




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 					__lowerCamelCase			(       SCREAMING_SNAKE_CASE_       ):




    """simple docstring"""

    snake_case__   = (DEISMultistepScheduler,)
    snake_case__   = (("num_inference_steps", 2_5),)


    def   a (			self	:	Any  ,      **SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			str:
         lowerCAmelCase__            =					{
             """num_train_timesteps""": 1_000,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """solver_order""": 2,
         }

         config.update(**__snake_case  )
         return config


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=0  ,      **SCREAMING_SNAKE_CASE__	:	Any  ) ->			List[Any]:
         lowerCAmelCase__            =					dict(self.forward_default_kwargs  )
         lowerCAmelCase__            =					kwargs.pop("num_inference_steps"  ,      __snake_case  )
         lowerCAmelCase__            =					self.dummy_sample
         lowerCAmelCase__            =					0.1 * sample
         lowerCAmelCase__            =					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

              lowerCAmelCase__            =					sample, sample
              for t in range(__snake_case  ,      time_step + scheduler.config.solver_order + 1  ):
                   lowerCAmelCase__            =					scheduler.step(__snake_case  ,      __snake_case  ,      __snake_case  ,      **__snake_case  ).prev_sample
                   lowerCAmelCase__            =					new_scheduler.step(__snake_case  ,      __snake_case  ,      __snake_case  ,      **__snake_case  ).prev_sample

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


    def   a (			self	:	Dict  ) ->			Any:
         pass


    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=0  ,      **SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			List[Any]:
         lowerCAmelCase__            =					dict(self.forward_default_kwargs  )
         lowerCAmelCase__            =					kwargs.pop("num_inference_steps"  ,      __snake_case  )
         lowerCAmelCase__            =					self.dummy_sample
         lowerCAmelCase__            =					0.1 * sample
         lowerCAmelCase__            =					[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__            =					self.get_scheduler_config()
              lowerCAmelCase__            =					scheduler_class(**__snake_case  )
              scheduler.set_timesteps(__snake_case  )

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__snake_case  )
                   lowerCAmelCase__            =					scheduler_class.from_pretrained(__snake_case  )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(__snake_case  )

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

              lowerCAmelCase__            =					scheduler.step(__snake_case  ,      __snake_case  ,      __snake_case  ,      **__snake_case  ).prev_sample
              lowerCAmelCase__            =					new_scheduler.step(__snake_case  ,      __snake_case  ,      __snake_case  ,      **__snake_case  ).prev_sample

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


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	str=None  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ) ->			Union[str, Any]:
         if scheduler is None:
              lowerCAmelCase__            =					self.scheduler_classes[0]
              lowerCAmelCase__            =					self.get_scheduler_config(**__snake_case  )
              lowerCAmelCase__            =					scheduler_class(**__snake_case  )

         lowerCAmelCase__            =					self.scheduler_classes[0]
         lowerCAmelCase__            =					self.get_scheduler_config(**__snake_case  )
         lowerCAmelCase__            =					scheduler_class(**__snake_case  )

         lowerCAmelCase__            =					10
         lowerCAmelCase__            =					self.dummy_model()
         lowerCAmelCase__            =					self.dummy_sample_deter
         scheduler.set_timesteps(__snake_case  )

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

         return sample


    def   a (			self	:	str  ) ->			Dict:
         lowerCAmelCase__            =					dict(self.forward_default_kwargs  )

         lowerCAmelCase__            =					kwargs.pop("num_inference_steps"  ,      __snake_case  )

         for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__            =					self.get_scheduler_config()
              lowerCAmelCase__            =					scheduler_class(**__snake_case  )

              lowerCAmelCase__            =					self.dummy_sample
              lowerCAmelCase__            =					0.1 * sample

              if num_inference_steps is not None and hasattr(__snake_case  ,      "set_timesteps"  ):
                   scheduler.set_timesteps(__snake_case  )
              elif num_inference_steps is not None and not hasattr(__snake_case  ,      "set_timesteps"  ):
                   lowerCAmelCase__            =					num_inference_steps

