

"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 			_UpperCAmelCase	(						__a):
			__a       :				int     =							(UniPCMultistepScheduler,)
			__a       :				Union[str, Any]     =							(("""num_inference_steps""", 2_5),)
			def 							__snake_case       (						self					,						**_A   )			->			List[str]:






				'''simple docstring'''







				_UpperCAmelCase			:							int	  =    {
				    'num_train_timesteps': 10_00,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'solver_order': 2,
				    'solver_type': 'bh2',
				}

				config.update(**__UpperCAmelCase   )
				return config
			def 							__snake_case       (						self					,						_A=0					,						**_A   )			->			str:






				'''simple docstring'''







				_UpperCAmelCase			:							int	  =    dict(self.forward_default_kwargs   )
				_UpperCAmelCase			:							Union[str, Any]	  =    kwargs.pop("""num_inference_steps"""					,						__UpperCAmelCase   )
				_UpperCAmelCase			:							List[str]	  =    self.dummy_sample
				_UpperCAmelCase			:							Union[str, Any]	  =    0.1 * sample
				_UpperCAmelCase			:							int	  =    [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					_UpperCAmelCase			:							Any	  =    self.get_scheduler_config(**__UpperCAmelCase   )
					_UpperCAmelCase			:							List[Any]	  =    scheduler_class(**__UpperCAmelCase   )
					scheduler.set_timesteps(__UpperCAmelCase   )
					# copy over dummy past residuals
					_UpperCAmelCase			:							int	  =    dummy_past_residuals[: scheduler.config.solver_order]

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

					_UpperCAmelCase			:							int	  =    sample, sample
					for t in range(__UpperCAmelCase					,						time_step + scheduler.config.solver_order + 1   ):
						_UpperCAmelCase			:							List[str]	  =    scheduler.step(__UpperCAmelCase					,						__UpperCAmelCase					,						__UpperCAmelCase					,						**__UpperCAmelCase   ).prev_sample
						_UpperCAmelCase			:							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 							__snake_case       (						self					,						_A=0					,						**_A   )			->			Optional[Any]:






				'''simple docstring'''







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

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

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

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(__UpperCAmelCase   )
						_UpperCAmelCase			:							Any	  =    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)
						_UpperCAmelCase			:							str	  =    dummy_past_residuals[: new_scheduler.config.solver_order]

					_UpperCAmelCase			:							int	  =    scheduler.step(__UpperCAmelCase					,						__UpperCAmelCase					,						__UpperCAmelCase					,						**__UpperCAmelCase   ).prev_sample
					_UpperCAmelCase			:							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 							__snake_case       (						self					,						_A=None					,						**_A   )			->			Any:






				'''simple docstring'''







				if scheduler is None:
					_UpperCAmelCase			:							Optional[int]	  =    self.scheduler_classes[0]
					_UpperCAmelCase			:							List[Any]	  =    self.get_scheduler_config(**__UpperCAmelCase   )
					_UpperCAmelCase			:							Optional[Any]	  =    scheduler_class(**__UpperCAmelCase   )

				_UpperCAmelCase			:							Optional[Any]	  =    self.scheduler_classes[0]
				_UpperCAmelCase			:							Optional[int]	  =    self.get_scheduler_config(**__UpperCAmelCase   )
				_UpperCAmelCase			:							Union[str, Any]	  =    scheduler_class(**__UpperCAmelCase   )

				_UpperCAmelCase			:							Union[str, Any]	  =    10
				_UpperCAmelCase			:							Tuple	  =    self.dummy_model()
				_UpperCAmelCase			:							int	  =    self.dummy_sample_deter
				scheduler.set_timesteps(__UpperCAmelCase   )

				for i, t in enumerate(scheduler.timesteps   ):
					_UpperCAmelCase			:							List[str]	  =    model(__UpperCAmelCase					,						__UpperCAmelCase   )
					_UpperCAmelCase			:							Optional[Any]	  =    scheduler.step(__UpperCAmelCase					,						__UpperCAmelCase					,						__UpperCAmelCase   ).prev_sample

				return sample
			def 							__snake_case       (						self   )			->			int:






				'''simple docstring'''







				_UpperCAmelCase			:							Optional[int]	  =    dict(self.forward_default_kwargs   )

				_UpperCAmelCase			:							Union[str, Any]	  =    kwargs.pop("""num_inference_steps"""					,						__UpperCAmelCase   )

				for scheduler_class in self.scheduler_classes:
					_UpperCAmelCase			:							Optional[Any]	  =    self.get_scheduler_config()
					_UpperCAmelCase			:							Dict	  =    scheduler_class(**__UpperCAmelCase   )

					_UpperCAmelCase			:							Optional[Any]	  =    self.dummy_sample
					_UpperCAmelCase			:							Optional[Any]	  =    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"""   ):
						_UpperCAmelCase			:							List[str]	  =    num_inference_steps

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

					_UpperCAmelCase			:							Optional[int]	  =    scheduler.timesteps[5]
					_UpperCAmelCase			:							Optional[int]	  =    scheduler.timesteps[6]

					_UpperCAmelCase			:							str	  =    scheduler.step(__UpperCAmelCase					,						__UpperCAmelCase					,						__UpperCAmelCase					,						**__UpperCAmelCase   ).prev_sample
					_UpperCAmelCase			:							List[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 							__snake_case       (						self   )			->			int:






