





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Tuple      =  (KDPMaDiscreteScheduler,)
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  1_0







       def    __UpperCAmelCase (    self	:	Optional[int]						,**__A	:	int  )						->					List[Any]:
         _lowercase							= {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**__A  )
         return config







       def    __UpperCAmelCase (    self	:	Dict  )						->					Dict:
         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=__A  )







       def    __UpperCAmelCase (    self	:	Dict  )						->					List[Any]:
         for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]						,[0.0002, 0.002, 0.02]  ):
           self.check_over_configs(beta_start=__A						,beta_end=__A  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					List[Any]:
         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=__A  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__A  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Dict:
         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(prediction_type='v_prediction'  )
         _lowercase							= scheduler_class(**__A  )

         scheduler.set_timesteps(self.num_inference_steps  )

         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase							= sample.to(__A  )

         for i, t in enumerate(scheduler.timesteps  ):
           _lowercase							= scheduler.scale_model_input(__A						,__A  )

           _lowercase							= model(__A						,__A  )

           _lowercase							= scheduler.step(__A						,__A						,__A  )
           _lowercase							= output.prev_sample

         _lowercase							= torch.sum(torch.abs(__A  )  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34e-07  ) < 1e-2
           assert abs(result_mean.item() - 6.11_12e-10  ) < 1e-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07  ) < 1e-2
           assert abs(result_mean.item() - 0.0002  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Optional[Any]:
         if torch_device == "mps":
           return
         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config()
         _lowercase							= scheduler_class(**__A  )

         scheduler.set_timesteps(self.num_inference_steps  )

         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase							= sample.to(__A  )

         for i, t in enumerate(scheduler.timesteps  ):
           _lowercase							= scheduler.scale_model_input(__A						,__A  )

           _lowercase							= model(__A						,__A  )

           _lowercase							= scheduler.step(__A						,__A						,__A  )
           _lowercase							= output.prev_sample

         _lowercase							= torch.sum(torch.abs(__A  )  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.4125  ) < 1e-2
           assert abs(result_mean.item() - 0.0266  ) < 1e-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.4125  ) < 1e-2
           assert abs(result_mean.item() - 0.0266  ) < 1e-3







       def    __UpperCAmelCase (    self	:	str  )						->					List[Any]:
         if torch_device == "mps":
           return
         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config()
         _lowercase							= scheduler_class(**__A  )

         scheduler.set_timesteps(self.num_inference_steps						,device=__A  )

         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter.to(__A  ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase							= scheduler.scale_model_input(__A						,__A  )

           _lowercase							= model(__A						,__A  )

           _lowercase							= scheduler.step(__A						,__A						,__A  )
           _lowercase							= output.prev_sample

         _lowercase							= torch.sum(torch.abs(__A  )  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         if str(__A  ).startswith('cpu'  ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.4125  ) < 1e-2
           assert abs(result_mean.item() - 0.0266  ) < 1e-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.4125  ) < 1e-2
           assert abs(result_mean.item() - 0.0266  ) < 1e-3






from itertools import count

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int = 50     )      ->						int:
  _lowercase							= [1] * min_block_length

  for n in count(snake_case__     ):
    fill_count_functions.append(1     )

    for block_length in range(snake_case__			,							n + 1     ):
      for block_start in range(n - block_length     ):
        fill_count_functions[n] += fill_count_functions[
            n - block_start - block_length - 1
        ]

      fill_count_functions[n] += 1

    if fill_count_functions[n] > 100_0000:
      break

  return n


if __name__ == "__main__":
      print(F"""{solution() = }""")