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

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

              lowerCAmelCase__            =					scheduler.step(__snake_case  ,      __snake_case  ,      __snake_case  ,      **__snake_case  ).prev_sample
              lowerCAmelCase__            =					scheduler.step(__snake_case  ,      __snake_case  ,      __snake_case  ,      **__snake_case  ).prev_sample

              self.assertEqual(output_a.shape  ,      sample.shape  )
              self.assertEqual(output_a.shape  ,      output_a.shape  )


    def   a (			self	:	List[Any]  ) ->			Dict:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         lowerCAmelCase__            =					DEISMultistepScheduler(**self.get_scheduler_config()  )
         lowerCAmelCase__            =					self.full_loop(scheduler=__snake_case  )
         lowerCAmelCase__            =					torch.mean(torch.abs(__snake_case  )  )

         assert abs(result_mean.item() - 0.23_916  ) < 1e-3

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

         lowerCAmelCase__            =					self.full_loop(scheduler=__snake_case  )
         lowerCAmelCase__            =					torch.mean(torch.abs(__snake_case  )  )

         assert abs(result_mean.item() - 0.23_916  ) < 1e-3


    def   a (			self	:	int  ) ->			str:
         for timesteps in [25, 50, 100, 999, 1_000]:
              self.check_over_configs(num_train_timesteps=__snake_case  )


    def   a (			self	:	Union[str, Any]  ) ->			List[Any]:
         self.check_over_configs(thresholding=__snake_case  )
         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=__snake_case  ,      prediction_type=__snake_case  ,      sample_max_value=__snake_case  ,      algorithm_type="deis"  ,      solver_order=__snake_case  ,      solver_type=__snake_case  ,      )


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


    def   a (			self	:	Optional[Any]  ) ->			List[Any]:
         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=__snake_case  ,      solver_type=__snake_case  ,      prediction_type=__snake_case  ,      algorithm_type=__snake_case  ,      )
                             lowerCAmelCase__            =					self.full_loop(
                                 solver_order=__snake_case  ,      solver_type=__snake_case  ,      prediction_type=__snake_case  ,      algorithm_type=__snake_case  ,      )
                             assert not torch.isnan(__snake_case  ).any(), "Samples have nan numbers"


    def   a (			self	:	Dict  ) ->			str:
         self.check_over_configs(lower_order_final=__snake_case  )
         self.check_over_configs(lower_order_final=__snake_case  )


    def   a (			self	:	int  ) ->			Optional[Any]:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
              self.check_over_forward(num_inference_steps=__snake_case  ,      time_step=0  )


    def   a (			self	:	Union[str, Any]  ) ->			List[str]:
         lowerCAmelCase__            =					self.full_loop()
         lowerCAmelCase__            =					torch.mean(torch.abs(__snake_case  )  )

         assert abs(result_mean.item() - 0.23_916  ) < 1e-3


    def   a (			self	:	Optional[int]  ) ->			Optional[Any]:
         lowerCAmelCase__            =					self.full_loop(prediction_type="v_prediction"  )
         lowerCAmelCase__            =					torch.mean(torch.abs(__snake_case  )  )

         assert abs(result_mean.item() - 0.091  ) < 1e-3


    def   a (			self	:	Union[str, Any]  ) ->			List[str]:
         lowerCAmelCase__            =					self.scheduler_classes[0]
         lowerCAmelCase__            =					self.get_scheduler_config(thresholding=__snake_case  ,      dynamic_thresholding_ratio=0  )
         lowerCAmelCase__            =					scheduler_class(**__snake_case  )

         lowerCAmelCase__            =					10
         lowerCAmelCase__            =					self.dummy_model()
         lowerCAmelCase__            =					self.dummy_sample_deter.half()
         scheduler.set_timesteps(__snake_case  )

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

         assert sample.dtype == torch.floataa








"""simple docstring"""





def   a    (     __UpperCAmelCase      :    list[int]  )    ->							float:
						if not nums:  # Makes sure that the list is not empty
												raise ValueError("""List is empty"""  )

						__magic_name__:	Dict							=       sum(__UpperCAmelCase  ) / len(__UpperCAmelCase  )  # Calculate the average
						return sum(abs(x - average  ) for x in nums  ) / len(__UpperCAmelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



