



'''simple docstring'''


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: List[Any]  =      (PNDMScheduler,)
 __magic_name__					: Any  =      (("num_inference_steps", 50),)




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



      """simple docstring"""







      a_       		={
          "num_train_timesteps": 1_0_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**lowerCAmelCase_)
      return config




 def       lowercase_  (   self							,  lowerCAmelCase_=0							,  **lowerCAmelCase_) ->      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 + 0.1_5, residual + 0.1, residual + 0.0_5]

      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[:]

           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_prk(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample
           a_       		=new_scheduler.step_prk(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample

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

           a_       		=scheduler.step_plms(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample
           a_       		=new_scheduler.step_plms(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample

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




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      pass




 def       lowercase_  (   self							,  lowerCAmelCase_=0							,  **lowerCAmelCase_) ->      Dict:



      """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 + 0.1_5, residual + 0.1, residual + 0.0_5]

      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[:]

           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_prk(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample
           a_       		=new_scheduler.step_prk(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample

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

           a_       		=scheduler.step_plms(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample
           a_       		=new_scheduler.step_plms(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample

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




 def       lowercase_  (   self							,  **lowerCAmelCase_) ->      Optional[Any]:



      """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.prk_timesteps):
           a_       		=model(lowerCAmelCase_							,  lowerCAmelCase_)
           a_       		=scheduler.step_prk(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_).prev_sample

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

      return sample




 def       lowercase_  (   self) ->      Union[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 + 0.1_5, residual + 0.1, residual + 0.0_5]
           a_       		=dummy_past_residuals[:]

           a_       		=scheduler.step_prk(lowerCAmelCase_							,  0							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample
           a_       		=scheduler.step_prk(lowerCAmelCase_							,  1							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample

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

           a_       		=scheduler.step_plms(lowerCAmelCase_							,  0							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample
           a_       		=scheduler.step_plms(lowerCAmelCase_							,  1							,  lowerCAmelCase_							,  **lowerCAmelCase_).prev_sample

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




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







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




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=lowerCAmelCase_)

      a_       		=self.scheduler_classes[0]
      a_       		=self.get_scheduler_config(steps_offset=1)
      a_       		=scheduler_class(**lowerCAmelCase_)
      scheduler.set_timesteps(1_0)
      assert torch.equal(
          scheduler.timesteps							,  torch.LongTensor(
              [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1])							,  )




 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      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=lowerCAmelCase_							,  beta_end=lowerCAmelCase_)




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







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




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



      """simple docstring"""







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




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      for t in [1, 5, 1_0]:
           self.check_over_forward(time_step=lowerCAmelCase_)




 def       lowercase_  (   self) ->      str:



      """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_)




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		=2_7

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

           a_       		=self.get_scheduler_config()
           a_       		=scheduler_class(**lowerCAmelCase_)

           scheduler.set_timesteps(lowerCAmelCase_)

           # 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]):
                a_       		=scheduler.step_prk(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_).prev_sample




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      with self.assertRaises(lowerCAmelCase_):
           a_       		=self.scheduler_classes[0]
           a_       		=self.get_scheduler_config()
           a_       		=scheduler_class(**lowerCAmelCase_)

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




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      a_       		=self.full_loop()
      a_       		=torch.sum(torch.abs(lowerCAmelCase_))
      a_       		=torch.mean(torch.abs(lowerCAmelCase_))

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



      """simple docstring"""







      a_       		=self.full_loop(prediction_type="v_prediction")
      a_       		=torch.sum(torch.abs(lowerCAmelCase_))
      a_       		=torch.mean(torch.abs(lowerCAmelCase_))

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



      """simple docstring"""







      a_       		=self.full_loop(set_alpha_to_one=lowerCAmelCase_							,  beta_start=0.0_1)
      a_       		=torch.sum(torch.abs(lowerCAmelCase_))
      a_       		=torch.mean(torch.abs(lowerCAmelCase_))

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



      """simple docstring"""







      a_       		=self.full_loop(set_alpha_to_one=lowerCAmelCase_							,  beta_start=0.0_1)
      a_       		=torch.sum(torch.abs(lowerCAmelCase_))
      a_       		=torch.mean(torch.abs(lowerCAmelCase_))

      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'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=str(lowercase__       )
     return len(lowercase__       ) == 9 and set(lowercase__       ) == set("123456789"       )






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     for base_num in range(9_9_9_9       ,	4_9_9_9       ,	-1       ):
          a_       		=1_0_0_0_0_2 * base_num
          if is_9_pandigital(lowercase__       ):
               return candidate

     for base_num in range(3_3_3       ,	9_9       ,	-1       ):
          a_       		=1_0_0_2_0_0_3 * base_num
          if is_9_pandigital(lowercase__       ):
               return candidate

     return None


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


