

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							(IPNDMScheduler,)
    lowerCamelCase			=							(("num_inference_steps", 50),)


    def 						snake_case__     (      self :       Any,**lowercase_ :       List[str]		)->    str:





        '''simple docstring'''







        A__          =   {"num_train_timesteps": 1_0_0_0}
        config.update(**snake_case__		)
        return config


    def 						snake_case__     (      self :       str,lowercase_ :       Tuple=0,**lowercase_ :       Dict		)->    int:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',snake_case__		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config(**snake_case__		)
            A__          =   scheduler_class(**snake_case__		)
            scheduler.set_timesteps(snake_case__		)
            # copy over dummy past residuals
            A__          =   dummy_past_residuals[:]

            if time_step is None:
                A__          =   scheduler.timesteps[len(scheduler.timesteps		) // 2]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(snake_case__		)
                A__          =   scheduler_class.from_pretrained(snake_case__		)
                new_scheduler.set_timesteps(snake_case__		)
                # copy over dummy past residuals
                A__          =   dummy_past_residuals[:]

            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample
            A__          =   new_scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample
            A__          =   new_scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


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





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       List[str],lowercase_ :       Any=0,**lowercase_ :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',snake_case__		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config()
            A__          =   scheduler_class(**snake_case__		)
            scheduler.set_timesteps(snake_case__		)

            # copy over dummy past residuals (must be after setting timesteps)
            A__          =   dummy_past_residuals[:]

            if time_step is None:
                A__          =   scheduler.timesteps[len(scheduler.timesteps		) // 2]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(snake_case__		)
                A__          =   scheduler_class.from_pretrained(snake_case__		)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(snake_case__		)

                # copy over dummy past residual (must be after setting timesteps)
                A__          =   dummy_past_residuals[:]

            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample
            A__          =   new_scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample
            A__          =   new_scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__     (      self :       Optional[int],**lowercase_ :       Tuple		)->    int:





        '''simple docstring'''







        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(**snake_case__		)
        A__          =   scheduler_class(**snake_case__		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(snake_case__		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(snake_case__,snake_case__		)
            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__		).prev_sample

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(snake_case__,snake_case__		)
            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__		).prev_sample

        return sample


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





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)

        A__          =   kwargs.pop('num_inference_steps',snake_case__		)

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config()
            A__          =   scheduler_class(**snake_case__		)

            A__          =   self.dummy_sample
            A__          =   0.1 * sample

            if num_inference_steps is not None and hasattr(snake_case__,'set_timesteps'		):
                scheduler.set_timesteps(snake_case__		)
            elif num_inference_steps is not None and not hasattr(snake_case__,'set_timesteps'		):
                A__          =   num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            A__          =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            A__          =   dummy_past_residuals[:]

            A__          =   scheduler.timesteps[5]
            A__          =   scheduler.timesteps[6]

            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample
            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample

            self.assertEqual(output_a.shape,sample.shape		)
            self.assertEqual(output_a.shape,output_a.shape		)

            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample
            A__          =   scheduler.step(snake_case__,snake_case__,snake_case__,**snake_case__		).prev_sample

            self.assertEqual(output_a.shape,sample.shape		)
            self.assertEqual(output_a.shape,output_a.shape		)


    def 						snake_case__     (      self :       Dict		)->    Tuple:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Dict		)->    Optional[Any]:





        '''simple docstring'''







        for t, num_inference_steps in zip([1, 5, 1_0],[1_0, 5_0, 1_0_0]		):
            self.check_over_forward(num_inference_steps=snake_case__,time_step=snake_case__		)


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





        '''simple docstring'''







        A__          =   self.full_loop()
        A__          =   torch.mean(torch.abs(snake_case__		)		)

        assert abs(result_mean.item() - 2_5_4_0_5_2_9		) < 1_0





"""simple docstring"""







def 					a__			(				SCREAMING_SNAKE_CASE    :    float				,      SCREAMING_SNAKE_CASE    :    float				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  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(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				):
    raise Exception("Years to repay must be an integer > 0"				)

  # Yearly rate is divided by 12 to get monthly rate
  lowerCAmelCase     :     Tuple								=							rate_per_annum / 1_2

  # Years to repay is multiplied by 12 to get number of payments as payment is monthly
  lowerCAmelCase     :     List[Any]								=							years_to_repay * 1_2

  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()





