





"""simple docstring"""





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       __magic_name__     :int		  =					(UniPCMultistepScheduler,)
       __magic_name__     :Union[str, Any]		  =					(("""num_inference_steps""", 25),)






       def      snake_case						(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							{
             'num_train_timesteps': 1_0_0_0,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
             'solver_type': 'bh2',
         }

         config.update(**__UpperCAmelCase      )
         return config






       def      snake_case						(    self    , __UpperCAmelCase=0    , **__UpperCAmelCase      ):



         '''simple docstring'''

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

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

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

           lowerCAmelCase__							,				lowerCAmelCase__			:int											=							sample, sample
           for t in range(__UpperCAmelCase    , time_step + scheduler.config.solver_order + 1      ):
             lowerCAmelCase__			:List[str]											=							scheduler.step(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , **__UpperCAmelCase      ).prev_sample
             lowerCAmelCase__			: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    , __UpperCAmelCase=0    , **__UpperCAmelCase      ):



         '''simple docstring'''

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

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

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

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

           lowerCAmelCase__			:int											=							scheduler.step(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , **__UpperCAmelCase      ).prev_sample
           lowerCAmelCase__			: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    , __UpperCAmelCase=None    , **__UpperCAmelCase      ):



         '''simple docstring'''

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

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

         lowerCAmelCase__			:Union[str, Any]											=							1_0
         lowerCAmelCase__			:Tuple											=							self.dummy_model()
         lowerCAmelCase__			:int											=							self.dummy_sample_deter
         scheduler.set_timesteps(__UpperCAmelCase      )

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

         return sample






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:Union[str, Any]											=							kwargs.pop('num_inference_steps'    , __UpperCAmelCase      )

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

           lowerCAmelCase__			:Optional[Any]											=							self.dummy_sample
           lowerCAmelCase__			: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'      ):
             lowerCAmelCase__			:List[str]											=							num_inference_steps

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

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

           lowerCAmelCase__			:str											=							scheduler.step(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , **__UpperCAmelCase      ).prev_sample
           lowerCAmelCase__			: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      ):



         '''simple docstring'''

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

         assert abs(result_mean.item() - 0.24_64      ) < 1E-3

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

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

         assert abs(result_mean.item() - 0.24_64      ) < 1E-3






       def      snake_case						(    self      ):



         '''simple docstring'''

         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''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      ):



         '''simple docstring'''

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






       def      snake_case						(    self      ):



         '''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    , )
               lowerCAmelCase__			: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      ):



         '''simple docstring'''

         self.check_over_configs(lower_order_final=__UpperCAmelCase      )
         self.check_over_configs(lower_order_final=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
           self.check_over_forward(num_inference_steps=__UpperCAmelCase    , time_step=0      )






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         assert abs(result_mean.item() - 0.24_64      ) < 1E-3






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         assert abs(result_mean.item() - 0.10_14      ) < 1E-3






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:int											=							1_0
         lowerCAmelCase__			:Any											=							self.dummy_model()
         lowerCAmelCase__			:Dict											=							self.dummy_sample_deter.half()
         scheduler.set_timesteps(__UpperCAmelCase      )

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

         assert sample.dtype == torch.floataa






       def      snake_case						(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

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

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







"""simple docstring"""





def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->bool:





  """simple docstring"""


  return numa ^ numa < 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()
