'''simple docstring'''




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 		__A       (__magic_name__   ):
       snake_case       :Dict				    =					(PNDMScheduler,)
       snake_case       :List[Any]				    =					(("num_inference_steps", 50),)






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		{
                  "num_train_timesteps": 10_00,
                  "beta_start": 0.0_0_0_1,
                  "beta_end": 0.0_2,
                  "beta_schedule": "linear",
              }

              config.update(**UpperCamelCase_      )
              return config






       def 			_snake_case						(      self				,     UpperCamelCase_=0				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		dict(self.forward_default_kwargs      )
              __UpperCAmelCase  :				Dict       				=		kwargs.pop("num_inference_steps"				,     UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		self.dummy_sample
              __UpperCAmelCase  :				Optional[Any]       				=		0.1 * sample
              __UpperCAmelCase  :				Any       				=		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

              for scheduler_class in self.scheduler_classes:
                     __UpperCAmelCase  :				List[Any]       				=		self.get_scheduler_config(**UpperCamelCase_      )
                     __UpperCAmelCase  :				Tuple       				=		scheduler_class(**UpperCamelCase_      )
                     scheduler.set_timesteps(UpperCamelCase_      )
                     # copy over dummy past residuals
                     __UpperCAmelCase  :				List[Any]       				=		dummy_past_residuals[:]

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

                     __UpperCAmelCase  :				Any       				=		scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				Dict       				=		new_scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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

                     __UpperCAmelCase  :				List[str]       				=		scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				int       				=		new_scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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






       def 			_snake_case						(      self      ):
              pass






       def 			_snake_case						(      self				,     UpperCamelCase_=0				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		dict(self.forward_default_kwargs      )
              __UpperCAmelCase  :				Optional[Any]       				=		kwargs.pop("num_inference_steps"				,     UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		self.dummy_sample
              __UpperCAmelCase  :				int       				=		0.1 * sample
              __UpperCAmelCase  :				Dict       				=		[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

              for scheduler_class in self.scheduler_classes:
                     __UpperCAmelCase  :				Optional[int]       				=		self.get_scheduler_config()
                     __UpperCAmelCase  :				Optional[int]       				=		scheduler_class(**UpperCamelCase_      )
                     scheduler.set_timesteps(UpperCamelCase_      )

                     # copy over dummy past residuals (must be after setting timesteps)
                     __UpperCAmelCase  :				Union[str, Any]       				=		dummy_past_residuals[:]

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(UpperCamelCase_      )
                            __UpperCAmelCase  :				List[Any]       				=		scheduler_class.from_pretrained(UpperCamelCase_      )
                            # copy over dummy past residuals
                            new_scheduler.set_timesteps(UpperCamelCase_      )

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

                     __UpperCAmelCase  :				str       				=		scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				str       				=		new_scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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

                     __UpperCAmelCase  :				Any       				=		scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				List[str]       				=		new_scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				Optional[int]       				=		self.get_scheduler_config(**UpperCamelCase_      )
              __UpperCAmelCase  :				Dict       				=		scheduler_class(**UpperCamelCase_      )

              __UpperCAmelCase  :				Optional[Any]       				=		10
              __UpperCAmelCase  :				Optional[Any]       				=		self.dummy_model()
              __UpperCAmelCase  :				Dict       				=		self.dummy_sample_deter
              scheduler.set_timesteps(UpperCamelCase_      )

              for i, t in enumerate(scheduler.prk_timesteps      ):
                     __UpperCAmelCase  :				str       				=		model(UpperCamelCase_				,     UpperCamelCase_      )
                     __UpperCAmelCase  :				Union[str, Any]       				=		scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample

              for i, t in enumerate(scheduler.plms_timesteps      ):
                     __UpperCAmelCase  :				Any       				=		model(UpperCamelCase_				,     UpperCamelCase_      )
                     __UpperCAmelCase  :				List[Any]       				=		scheduler.step_plms(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample

              return sample






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		dict(self.forward_default_kwargs      )

              __UpperCAmelCase  :				Optional[int]       				=		kwargs.pop("num_inference_steps"				,     UpperCamelCase_      )

              for scheduler_class in self.scheduler_classes:
                     __UpperCAmelCase  :				str       				=		self.get_scheduler_config()
                     __UpperCAmelCase  :				Tuple       				=		scheduler_class(**UpperCamelCase_      )

                     __UpperCAmelCase  :				Any       				=		self.dummy_sample
                     __UpperCAmelCase  :				List[str]       				=		0.1 * sample

                     if num_inference_steps is not None and hasattr(UpperCamelCase_				,     "set_timesteps"      ):
                            scheduler.set_timesteps(UpperCamelCase_      )
                     elif num_inference_steps is not None and not hasattr(UpperCamelCase_				,     "set_timesteps"      ):
                            __UpperCAmelCase  :				List[str]       				=		num_inference_steps

