

'''simple docstring'''
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class    a__  (							a__  ):
   _SCREAMING_SNAKE_CASE    :     Any	       =							(DPMSolverSinglestepScheduler,)
   _SCREAMING_SNAKE_CASE    :     str	       =							(("""num_inference_steps""", 25),)


   def 						_lowerCamelCase   ( self			,				**_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      List[Any]			     =   {
          "num_train_timesteps": 1000,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "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 						_lowerCamelCase   ( self			,				_UpperCamelCase=0			,				**_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Any			     =   dict(self.forward_default_kwargs  )
      _lowercase			:      Dict			     =   kwargs.pop("num_inference_steps"			,				lowerCAmelCase__  )
      _lowercase			:      List[str]			     =   self.dummy_sample
      _lowercase			:      List[Any]			     =   0.1 * sample
      _lowercase			:      int			     =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

         _lowercase			:      Union[str, Any]			     =   sample, sample
         for t in range(lowerCAmelCase__			,				time_step + scheduler.config.solver_order + 1  ):
            _lowercase			:      List[str]			     =   scheduler.step(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				**lowerCAmelCase__  ).prev_sample
            _lowercase			:      Optional[Any]			     =   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 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      pass


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase=0			,				**_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Union[str, Any]			     =   dict(self.forward_default_kwargs  )
      _lowercase			:      int			     =   kwargs.pop("num_inference_steps"			,				lowerCAmelCase__  )
      _lowercase			:      List[str]			     =   self.dummy_sample
      _lowercase			:      Optional[int]			     =   0.1 * sample
      _lowercase			:      Tuple			     =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
         _lowercase			:      Dict			     =   self.get_scheduler_config()
         _lowercase			:      str			     =   scheduler_class(**lowerCAmelCase__  )
         scheduler.set_timesteps(lowerCAmelCase__  )

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

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowerCAmelCase__  )
            _lowercase			:      List[str]			     =   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			:      List[str]			     =   dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowercase			:      List[str]			     =   scheduler.step(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				**lowerCAmelCase__  ).prev_sample
         _lowercase			:      Optional[int]			     =   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 						_lowerCamelCase   ( self			,				_UpperCamelCase=None			,				**_UpperCamelCase  ):





      """simple docstring"""





      if scheduler is None:
         _lowercase			:      Dict			     =   self.scheduler_classes[0]
         _lowercase			:      int			     =   self.get_scheduler_config(**lowerCAmelCase__  )
         _lowercase			:      Tuple			     =   scheduler_class(**lowerCAmelCase__  )

      _lowercase			:      List[Any]			     =   self.scheduler_classes[0]
      _lowercase			:      Optional[Any]			     =   self.get_scheduler_config(**lowerCAmelCase__  )
      _lowercase			:      List[Any]			     =   scheduler_class(**lowerCAmelCase__  )

      _lowercase			:      int			     =   10
      _lowercase			:      str			     =   self.dummy_model()
      _lowercase			:      Union[str, Any]			     =   self.dummy_sample_deter
      scheduler.set_timesteps(lowerCAmelCase__  )

      for i, t in enumerate(scheduler.timesteps  ):
         _lowercase			:      Optional[int]			     =   model(lowerCAmelCase__			,				lowerCAmelCase__  )
         _lowercase			:      Tuple			     =   scheduler.step(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  ).prev_sample

      return sample


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Union[str, Any]			     =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
      _lowercase			:      Optional[int]			     =   50
      _lowercase			:      List[str]			     =   self.dummy_model()
      _lowercase			:      Union[str, Any]			     =   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			:      Any			     =   model(lowerCAmelCase__			,				lowerCAmelCase__  )
         _lowercase			:      Union[str, Any]			     =   scheduler.step(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  ).prev_sample

      _lowercase			:      Union[str, Any]			     =   torch.mean(torch.abs(lowerCAmelCase__  )  )

      assert abs(result_mean.item() - 0.2_5_7_4  ) < 1E-3


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Dict			     =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
      _lowercase			:      List[Any]			     =   self.full_loop(scheduler=lowerCAmelCase__  )
      _lowercase			:      int			     =   torch.mean(torch.abs(lowerCAmelCase__  )  )

      assert abs(result_mean.item() - 0.2_7_9_1  ) < 1E-3

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

      _lowercase			:      List[Any]			     =   self.full_loop(scheduler=lowerCAmelCase__  )
      _lowercase			:      int			     =   torch.mean(torch.abs(lowerCAmelCase__  )  )

      assert abs(result_mean.item() - 0.2_7_9_1  ) < 1E-3


   def 						_lowerCamelCase   ( self  ):





      """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 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self  ):





      """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			:      Optional[Any]			     =   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 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      self.check_over_configs(lower_order_final=lowerCAmelCase__  )
      self.check_over_configs(lower_order_final=lowerCAmelCase__  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self  ):





      """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 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Any			     =   self.full_loop()
      _lowercase			:      Union[str, Any]			     =   torch.mean(torch.abs(lowerCAmelCase__  )  )

      assert abs(result_mean.item() - 0.2_7_9_1  ) < 1E-3


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   self.full_loop(use_karras_sigmas=lowerCAmelCase__  )
      _lowercase			:      int			     =   torch.mean(torch.abs(lowerCAmelCase__  )  )

      assert abs(result_mean.item() - 0.2_2_4_8  ) < 1E-3


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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

      assert abs(result_mean.item() - 0.1_4_5_3  ) < 1E-3


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      List[str]			     =   self.full_loop(prediction_type="v_prediction"			,				use_karras_sigmas=lowerCAmelCase__  )
      _lowercase			:      int			     =   torch.mean(torch.abs(lowerCAmelCase__  )  )

      assert abs(result_mean.item() - 0.0_6_4_9  ) < 1E-3






   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      int			     =   self.scheduler_classes[0]
      _lowercase			:      Optional[Any]			     =   self.get_scheduler_config(thresholding=lowerCAmelCase__			,				dynamic_thresholding_ratio=0  )
      _lowercase			:      Tuple			     =   scheduler_class(**lowerCAmelCase__  )

      _lowercase			:      Optional[int]			     =   10
      _lowercase			:      List[Any]			     =   self.dummy_model()
      _lowercase			:      List[str]			     =   self.dummy_sample_deter.half()
      scheduler.set_timesteps(lowerCAmelCase__  )

      for i, t in enumerate(scheduler.timesteps  ):
         _lowercase			:      Any			     =   model(lowerCAmelCase__			,				lowerCAmelCase__  )
         _lowercase			:      Any			     =   scheduler.step(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  ).prev_sample

      assert sample.dtype == torch.floataa













"""simple docstring"""




from __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->dict[str, float]:






	'''simple docstring'''






	if (inductance, frequency, reactance).count(0					) != 1:
		raise ValueError("One and only one argument must be 0"					)
	if inductance < 0:
		raise ValueError("Inductance cannot be negative"					)
	if frequency < 0:
		raise ValueError("Frequency cannot be negative"					)
	if reactance < 0:
		raise ValueError("Inductive reactance cannot be negative"					)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	else:
		raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