				'''simple docstring'''







				_UpperCAmelCase			:							List[Any]	  =    UniPCMultistepScheduler(**self.get_scheduler_config()   )
				_UpperCAmelCase			:							Optional[int]	  =    self.full_loop(scheduler=__UpperCAmelCase   )
				_UpperCAmelCase			:							Optional[int]	  =    torch.mean(torch.abs(__UpperCAmelCase   )   )

				assert abs(result_mean.item() - 0.2464   ) < 1e-3

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

				_UpperCAmelCase			:							str	  =    self.full_loop(scheduler=__UpperCAmelCase   )
				_UpperCAmelCase			:							int	  =    torch.mean(torch.abs(__UpperCAmelCase   )   )

				assert abs(result_mean.item() - 0.2464   ) < 1e-3
			def 							__snake_case       (						self   )			->			Union[str, Any]:






				'''simple docstring'''







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






				'''simple docstring'''







				self.check_over_configs(thresholding=__UpperCAmelCase   )
				for order in [1, 2, 3]:
					for solver_type in ["bh1", "bh2"]:
						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					,						solver_order=__UpperCAmelCase					,						solver_type=__UpperCAmelCase					,						)
			def 							__snake_case       (						self   )			->			List[str]:






				'''simple docstring'''







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






				'''simple docstring'''







				for solver_type in ["bh1", "bh2"]:
					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					,						)
							_UpperCAmelCase			:							List[str]	  =    self.full_loop(
							    solver_order=__UpperCAmelCase					,						solver_type=__UpperCAmelCase					,						prediction_type=__UpperCAmelCase					,						)
							assert not torch.isnan(__UpperCAmelCase   ).any(), "Samples have nan numbers"
			def 							__snake_case       (						self   )			->			List[str]:






				'''simple docstring'''







				self.check_over_configs(lower_order_final=__UpperCAmelCase   )
				self.check_over_configs(lower_order_final=__UpperCAmelCase   )
			def 							__snake_case       (						self   )			->			Union[str, Any]:






				'''simple docstring'''







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






				'''simple docstring'''







				_UpperCAmelCase			:							str	  =    self.full_loop()
				_UpperCAmelCase			:							Dict	  =    torch.mean(torch.abs(__UpperCAmelCase   )   )

				assert abs(result_mean.item() - 0.2464   ) < 1e-3
			def 							__snake_case       (						self   )			->			List[Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							str	  =    self.full_loop(prediction_type="""v_prediction"""   )
				_UpperCAmelCase			:							List[str]	  =    torch.mean(torch.abs(__UpperCAmelCase   )   )

				assert abs(result_mean.item() - 0.1014   ) < 1e-3
			def 							__snake_case       (						self   )			->			List[Any]:






				'''simple docstring'''







				_UpperCAmelCase			:							Optional[int]	  =    self.scheduler_classes[0]
				_UpperCAmelCase			:							int	  =    self.get_scheduler_config(thresholding=__UpperCAmelCase					,						dynamic_thresholding_ratio=0   )
				_UpperCAmelCase			:							List[Any]	  =    scheduler_class(**__UpperCAmelCase   )

				_UpperCAmelCase			:							int	  =    10
				_UpperCAmelCase			:							Any	  =    self.dummy_model()
				_UpperCAmelCase			:							Dict	  =    self.dummy_sample_deter.half()
				scheduler.set_timesteps(__UpperCAmelCase   )

				for i, t in enumerate(scheduler.timesteps   ):
					_UpperCAmelCase			:							Any	  =    model(__UpperCAmelCase					,						__UpperCAmelCase   )
					_UpperCAmelCase			:							int	  =    scheduler.step(__UpperCAmelCase					,						__UpperCAmelCase					,						__UpperCAmelCase   ).prev_sample

				assert sample.dtype == torch.floataa
			def 							__snake_case       (						self					,						**_A   )			->			Optional[int]:






				'''simple docstring'''







				for scheduler_class in self.scheduler_classes:
					_UpperCAmelCase			:							int	  =    self.get_scheduler_config(**__UpperCAmelCase   )
					_UpperCAmelCase			:							Optional[Any]	  =    scheduler_class(**__UpperCAmelCase   )

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












"""simple docstring"""





from __future__ import annotations

__A											=		1.6_021e-19  # units = C
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   )					->tuple[str, float]:





  """simple docstring"""


  if (conductivity, electron_conc, mobility).count(0			) != 1:
    raise ValueError('You cannot supply more or less than 2 values'			)
  elif conductivity < 0:
    raise ValueError('Conductivity cannot be negative'			)
  elif electron_conc < 0:
    raise ValueError('Electron concentration cannot be negative'			)
  elif mobility < 0:
    raise ValueError('mobility cannot be negative'			)
  elif conductivity == 0:
    return (
        "conductivity",
        mobility * electron_conc * ELECTRON_CHARGE,
    )
  elif electron_conc == 0:
    return (
        "electron_conc",
        conductivity / (mobility * ELECTRON_CHARGE),
    )
  else:
    return (
        "mobility",
        conductivity / (electron_conc * ELECTRON_CHARGE),
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