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

                     __UpperCAmelCase  :				Dict       				=		scheduler.step_prk(UpperCamelCase_				,     0				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				int       				=		scheduler.step_prk(UpperCamelCase_				,     1				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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

                     __UpperCAmelCase  :				Optional[int]       				=		scheduler.step_plms(UpperCamelCase_				,     0				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample
                     __UpperCAmelCase  :				Union[str, Any]       				=		scheduler.step_plms(UpperCamelCase_				,     1				,     UpperCamelCase_				,     **UpperCamelCase_      ).prev_sample

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






       def 			_snake_case						(      self      ):
              for timesteps in [1_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=UpperCamelCase_      )

              __UpperCAmelCase  :				str       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				str       				=		self.get_scheduler_config(steps_offset=1      )
              __UpperCAmelCase  :				List[str]       				=		scheduler_class(**UpperCamelCase_      )
              scheduler.set_timesteps(10      )
              assert torch.equal(
                  scheduler.timesteps				,     torch.LongTensor(
                      [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]      )				,     )






       def 			_snake_case						(      self      ):
              for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]				,     [0.0_0_2, 0.0_2]      ):
                     self.check_over_configs(beta_start=UpperCamelCase_				,     beta_end=UpperCamelCase_      )






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






       def 			_snake_case						(      self      ):
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for t in [1, 5, 10]:
                     self.check_over_forward(time_step=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for t, num_inference_steps in zip([1, 5, 10]				,     [10, 50, 1_00]      ):
                     self.check_over_forward(num_inference_steps=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
              __UpperCAmelCase  :				Dict       				=		27

              for scheduler_class in self.scheduler_classes:
                     __UpperCAmelCase  :				Optional[Any]       				=		self.dummy_sample
                     __UpperCAmelCase  :				int       				=		0.1 * sample

                     __UpperCAmelCase  :				Dict       				=		self.get_scheduler_config()
                     __UpperCAmelCase  :				str       				=		scheduler_class(**UpperCamelCase_      )

                     scheduler.set_timesteps(UpperCamelCase_      )

                     # before power of 3 fix, would error on first step, so we only need to do two
                     for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
                            __UpperCAmelCase  :				int       				=		scheduler.step_prk(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample






       def 			_snake_case						(      self      ):
              with self.assertRaises(UpperCamelCase_      ):
                     __UpperCAmelCase  :				Tuple       				=		self.scheduler_classes[0]
                     __UpperCAmelCase  :				List[str]       				=		self.get_scheduler_config()
                     __UpperCAmelCase  :				Optional[Any]       				=		scheduler_class(**UpperCamelCase_      )

                     scheduler.step_plms(self.dummy_sample				,     1				,     self.dummy_sample      ).prev_sample






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		self.full_loop()
              __UpperCAmelCase  :				Tuple       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				int       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 1_9_8.1_3_1_8      ) < 1E-2
              assert abs(result_mean.item() - 0.2_5_8_0      ) < 1E-3






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		self.full_loop(prediction_type="v_prediction"      )
              __UpperCAmelCase  :				Any       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				List[Any]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 6_7.3_9_8_6      ) < 1E-2
              assert abs(result_mean.item() - 0.0_8_7_8      ) < 1E-3






       def 			_snake_case						(      self      ):
              # We specify different beta, so that the first alpha is 0.99
              __UpperCAmelCase  :				str       				=		self.full_loop(set_alpha_to_one=UpperCamelCase_				,     beta_start=0.0_1      )
              __UpperCAmelCase  :				Tuple       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				List[str]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 2_3_0.0_3_9_9      ) < 1E-2
              assert abs(result_mean.item() - 0.2_9_9_5      ) < 1E-3







       def 			_snake_case						(      self      ):
              # We specify different beta, so that the first alpha is 0.99
              __UpperCAmelCase  :				Dict       				=		self.full_loop(set_alpha_to_one=UpperCamelCase_				,     beta_start=0.0_1      )
              __UpperCAmelCase  :				Optional[int]       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				Tuple       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 1_8_6.9_4_8_2      ) < 1E-2
              assert abs(result_mean.item() - 0.2_4_3_4      ) < 1E-3







'''simple docstring'''




def _lowercase	(      lowerCamelCase__ = 100						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		(n * (n + 1) // 2) ** 2
       __UpperCAmelCase  :				Any       				=		n * (n + 1) * (2 * n + 1) // 6
       return sum_cubes - sum_squares


if __name__ == "__main__":
   print(f"""{solution() = }""")






