



'''simple docstring'''





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[Any]						   =					(PNDMScheduler,)
       lowerCAmelCase__  :List[Any]						   =					(("num_inference_steps", 50),)



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							int:
           lowercase__		       =						{
               "num_train_timesteps": 1_000,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**UpperCAmelCase_      )
           return config



       def 							_a      (				self     ,UpperCAmelCase_=0     ,**UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						dict(self.forward_default_kwargs      )
           lowercase__		       =						kwargs.pop("num_inference_steps"     ,UpperCAmelCase_      )
           lowercase__		       =						self.dummy_sample
           lowercase__		       =						0.1 * sample
           lowercase__		       =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

               lowercase__		       =						scheduler.step_prk(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      ).prev_sample
               lowercase__		       =						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"

               lowercase__		       =						scheduler.step_plms(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      ).prev_sample
               lowercase__		       =						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 							_a      (				self      )  ->							Dict:
           pass



       def 							_a      (				self     ,UpperCAmelCase_=0     ,**UpperCAmelCase_      )  ->							str:
           lowercase__		       =						dict(self.forward_default_kwargs      )
           lowercase__		       =						kwargs.pop("num_inference_steps"     ,UpperCAmelCase_      )
           lowercase__		       =						self.dummy_sample
           lowercase__		       =						0.1 * sample
           lowercase__		       =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
               lowercase__		       =						self.get_scheduler_config()
               lowercase__		       =						scheduler_class(**UpperCAmelCase_      )
               scheduler.set_timesteps(UpperCAmelCase_      )

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(UpperCAmelCase_      )
                   lowercase__		       =						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)
                   lowercase__		       =						dummy_past_residuals[:]

               lowercase__		       =						scheduler.step_prk(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      ).prev_sample
               lowercase__		       =						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"

               lowercase__		       =						scheduler.step_plms(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      ).prev_sample
               lowercase__		       =						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 							_a      (				self     ,**UpperCAmelCase_      )  ->							str:
           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config(**UpperCAmelCase_      )
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

           lowercase__		       =						10
           lowercase__		       =						self.dummy_model()
           lowercase__		       =						self.dummy_sample_deter
           scheduler.set_timesteps(UpperCAmelCase_      )

           for i, t in enumerate(scheduler.prk_timesteps      ):
               lowercase__		       =						model(UpperCAmelCase_     ,UpperCAmelCase_      )
               lowercase__		       =						scheduler.step_prk(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps      ):
               lowercase__		       =						model(UpperCAmelCase_     ,UpperCAmelCase_      )
               lowercase__		       =						scheduler.step_plms(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      ).prev_sample

           return sample



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						dict(self.forward_default_kwargs      )

           lowercase__		       =						kwargs.pop("num_inference_steps"     ,UpperCAmelCase_      )

           for scheduler_class in self.scheduler_classes:
               lowercase__		       =						self.get_scheduler_config()
               lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

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

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

               lowercase__		       =						scheduler.step_prk(UpperCAmelCase_     ,0     ,UpperCAmelCase_     ,**UpperCAmelCase_      ).prev_sample
               lowercase__		       =						scheduler.step_prk(UpperCAmelCase_     ,1     ,UpperCAmelCase_     ,**UpperCAmelCase_      ).prev_sample

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

               lowercase__		       =						scheduler.step_plms(UpperCAmelCase_     ,0     ,UpperCAmelCase_     ,**UpperCAmelCase_      ).prev_sample
               lowercase__		       =						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 							_a      (				self      )  ->							Any:
           for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[int]:
           for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=UpperCAmelCase_      )

           lowercase__		       =						self.scheduler_classes[0]
           lowercase__		       =						self.get_scheduler_config(steps_offset=1      )
           lowercase__		       =						scheduler_class(**UpperCAmelCase_      )
           scheduler.set_timesteps(10      )
           assert torch.equal(
               scheduler.timesteps     ,torch.LongTensor(
                   [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]      )     ,)



       def 							_a      (				self      )  ->							Optional[Any]:
           for beta_start, beta_end in zip([0.00_01, 0.0_01]     ,[0.0_02, 0.02]      ):
               self.check_over_configs(beta_start=UpperCAmelCase_     ,beta_end=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[int]:
           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           for t in [1, 5, 10]:
               self.check_over_forward(time_step=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							List[str]:
           for t, num_inference_steps in zip([1, 5, 10]     ,[10, 50, 100]      ):
               self.check_over_forward(num_inference_steps=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							List[Any]:
           # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
           lowercase__		       =						27

           for scheduler_class in self.scheduler_classes:
               lowercase__		       =						self.dummy_sample
               lowercase__		       =						0.1 * sample

               lowercase__		       =						self.get_scheduler_config()
               lowercase__		       =						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]      ):
                   lowercase__		       =						scheduler.step_prk(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      ).prev_sample



       def 							_a      (				self      )  ->							List[str]:
           with self.assertRaises(UpperCAmelCase_      ):
               lowercase__		       =						self.scheduler_classes[0]
               lowercase__		       =						self.get_scheduler_config()
               lowercase__		       =						scheduler_class(**UpperCAmelCase_      )

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



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						self.full_loop()
           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 1_98.13_18      ) < 1E-2
           assert abs(result_mean.item() - 0.25_80      ) < 1E-3



       def 							_a      (				self      )  ->							int:
           lowercase__		       =						self.full_loop(prediction_type="v_prediction"      )
           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 67.39_86      ) < 1E-2
           assert abs(result_mean.item() - 0.08_78      ) < 1E-3



       def 							_a      (				self      )  ->							Any:
           # We specify different beta, so that the first alpha is 0.99
           lowercase__		       =						self.full_loop(set_alpha_to_one=UpperCAmelCase_     ,beta_start=0.01      )
           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 2_30.03_99      ) < 1E-2
           assert abs(result_mean.item() - 0.29_95      ) < 1E-3







       def 							_a      (				self      )  ->							Optional[Any]:
           # We specify different beta, so that the first alpha is 0.99
           lowercase__		       =						self.full_loop(set_alpha_to_one=UpperCAmelCase_     ,beta_start=0.01      )
           lowercase__		       =						torch.sum(torch.abs(UpperCAmelCase_      )      )
           lowercase__		       =						torch.mean(torch.abs(UpperCAmelCase_      )      )

           assert abs(result_sum.item() - 1_86.94_82      ) < 1E-2
           assert abs(result_mean.item() - 0.24_34      ) < 1E-3









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int       ):





    '''simple docstring'''


    return x if y == 0 else greatest_common_divisor(_snake_case     ,x % y       )




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int       ):





    '''simple docstring'''


    return (x * y) // greatest_common_divisor(_snake_case     ,_snake_case       )




def        lowerCamelCase  (				_snake_case  :						int = 20       ):





    '''simple docstring'''


    lowercase__		       =						1
    for i in range(1     ,n + 1       ):
        lowercase__		       =						lcm(_snake_case     ,_snake_case       )
    return g


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




