





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Union[str, Any]		       =  (DPMSolverSinglestepScheduler,)
      _UpperCAmelCase      :Any		       =  (('num_inference_steps', 2_5),)





      def 			__UpperCamelCase(					self			,				**A_  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  {
           "num_train_timesteps": 1000,
           "beta_start": 0.00_01,
           "beta_end": 0.02,
           "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(**A_  )
       return config





      def 			__UpperCamelCase(					self			,				A_=0			,				**A_  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  dict(self.forward_default_kwargs  )
       UpperCamelCase      :   Any			      =  kwargs.pop("num_inference_steps"			,				A_  )
       UpperCamelCase      :   Tuple			      =  self.dummy_sample
       UpperCamelCase      :   int			      =  0.1 * sample
       UpperCamelCase      :   Dict			      =  [residual + 0.2, residual + 0.15, residual + 0.10]

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

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(A_  )
         UpperCamelCase      :   Union[str, Any]			      =  scheduler_class.from_pretrained(A_  )
         new_scheduler.set_timesteps(A_  )
         # copy over dummy past residuals
         UpperCamelCase      :   List[str]			      =  dummy_past_residuals[: new_scheduler.config.solver_order]

        UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  sample, sample
        for t in range(A_			,				time_step + scheduler.config.solver_order + 1  ):
         UpperCamelCase      :   Optional[int]			      =  scheduler.step(A_			,				A_			,				A_			,				**A_  ).prev_sample
         UpperCamelCase      :   Optional[Any]			      =  new_scheduler.step(A_			,				A_			,				A_			,				**A_  ).prev_sample

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      def 			__UpperCamelCase(					self			,				A_=0			,				**A_  ):
       '''simple docstring'''




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

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

        # 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(A_  )
         UpperCamelCase      :   List[Any]			      =  scheduler_class.from_pretrained(A_  )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(A_  )

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

        UpperCamelCase      :   Dict			      =  scheduler.step(A_			,				A_			,				A_			,				**A_  ).prev_sample
        UpperCamelCase      :   Dict			      =  new_scheduler.step(A_			,				A_			,				A_			,				**A_  ).prev_sample

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





      def 			__UpperCamelCase(					self			,				A_=None			,				**A_  ):
       '''simple docstring'''




       if scheduler is None:
        UpperCamelCase      :   str			      =  self.scheduler_classes[0]
        UpperCamelCase      :   Optional[int]			      =  self.get_scheduler_config(**A_  )
        UpperCamelCase      :   Optional[Any]			      =  scheduler_class(**A_  )

       UpperCamelCase      :   Optional[int]			      =  self.scheduler_classes[0]
       UpperCamelCase      :   Optional[Any]			      =  self.get_scheduler_config(**A_  )
       UpperCamelCase      :   List[str]			      =  scheduler_class(**A_  )

       UpperCamelCase      :   List[str]			      =  10
       UpperCamelCase      :   Any			      =  self.dummy_model()
       UpperCamelCase      :   Optional[int]			      =  self.dummy_sample_deter
       scheduler.set_timesteps(A_  )

       for i, t in enumerate(scheduler.timesteps  ):
        UpperCamelCase      :   List[str]			      =  model(A_			,				A_  )
        UpperCamelCase      :   Tuple			      =  scheduler.step(A_			,				A_			,				A_  ).prev_sample

       return sample





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
       UpperCamelCase      :   str			      =  50
       UpperCamelCase      :   Dict			      =  self.dummy_model()
       UpperCamelCase      :   str			      =  self.dummy_sample_deter
       scheduler.set_timesteps(A_  )

       # make sure that the first t is uneven
       for i, t in enumerate(scheduler.timesteps[3:]  ):
        UpperCamelCase      :   List[str]			      =  model(A_			,				A_  )
        UpperCamelCase      :   Any			      =  scheduler.step(A_			,				A_			,				A_  ).prev_sample

