






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class     _UpperCAmelCase  (						snake_case_     ):
    __lowerCamelCase:				int						    =      (DDIMScheduler,)
    __lowerCamelCase:				Union[str, Any]						    =      (("""eta""", 0.0), ("""num_inference_steps""", 50))




    def 			lowerCAmelCase__			(     self					:    Optional[int]					,       **a					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       {
           "num_train_timesteps": 1_0_0_0,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "clip_sample": True,
       }

       config.update(**a     )
       return config




    def 			lowerCAmelCase__			(     self					:    Any					,       **a					:    List[Any]     ):






       '''simple docstring'''






       lowercase_		:      Optional[int]          =       self.scheduler_classes[0]
       lowercase_		:      List[str]          =       self.get_scheduler_config(**a     )
       lowercase_		:      List[Any]          =       scheduler_class(**a     )

       lowercase_		:      Union[str, Any]          =       1_0, 0.0

       lowercase_		:      Union[str, Any]          =       self.dummy_model()
       lowercase_		:      Optional[int]          =       self.dummy_sample_deter

       scheduler.set_timesteps(a     )

       for t in scheduler.timesteps:
          lowercase_		:      Optional[int]          =       model(a					,       a     )
          lowercase_		:      Union[str, Any]          =       scheduler.step(a					,       a					,       a					,       a     ).prev_sample

       return sample




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






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

       lowercase_		:      int          =       self.scheduler_classes[0]
       lowercase_		:      List[Any]          =       self.get_scheduler_config(steps_offset=1     )
       lowercase_		:      List[str]          =       scheduler_class(**a     )
       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					:    Dict     ):






       '''simple docstring'''






       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,       [0.002, 0.02, 0.2, 2]     ):
          self.check_over_configs(beta_start=a					,       beta_end=a     )




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=a     )




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






       for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=a     )




    def 			lowerCAmelCase__			(     self					:    Optional[int]     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       for t in [1, 1_0, 4_9]:
          self.check_over_forward(time_step=a     )




    def 			lowerCAmelCase__			(     self					:    Any     ):






       '''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=a					,       num_inference_steps=a     )




    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=a					,       eta=a     )




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       lowercase_		:      Tuple          =       self.scheduler_classes[0]
       lowercase_		:      List[str]          =       self.get_scheduler_config()
       lowercase_		:      Tuple          =       scheduler_class(**a     )

       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_4771     )     ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_8_0					,       9_6_0     ) - 0.3_2460     )     ) < 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_0979     )     ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9					,       9_9_8     ) - 0.02     )     ) < 1e-5




    def 			lowerCAmelCase__			(     self					:    Optional[int]     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       self.full_loop()

       lowercase_		:      Optional[Any]          =       torch.sum(torch.abs(a     )     )
       lowercase_		:      List[Any]          =       torch.mean(torch.abs(a     )     )

       assert abs(result_sum.item() - 1_7_2.0_0_6_7     ) < 1e-2
       assert abs(result_mean.item() - 0.22_3967     ) < 1e-3




    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       self.full_loop(prediction_type="v_prediction"     )

       lowercase_		:      List[Any]          =       torch.sum(torch.abs(a     )     )
       lowercase_		:      Any          =       torch.mean(torch.abs(a     )     )

       assert abs(result_sum.item() - 52.5302     ) < 1e-2
       assert abs(result_mean.item() - 0.0684     ) < 1e-3




    def 			lowerCAmelCase__			(     self					:    Optional[int]     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       self.full_loop(set_alpha_to_one=a					,       beta_start=0.01     )
       lowercase_		:      List[Any]          =       torch.sum(torch.abs(a     )     )
       lowercase_		:      Dict          =       torch.mean(torch.abs(a     )     )

       assert abs(result_sum.item() - 1_4_9.8_2_9_5     ) < 1e-2
       assert abs(result_mean.item() - 0.1951     ) < 1e-3





    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       lowercase_		:      Optional[int]          =       self.full_loop(set_alpha_to_one=a					,       beta_start=0.01     )
       lowercase_		:      List[Any]          =       torch.sum(torch.abs(a     )     )
       lowercase_		:      Any          =       torch.mean(torch.abs(a     )     )

       assert abs(result_sum.item() - 1_4_9.0_7_8_4     ) < 1e-2
       assert abs(result_mean.item() - 0.1941     ) < 1e-3












'''simple docstring'''
UpperCamelCase__	=     (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
UpperCamelCase__	=     (-153, -1, 0, 1.2, 200, "A", [], {}, None)




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False

   # Initialization of sum and number of digits.
   lowercase_		:      Optional[int]          =       0
   lowercase_		:      Any          =       0
   lowercase_		:      Optional[Any]          =       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
   lowercase_		:      int          =       n
   while temp > 0:
      lowercase_		:      Any          =       temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False

   # Init a "histogram" of the digits
   lowercase_		:      Optional[Any]          =       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   lowercase_		:      Tuple          =       0
   lowercase_		:      Any          =       0
   lowercase_		:      Tuple          =       n
   while temp > 0:
      lowercase_      ,						lowercase_		:      Optional[Any]          =       divmod(_UpperCamelCase  ,						10							)
      digit_histogram[rem] += 1
      digit_total += 1

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

   return n == total




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   if not isinstance(_UpperCamelCase  ,						_UpperCamelCase							) or n < 1:
      return False
   lowercase_		:      List[str]          =       len(str(_UpperCamelCase							)							)  # 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(_UpperCamelCase							) ** expo for i in str(_UpperCamelCase							)							)




def 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   lowercase_		:      Dict          =       int(input("Enter an integer to see if it is an Armstrong number: "							).strip()							)
   print(F"""{num} is {"" if armstrong_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)
   print(F"""{num} is {"" if narcissistic_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)
   print(F"""{num} is {"" if pluperfect_number(_UpperCamelCase							) else "not "}an Armstrong number."""							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()




