





"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






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




       __magic_name__     :str		  =					(DDPMScheduler,)






       def      snake_case						(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							{
             'num_train_timesteps': 1_0_0_0,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**__UpperCAmelCase      )
         return config






       def      snake_case						(    self      ):



         '''simple docstring'''

         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    , [0.0_02, 0.02, 0.2, 2]      ):
           self.check_over_configs(beta_start=__UpperCAmelCase    , beta_end=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self.check_over_configs(thresholding=__UpperCAmelCase      )
         for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(
                 thresholding=__UpperCAmelCase    , prediction_type=__UpperCAmelCase    , sample_max_value=__UpperCAmelCase    , )






       def      snake_case						(    self      ):



         '''simple docstring'''

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         for t in [0, 5_0_0, 9_9_9]:
           self.check_over_forward(time_step=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ) - 0.0_09_79      )      ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ) - 0.02      )      ) < 1E-5






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:str											=							len(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							self.dummy_model()
         lowerCAmelCase__			:Any											=							self.dummy_sample_deter
         lowerCAmelCase__			:int											=							torch.manual_seed(0      )

         for t in reversed(range(__UpperCAmelCase      )      ):
           # 1. predict noise residual
           lowerCAmelCase__			:Union[str, Any]											=							model(__UpperCAmelCase    , __UpperCAmelCase      )

           # 2. predict previous mean of sample x_t-1
           lowerCAmelCase__			:Optional[Any]											=							scheduler.step(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , generator=__UpperCAmelCase      ).prev_sample

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           lowerCAmelCase__			:Optional[int]											=							pred_prev_sample

         lowerCAmelCase__			:Any											=							torch.sum(torch.abs(__UpperCAmelCase      )      )
         lowerCAmelCase__			:Optional[int]											=							torch.mean(torch.abs(__UpperCAmelCase      )      )

         assert abs(result_sum.item() - 2_58.96_06      ) < 1E-2
         assert abs(result_mean.item() - 0.33_72      ) < 1E-3






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							self.scheduler_classes[0]
         lowerCAmelCase__			:Tuple											=							self.get_scheduler_config(prediction_type='v_prediction'      )
         lowerCAmelCase__			:Tuple											=							scheduler_class(**__UpperCAmelCase      )

         lowerCAmelCase__			:Dict											=							len(__UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							self.dummy_model()
         lowerCAmelCase__			:str											=							self.dummy_sample_deter
         lowerCAmelCase__			:Dict											=							torch.manual_seed(0      )

         for t in reversed(range(__UpperCAmelCase      )      ):
           # 1. predict noise residual
           lowerCAmelCase__			:List[Any]											=							model(__UpperCAmelCase    , __UpperCAmelCase      )

           # 2. predict previous mean of sample x_t-1
           lowerCAmelCase__			:List[Any]											=							scheduler.step(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , generator=__UpperCAmelCase      ).prev_sample

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           lowerCAmelCase__			:List[str]											=							pred_prev_sample

         lowerCAmelCase__			:Any											=							torch.sum(torch.abs(__UpperCAmelCase      )      )
         lowerCAmelCase__			:Tuple											=							torch.mean(torch.abs(__UpperCAmelCase      )      )

         assert abs(result_sum.item() - 2_02.02_96      ) < 1E-2
         assert abs(result_mean.item() - 0.26_31      ) < 1E-3






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:Optional[Any]											=							[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							scheduler.timesteps

         for i, timestep in enumerate(__UpperCAmelCase      ):
           if i == len(__UpperCAmelCase      ) - 1:
             lowerCAmelCase__			:str											=							-1
           else:
             lowerCAmelCase__			:Any											=							timesteps[i + 1]

           lowerCAmelCase__			:Optional[int]											=							scheduler.previous_timestep(__UpperCAmelCase      )
           lowerCAmelCase__			:Tuple											=							prev_t.item()

           self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							self.scheduler_classes[0]
         lowerCAmelCase__			:Optional[int]											=							self.get_scheduler_config()
         lowerCAmelCase__			:Dict											=							scheduler_class(**__UpperCAmelCase      )

         lowerCAmelCase__			:Dict											=							[1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(__UpperCAmelCase    , msg='`custom_timesteps` must be in descending order.'      ):
           scheduler.set_timesteps(timesteps=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:List[Any]											=							[1_0_0, 8_7, 5_0, 1, 0]
         lowerCAmelCase__			:Optional[int]											=							len(__UpperCAmelCase      )

         with self.assertRaises(__UpperCAmelCase    , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'      ):
           scheduler.set_timesteps(num_inference_steps=__UpperCAmelCase    , timesteps=__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:List[str]											=							[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             __UpperCAmelCase    , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    , ):
           scheduler.set_timesteps(timesteps=__UpperCAmelCase      )







"""simple docstring"""





def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->bool:





  """simple docstring"""


  return numa ^ numa < 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()