       UpperCamelCase      :   Tuple			      =  torch.mean(torch.abs(A_  )  )

       assert abs(result_mean.item() - 0.25_74  ) < 1e-3





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
       UpperCamelCase      :   Optional[int]			      =  self.full_loop(scheduler=A_  )
       UpperCamelCase      :   Tuple			      =  torch.mean(torch.abs(A_  )  )

       assert abs(result_mean.item() - 0.27_91  ) < 1e-3

       UpperCamelCase      :   Optional[Any]			      =  DEISMultistepScheduler.from_config(scheduler.config  )
       UpperCamelCase      :   List[Any]			      =  DPMSolverMultistepScheduler.from_config(scheduler.config  )
       UpperCamelCase      :   Optional[int]			      =  UniPCMultistepScheduler.from_config(scheduler.config  )
       UpperCamelCase      :   Union[str, Any]			      =  DPMSolverSinglestepScheduler.from_config(scheduler.config  )

       UpperCamelCase      :   int			      =  self.full_loop(scheduler=A_  )
       UpperCamelCase      :   int			      =  torch.mean(torch.abs(A_  )  )

       assert abs(result_mean.item() - 0.27_91  ) < 1e-3





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.check_over_configs(thresholding=A_  )
       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=A_			,				prediction_type=A_			,				sample_max_value=A_			,				algorithm_type="dpmsolver++"			,				solver_order=A_			,				solver_type=A_			,				)





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					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=A_			,				solver_type=A_			,				prediction_type=A_			,				algorithm_type=A_			,				)
           UpperCamelCase      :   Optional[Any]			      =  self.full_loop(
               solver_order=A_			,				solver_type=A_			,				prediction_type=A_			,				algorithm_type=A_			,				)
           assert not torch.isnan(A_  ).any(), "Samples have nan numbers"





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.check_over_configs(lower_order_final=A_  )
       self.check_over_configs(lower_order_final=A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
        self.check_over_forward(num_inference_steps=A_			,				time_step=0  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  self.full_loop()
       UpperCamelCase      :   Optional[int]			      =  torch.mean(torch.abs(A_  )  )

       assert abs(result_mean.item() - 0.27_91  ) < 1e-3





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self.full_loop(use_karras_sigmas=A_  )
       UpperCamelCase      :   Optional[Any]			      =  torch.mean(torch.abs(A_  )  )

       assert abs(result_mean.item() - 0.22_48  ) < 1e-3





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.full_loop(prediction_type="v_prediction"  )
       UpperCamelCase      :   Any			      =  torch.mean(torch.abs(A_  )  )

       assert abs(result_mean.item() - 0.14_53  ) < 1e-3





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  self.full_loop(prediction_type="v_prediction"			,				use_karras_sigmas=A_  )
       UpperCamelCase      :   str			      =  torch.mean(torch.abs(A_  )  )

       assert abs(result_mean.item() - 0.06_49  ) < 1e-3





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  self.scheduler_classes[0]
       UpperCamelCase      :   Any			      =  self.get_scheduler_config(thresholding=A_			,				dynamic_thresholding_ratio=0  )
       UpperCamelCase      :   int			      =  scheduler_class(**A_  )

       UpperCamelCase      :   Tuple			      =  10
       UpperCamelCase      :   List[Any]			      =  self.dummy_model()
       UpperCamelCase      :   Dict			      =  self.dummy_sample_deter.half()
       scheduler.set_timesteps(A_  )

       for i, t in enumerate(scheduler.timesteps  ):
        UpperCamelCase      :   Any			      =  model(A_			,				A_  )
        UpperCamelCase      :   Optional[Any]			      =  scheduler.step(A_			,				A_			,				A_  ).prev_sample

       assert sample.dtype == torch.floataa







__lowerCamelCase			:	Any					    =	9.8_0_6_6_5



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase = g	)		->					float:

 if fluid_density <= 0:
  raise ValueError("Impossible fluid density"	)
 if volume < 0:
  raise ValueError("Impossible Object volume"	)
 if gravity <= 0:
  raise ValueError("Impossible Gravity"	)

 return fluid_density * gravity * volume


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()
