



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Tuple       				=    (DDPMScheduler,)


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				**lowerCAmelCase  :			Optional[Any])	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowerCAmelCase)
         return config


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




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Optional[Any]:




         """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=lowerCAmelCase			,				beta_end=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[int]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Tuple:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[int]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			int)	->				Tuple:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Dict)	->				Optional[int]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         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_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1E-5


   def 			UpperCAmelCase		(						self  :			List[Any])	->				str:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__     =  len(lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  torch.manual_seed(0)

         for t in reversed(range(lowerCAmelCase)):
               # 1. predict noise residual
               lowercase__     =  model(lowerCAmelCase			,				lowerCAmelCase)

               # 2. predict previous mean of sample x_t-1
               lowercase__     =  scheduler.step(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				generator=lowerCAmelCase).prev_sample

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

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

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


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




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(prediction_type='v_prediction')
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__     =  len(lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  torch.manual_seed(0)

         for t in reversed(range(lowerCAmelCase)):
               # 1. predict noise residual
               lowercase__     =  model(lowerCAmelCase			,				lowerCAmelCase)

               # 2. predict previous mean of sample x_t-1
               lowercase__     =  scheduler.step(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				generator=lowerCAmelCase).prev_sample

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

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

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


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__     =  [1_00, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowerCAmelCase)

         lowercase__     =  scheduler.timesteps

         for i, timestep in enumerate(lowerCAmelCase):
               if i == len(lowerCAmelCase) - 1:
                     lowercase__     =  -1
               else:
                     lowercase__     =  timesteps[i + 1]

               lowercase__     =  scheduler.previous_timestep(lowerCAmelCase)
               lowercase__     =  prev_t.item()

               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			int)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__     =  [1_00, 87, 50, 51, 0]

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


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




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__     =  [1_00, 87, 50, 1, 0]
         lowercase__     =  len(lowerCAmelCase)

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



   def 			UpperCAmelCase		(						self  :			List[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__     =  [scheduler.config.num_train_timesteps]

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





def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      if principal <= 0:
            raise Exception('Principal borrowed must be > 0'	)
      if rate_per_annum < 0:
            raise Exception('Rate of interest must be >= 0'	)
      if years_to_repay <= 0 or not isinstance(A__						,  A__	):
            raise Exception('Years to repay must be an integer > 0'	)

      # Yearly rate is divided by 12 to get monthly rate
      lowercase__     =  rate_per_annum / 12

      # Years to repay is multiplied by 12 to get number of payments as payment is monthly
      lowercase__     =  years_to_repay * 12

      return (
          principal
          * rate_per_month
          * (1 + rate_per_month) ** number_of_payments
          / ((1 + rate_per_month) ** number_of_payments - 1)
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
