





"""simple docstring"""
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  (DDPMScheduler,)




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {
                 '''num_train_timesteps''': 10_00,
                 '''beta_start''': 0.0_0_0_1,
                 '''beta_end''': 0.0_2,
                 '''beta_schedule''': '''linear''',
                 '''variance_type''': '''fixed_small''',
                 '''clip_sample''': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__							)
             return config




       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             for timesteps in [1, 5, 1_00, 10_00]:
                   self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]		,[0.0_0_2, 0.0_2, 0.2, 2]							):
                   self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__		,beta_end=SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             for t in [0, 5_00, 9_99]:
                   self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






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

             assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_87							) - 0.0_0_9_7_9							)							) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_99							) - 0.0_2							)							) < 1E-5




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE       :str		    =  len(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :str		    =  self.dummy_model()
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.dummy_sample_deter
             __SCREAMING_SNAKE_CASE       :Any		    =  torch.manual_seed(0							)

             for t in reversed(range(SCREAMING_SNAKE_CASE__							)							):
                   # 1. predict noise residual
                   __SCREAMING_SNAKE_CASE       :Dict		    =  model(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

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

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

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE       :int		    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

             assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
             assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.get_scheduler_config(prediction_type='''v_prediction'''							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  scheduler_class(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Dict		    =  len(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  self.dummy_model()
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.dummy_sample_deter
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.manual_seed(0							)

             for t in reversed(range(SCREAMING_SNAKE_CASE__							)							):
                   # 1. predict noise residual
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  model(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

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

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  pred_prev_sample

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE       :int		    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

             assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
             assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE       :Dict		    =  [1_00, 87, 50, 1, 0]

             scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :str		    =  scheduler.timesteps

             for i, timestep in enumerate(SCREAMING_SNAKE_CASE__							):
                   if i == len(SCREAMING_SNAKE_CASE__							) - 1:
                         __SCREAMING_SNAKE_CASE       :List[str]		    =  -1
                   else:
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  timesteps[i + 1]

                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  scheduler.previous_timestep(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  prev_t.item()

                   self.assertEqual(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_scheduler_config()
             __SCREAMING_SNAKE_CASE       :int		    =  scheduler_class(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :str		    =  [1_00, 87, 50, 51, 0]

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




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.get_scheduler_config()
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  scheduler_class(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  [1_00, 87, 50, 1, 0]
             __SCREAMING_SNAKE_CASE       :Tuple		    =  len(SCREAMING_SNAKE_CASE__							)

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




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE       :Any		    =  self.get_scheduler_config()
             __SCREAMING_SNAKE_CASE       :int		    =  scheduler_class(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :str		    =  [scheduler.config.num_train_timesteps]

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






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		str							)      ->				list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(a_							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
  __import__("doctest").testmod()