



'''simple docstring'''





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: int    = (PNDMScheduler,)
 __lowerCamelCase					: str    = (('num_inference_steps', 50),)



 def       a__						(self				,     **A )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						{
          '''num_train_timesteps''': 1_000,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
      }

      config.update(**A )
      return config



 def       a__						(self				,     A=0				,     **A )					->  Tuple:


      """simple docstring"""



      _a											=						dict(self.forward_default_kwargs )
      _a											=						kwargs.pop('''num_inference_steps'''				,     A )
      _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 )
           _a											=						scheduler_class(**A )
           scheduler.set_timesteps(A )
           # copy over dummy past residuals
           _a											=						dummy_past_residuals[:]

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

           _a											=						scheduler.step_prk(A				,     A				,     A				,     **A ).prev_sample
           _a											=						new_scheduler.step_prk(A				,     A				,     A				,     **A ).prev_sample

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

           _a											=						scheduler.step_plms(A				,     A				,     A				,     **A ).prev_sample
           _a											=						new_scheduler.step_plms(A				,     A				,     A				,     **A ).prev_sample

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



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


      """simple docstring"""



      pass



 def       a__						(self				,     A=0				,     **A )					->  str:


      """simple docstring"""



      _a											=						dict(self.forward_default_kwargs )
      _a											=						kwargs.pop('''num_inference_steps'''				,     A )
      _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(**A )
           scheduler.set_timesteps(A )

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

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(A )
                _a											=						scheduler_class.from_pretrained(A )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(A )

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

           _a											=						scheduler.step_prk(A				,     A				,     A				,     **A ).prev_sample
           _a											=						new_scheduler.step_prk(A				,     A				,     A				,     **A ).prev_sample

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

           _a											=						scheduler.step_plms(A				,     A				,     A				,     **A ).prev_sample
           _a											=						new_scheduler.step_plms(A				,     A				,     A				,     **A ).prev_sample

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



 def       a__						(self				,     **A )					->  List[str]:


      """simple docstring"""



      _a											=						self.scheduler_classes[0]
      _a											=						self.get_scheduler_config(**A )
      _a											=						scheduler_class(**A )

      _a											=						10
      _a											=						self.dummy_model()
      _a											=						self.dummy_sample_deter
      scheduler.set_timesteps(A )

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

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

      return sample



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						dict(self.forward_default_kwargs )

      _a											=						kwargs.pop('''num_inference_steps'''				,     A )

      for scheduler_class in self.scheduler_classes:
           _a											=						self.get_scheduler_config()
           _a											=						scheduler_class(**A )

           _a											=						self.dummy_sample
           _a											=						0.1 * sample

           if num_inference_steps is not None and hasattr(A				,     '''set_timesteps''' ):
                scheduler.set_timesteps(A )
           elif num_inference_steps is not None and not hasattr(A				,     '''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.step_prk(A				,     0				,     A				,     **A ).prev_sample
           _a											=						scheduler.step_prk(A				,     1				,     A				,     **A ).prev_sample

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

           _a											=						scheduler.step_plms(A				,     0				,     A				,     **A ).prev_sample
           _a											=						scheduler.step_plms(A				,     1				,     A				,     **A ).prev_sample

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



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


      """simple docstring"""



      for timesteps in [100, 1_000]:
           self.check_over_configs(num_train_timesteps=A )



 def       a__						(self )					->  int:


      """simple docstring"""



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

      _a											=						self.scheduler_classes[0]
      _a											=						self.get_scheduler_config(steps_offset=1 )
      _a											=						scheduler_class(**A )
      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 )					->  Union[str, Any]:


      """simple docstring"""



      for beta_start, beta_end in zip([0.0001, 0.001]				,     [0.002, 0.02] ):
           self.check_over_configs(beta_start=A				,     beta_end=A )



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


      """simple docstring"""



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



 def       a__						(self )					->  str:


      """simple docstring"""



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



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



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



 def       a__						(self )					->  Tuple:


      """simple docstring"""



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



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						27

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

           _a											=						self.get_scheduler_config()
           _a											=						scheduler_class(**A )

           scheduler.set_timesteps(A )

           # 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(A				,     A				,     A ).prev_sample



 def       a__						(self )					->  Any:


      """simple docstring"""



      with self.assertRaises(A ):
           _a											=						self.scheduler_classes[0]
           _a											=						self.get_scheduler_config()
           _a											=						scheduler_class(**A )

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



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						self.full_loop()
      _a											=						torch.sum(torch.abs(A ) )
      _a											=						torch.mean(torch.abs(A ) )

      assert abs(result_sum.item() - 198.1318 ) < 1E-2
      assert abs(result_mean.item() - 0.2580 ) < 1E-3



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.full_loop(prediction_type='''v_prediction''' )
      _a											=						torch.sum(torch.abs(A ) )
      _a											=						torch.mean(torch.abs(A ) )

      assert abs(result_sum.item() - 67.3986 ) < 1E-2
      assert abs(result_mean.item() - 0.0878 ) < 1E-3



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.full_loop(set_alpha_to_one=A				,     beta_start=0.01 )
      _a											=						torch.sum(torch.abs(A ) )
      _a											=						torch.mean(torch.abs(A ) )

      assert abs(result_sum.item() - 230.0399 ) < 1E-2
      assert abs(result_mean.item() - 0.2995 ) < 1E-3



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						self.full_loop(set_alpha_to_one=A				,     beta_start=0.01 )
      _a											=						torch.sum(torch.abs(A ) )
      _a											=						torch.mean(torch.abs(A ) )

      assert abs(result_sum.item() - 186.9482 ) < 1E-2
      assert abs(result_mean.item() - 0.2434 ) < 1E-3








'''simple docstring'''





def lowerCAmelCase						(__A = 1_000):



     """simple docstring"""



     _a											=						2**power
     _a											=						str(__A)
     _a											=						list(__A)
     _a											=						0

     for i in list_num:
          sum_of_num += int(__A)

     return sum_of_num


if __name__ == "__main__":
  lowercase_	 =						int(input("Enter the power of 2: ").strip())
  print("2 ^ ", power, " = ", 2**power)
  lowercase_	 =						solution(power)
  print("Sum of the digits is: ", result)



