(set_alpha_to_one=A			,   beta_start=0.01      )
								lowerCamelCase_					:      List[Any]     =		torch.sum(torch.abs(A      )      )
								lowerCamelCase_					:      Any     =		torch.mean(torch.abs(A      )      )

								assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
								assert abs(result_mean.item() - 0.19_51      ) < 1E-3






			def        UpperCAmelCase__ (self      ):
								# We specify different beta, so that the first alpha is 0.99
								lowerCamelCase_					:      int     =		self.full_loop(set_alpha_to_one=A			,   beta_start=0.01      )
								lowerCamelCase_					:      Optional[Any]     =		torch.sum(torch.abs(A      )      )
								lowerCamelCase_					:      List[Any]     =		torch.mean(torch.abs(A      )      )

								assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
								assert abs(result_mean.item() - 0.19_41      ) < 1E-3




'''simple docstring'''


from __future__ import annotations




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							dict[str, float]:
					'''simple docstring'''

					if (voltage, current, resistance).count(0				) != 1:
										raise ValueError('''One and only one argument must be 0'''				)
					if resistance < 0:
										raise ValueError('''Resistance cannot be negative'''				)
					if voltage == 0:
										return {"voltage": float(current * resistance				)}
					elif current == 0:
										return {"current": voltage / resistance}
					elif resistance == 0:
										return {"resistance": voltage / current}
					else:
										raise ValueError('''Exactly one argument must be 0'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


