import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Union[str, Any]													=			(IPNDMScheduler,)
      snake_case__    :     List[str]													=			(('num_inference_steps', 50),)

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			{"""num_train_timesteps""": 1000}
            config.update(**__lowerCAmelCase		)
            return config

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			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        A__ (		self		):







            """simple docstring"""



            pass

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)

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

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(__lowerCAmelCase		)
                        lowercase														=			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)
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			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        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.scheduler_classes[0]
            lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
            lowercase														=			scheduler_class(**__lowerCAmelCase		)

            lowercase														=			10
            lowercase														=			self.dummy_model()
            lowercase														=			self.dummy_sample_deter
            scheduler.set_timesteps(__lowerCAmelCase		)

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

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

            return sample

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)

            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)

                  lowercase														=			self.dummy_sample
                  lowercase														=			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"""		):
                        lowercase														=			num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                  lowercase														=			dummy_past_residuals[:]

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self		):







            """simple docstring"""



            for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            for t, num_inference_steps in zip([1, 5, 10]	, [10, 50, 100]		):
                  self.check_over_forward(num_inference_steps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.full_loop()
            lowercase														=			torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_mean.item() - 254_0529		) < 10




"""simple docstring"""






from __future__ import annotations


def     UpperCAmelCase__ (       lowerCAmelCase__  :list[float]      ,	lowerCAmelCase__  :list[float]	)				->					float:
      '''simple docstring'''


      lowercase														=			sorted(numsa + numsa	)
      lowercase							,							lowercase														=			divmod(len(lowerCAmelCase__	)      ,	2	)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of first array: """).split()]
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of second array: """).split()]
       print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



