

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Dict  					=    (IPNDMScheduler,)
   _lowercase     :       Optional[int]  					=    (('num_inference_steps', 50),)

   def     UpperCamelCase					(				self	:    Dict			,						**lowerCamelCase__	:    Union[str, Any]       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		{'''num_train_timesteps''': 1_0_0_0}
         config.update(**lowerCamelCase__       )
         return config

   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    Optional[Any]=0			,						**lowerCamelCase__	:    Optional[int]       ) ->			int:
         """simple docstring"""





         A_							       =		dict(self.forward_default_kwargs       )
         A_							       =		kwargs.pop('''num_inference_steps'''			,						lowerCamelCase__       )
         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(**lowerCamelCase__       )
               A_							       =		scheduler_class(**lowerCamelCase__       )
               scheduler.set_timesteps(lowerCamelCase__       )
               # 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(lowerCamelCase__       )
                     A_							       =		scheduler_class.from_pretrained(lowerCamelCase__       )
                     new_scheduler.set_timesteps(lowerCamelCase__       )
                     # copy over dummy past residuals
                     A_							       =		dummy_past_residuals[:]

               A_							       =		scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample
               A_							       =		new_scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample

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

               A_							       =		scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample
               A_							       =		new_scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample

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

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Tuple:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    Tuple=0			,						**lowerCamelCase__	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         A_							       =		dict(self.forward_default_kwargs       )
         A_							       =		kwargs.pop('''num_inference_steps'''			,						lowerCamelCase__       )
         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(**lowerCamelCase__       )
               scheduler.set_timesteps(lowerCamelCase__       )

               # 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(lowerCamelCase__       )
                     A_							       =		scheduler_class.from_pretrained(lowerCamelCase__       )
                     # copy over dummy past residuals
                     new_scheduler.set_timesteps(lowerCamelCase__       )

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

               A_							       =		scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample
               A_							       =		new_scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample

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

               A_							       =		scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample
               A_							       =		new_scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample

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

   def     UpperCamelCase					(				self	:    Optional[Any]			,						**lowerCamelCase__	:    Dict       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.scheduler_classes[0]
         A_							       =		self.get_scheduler_config(**lowerCamelCase__       )
         A_							       =		scheduler_class(**lowerCamelCase__       )

         A_							       =		1_0
         A_							       =		self.dummy_model()
         A_							       =		self.dummy_sample_deter
         scheduler.set_timesteps(lowerCamelCase__       )

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

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

         return sample

   def     UpperCamelCase					(				self	:    str       ) ->			Any:
         """simple docstring"""





         A_							       =		dict(self.forward_default_kwargs       )

         A_							       =		kwargs.pop('''num_inference_steps'''			,						lowerCamelCase__       )

         for scheduler_class in self.scheduler_classes:
               A_							       =		self.get_scheduler_config()
               A_							       =		scheduler_class(**lowerCamelCase__       )

               A_							       =		self.dummy_sample
               A_							       =		0.1 * sample

               if num_inference_steps is not None and hasattr(lowerCamelCase__			,						'''set_timesteps'''       ):
                     scheduler.set_timesteps(lowerCamelCase__       )
               elif num_inference_steps is not None and not hasattr(lowerCamelCase__			,						'''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(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample
               A_							       =		scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample

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

               A_							       =		scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample
               A_							       =		scheduler.step(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						**lowerCamelCase__       ).prev_sample

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

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[str]       ) ->			int:
         """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=lowerCamelCase__			,						time_step=lowerCamelCase__       )





   def     UpperCamelCase					(				self	:    Tuple       ) ->			Tuple:
         """simple docstring"""





         A_							       =		self.full_loop()
         A_							       =		torch.mean(torch.abs(lowerCamelCase__       )       )

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








from math import factorial, radians







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 18		,				SCREAMING_SNAKE_CASE = 10   ):
      '''simple docstring'''







      A_							       =		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

      # Converting from degrees to radians
      A_							       =		radians(SCREAMING_SNAKE_CASE   )

      A_							       =		angle_in_radians
      A_							       =		3
      A_							       =		-1

      for _ in range(SCREAMING_SNAKE_CASE   ):
            result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE   )

            A_							       =		-b  # One positive term and the next will be negative and so on...
            a += 2  # Increased by 2 for every term.

      return round(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )


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





