




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		(LMSDiscreteScheduler,)
   lowerCamelCase_       						=		10



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   {
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**__lowerCamelCase					)
      return config



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""







      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""







      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,   [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
         self.check_over_configs(beta_start=__lowerCamelCase						,   beta_end=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""







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



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""







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



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""







      for t in [0, 5_0_0, 8_0_0]:
         self.check_over_forward(time_step=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

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

      scheduler.set_timesteps(self.num_inference_steps					)

      _SCREAMING_SNAKE_CASE =   self.dummy_model()
      _SCREAMING_SNAKE_CASE =   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps					):
         _SCREAMING_SNAKE_CASE =   scheduler.scale_model_input(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   scheduler.step(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   output.prev_sample

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

      assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
      assert abs(result_mean.item() - 1.3_1					) < 1e-3



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

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

      scheduler.set_timesteps(self.num_inference_steps					)

      _SCREAMING_SNAKE_CASE =   self.dummy_model()
      _SCREAMING_SNAKE_CASE =   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps					):
         _SCREAMING_SNAKE_CASE =   scheduler.scale_model_input(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   scheduler.step(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   output.prev_sample

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

      assert abs(result_sum.item() - 0.0_0_1_7					) < 1e-2
      assert abs(result_mean.item() - 2.2_676e-06					) < 1e-3



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

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

      scheduler.set_timesteps(self.num_inference_steps						,   device=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.dummy_model()
      _SCREAMING_SNAKE_CASE =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _SCREAMING_SNAKE_CASE =   sample.to(__lowerCamelCase					)

      for i, t in enumerate(scheduler.timesteps					):
         _SCREAMING_SNAKE_CASE =   scheduler.scale_model_input(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   scheduler.step(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   output.prev_sample

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

      assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
      assert abs(result_mean.item() - 1.3_1					) < 1e-3



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

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

      scheduler.set_timesteps(self.num_inference_steps						,   device=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.dummy_model()
      _SCREAMING_SNAKE_CASE =   self.dummy_sample_deter.to(__lowerCamelCase					) * scheduler.init_noise_sigma
      _SCREAMING_SNAKE_CASE =   sample.to(__lowerCamelCase					)

      for t in scheduler.timesteps:
         _SCREAMING_SNAKE_CASE =   scheduler.scale_model_input(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   __lowerCamelCase					)

         _SCREAMING_SNAKE_CASE =   scheduler.step(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   output.prev_sample

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

      assert abs(result_sum.item() - 3_8_1_2.9_9_2_7					) < 2e-2
      assert abs(result_mean.item() - 4.9_6_4_8					) < 1e-3







'''simple docstring'''



lowerCamelCase_	 =		(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCamelCase_	 =		(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Initialization of sum and number of digits.
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE =   temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Init a "histogram" of the digits
   _SCREAMING_SNAKE_CASE =   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   divmod(__A ,  10						)
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(__A ,  range(len(__A						)						)						):
      total += cnt * i**digit_total

   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False
   _SCREAMING_SNAKE_CASE =   len(str(__A						)						)  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(__A						) ** expo for i in str(__A						)						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   int(input("Enter an integer to see if it is an Armstrong number: "						).strip()						)
   print(f"""{num} is {"" if armstrong_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if narcissistic_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if pluperfect_number(__A						) else "not "}an Armstrong number."""						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

