




'''simple docstring'''



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		(DDIMScheduler,)
   lowerCamelCase_       						=		(('''eta''', 0.0), ('''num_inference_steps''', 50))



   def 							lowerCAmelCase_    (					self      :			str						,   **__lowerCamelCase      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   {
          "num_train_timesteps": 1_0_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "clip_sample": True,
      }

      config.update(**__lowerCamelCase					)
      return config



   def 							lowerCAmelCase_    (					self      :			Tuple						,   **__lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE =   self.get_scheduler_config(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   scheduler_class(**__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   1_0, 0.0

      _SCREAMING_SNAKE_CASE =   self.dummy_model()
      _SCREAMING_SNAKE_CASE =   self.dummy_sample_deter

      scheduler.set_timesteps(__lowerCamelCase					)

      for t in scheduler.timesteps:
         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   __lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   scheduler.step(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					).prev_sample

      return sample



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE =   self.get_scheduler_config(steps_offset=1					)
      _SCREAMING_SNAKE_CASE =   scheduler_class(**__lowerCamelCase					)
      scheduler.set_timesteps(5					)
      assert torch.equal(scheduler.timesteps						,   torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]					)					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]						,   [0.0_0_2, 0.0_2, 0.2, 2]					):
         self.check_over_configs(beta_start=__lowerCamelCase						,   beta_end=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int					):

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



   def 							lowerCAmelCase_    (					self      :			List[str]					):

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



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      self.check_over_configs(thresholding=__lowerCamelCase					)
      for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=__lowerCamelCase						,   prediction_type=__lowerCamelCase						,   sample_max_value=__lowerCamelCase						,   )



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      for t in [1, 1_0, 4_9]:
         self.check_over_forward(time_step=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      for t, num_inference_steps in zip([1, 1_0, 5_0]						,   [1_0, 5_0, 5_0_0]					):
         self.check_over_forward(time_step=__lowerCamelCase						,   num_inference_steps=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      for t, eta in zip([1, 1_0, 4_9]						,   [0.0, 0.5, 1.0]					):
         self.check_over_forward(time_step=__lowerCamelCase						,   eta=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE =   self.get_scheduler_config()
      _SCREAMING_SNAKE_CASE =   scheduler_class(**__lowerCamelCase					)

      assert torch.sum(torch.abs(scheduler._get_variance(0						,   0					) - 0.0					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_2_0						,   4_0_0					) - 0.1_4_7_7_1					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_8_0						,   9_6_0					) - 0.3_2_4_6_0					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(0						,   0					) - 0.0					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_8_7						,   4_8_6					) - 0.0_0_9_7_9					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						,   9_9_8					) - 0.0_2					)					) < 1e-5



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.full_loop()

      _SCREAMING_SNAKE_CASE =   torch.sum(torch.abs(__lowerCamelCase					)					)
      _SCREAMING_SNAKE_CASE =   torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 1_7_2.0_0_6_7					) < 1e-2
      assert abs(result_mean.item() - 0.2_2_3_9_6_7					) < 1e-3



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.full_loop(prediction_type="v_prediction"					)

      _SCREAMING_SNAKE_CASE =   torch.sum(torch.abs(__lowerCamelCase					)					)
      _SCREAMING_SNAKE_CASE =   torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 5_2.5_3_0_2					) < 1e-2
      assert abs(result_mean.item() - 0.0_6_8_4					) < 1e-3



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      # We specify different beta, so that the first alpha is 0.99
      _SCREAMING_SNAKE_CASE =   self.full_loop(set_alpha_to_one=__lowerCamelCase						,   beta_start=0.0_1					)
      _SCREAMING_SNAKE_CASE =   torch.sum(torch.abs(__lowerCamelCase					)					)
      _SCREAMING_SNAKE_CASE =   torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 1_4_9.8_2_9_5					) < 1e-2
      assert abs(result_mean.item() - 0.1_9_5_1					) < 1e-3



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      # We specify different beta, so that the first alpha is 0.99
      _SCREAMING_SNAKE_CASE =   self.full_loop(set_alpha_to_one=__lowerCamelCase						,   beta_start=0.0_1					)
      _SCREAMING_SNAKE_CASE =   torch.sum(torch.abs(__lowerCamelCase					)					)
      _SCREAMING_SNAKE_CASE =   torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 1_4_9.0_7_8_4					) < 1e-2
      assert abs(result_mean.item() - 0.1_9_4_1					) < 1e-3







'''simple docstring'''



def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Dict						)				->				Union[str, Any]:
   if n == 0:
      return 1

   elif n % 2 == 1:
      return binary_exponentiation(__A ,  n - 1						) * a

   else:
      _SCREAMING_SNAKE_CASE =   binary_exponentiation(__A ,  n / 2						)
      return b * b


if __name__ == "__main__":
 try:
  lowerCamelCase_	 =		int(input('Enter Base : ').strip())
  lowerCamelCase_	 =		int(input('Enter Power : ').strip())
 except ValueError:
  print('Invalid literal for integer')

 lowerCamelCase_	 =		binary_exponentiation(BASE, POWER)
 print(f'''{BASE}^({POWER}) : {RESULT}''')

