'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class     _lowercase					(    _UpperCAmelCase      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	str				     = (DDIMScheduler,)
 _SCREAMING_SNAKE_CASE					:	Dict				     = (("""eta""", 0.0), ("""num_inference_steps""", 50))
 def a (				self       :     str				,	**SCREAMING_SNAKE_CASE__       :     Dict							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						{
      "num_train_timesteps": 10_00,
      "beta_start": 0.0_0_0_1,
      "beta_end": 0.0_2,
      "beta_schedule": "linear",
      "clip_sample": True,
  }

  config.update(**lowercase__							)
  return config
 def a (				self       :     Optional[int]				,	**SCREAMING_SNAKE_CASE__       :     List[Any]							)  ->   str:
  __lowerCAmelCase    							=						self.scheduler_classes[0]
  __lowerCAmelCase    							=						self.get_scheduler_config(**lowercase__							)
  __lowerCAmelCase    							=						scheduler_class(**lowercase__							)

  __lowerCAmelCase    							=						10, 0.0

  __lowerCAmelCase    							=						self.dummy_model()
  __lowerCAmelCase    							=						self.dummy_sample_deter

  scheduler.set_timesteps(lowercase__							)

  for t in scheduler.timesteps:
   __lowerCAmelCase    							=						model(lowercase__				,	lowercase__							)
   __lowerCAmelCase    							=						scheduler.step(lowercase__				,	lowercase__				,	lowercase__				,	lowercase__							).prev_sample

  return sample
 def a (				self       :     str							)  ->   Tuple:






  for timesteps in [1_00, 5_00, 10_00]:
   self.check_over_configs(num_train_timesteps=lowercase__							)
 def a (				self       :     List[str]							)  ->   Optional[int]:






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

  __lowerCAmelCase    							=						self.scheduler_classes[0]
  __lowerCAmelCase    							=						self.get_scheduler_config(steps_offset=1							)
  __lowerCAmelCase    							=						scheduler_class(**lowercase__							)
  scheduler.set_timesteps(5							)
  assert torch.equal(scheduler.timesteps				,	torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]							)							)
 def a (				self       :     str							)  ->   Tuple:






  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=lowercase__				,	beta_end=lowercase__							)
 def a (				self       :     Dict							)  ->   Optional[int]:






  for schedule in ["linear", "squaredcos_cap_v2"]:
   self.check_over_configs(beta_schedule=lowercase__							)
 def a (				self       :     str							)  ->   str:






  for prediction_type in ["epsilon", "v_prediction"]:
   self.check_over_configs(prediction_type=lowercase__							)
 def a (				self       :     List[str]							)  ->   Union[str, Any]:






  for clip_sample in [True, False]:
   self.check_over_configs(clip_sample=lowercase__							)
 def a (				self       :     str							)  ->   Any:






  for timestep_spacing in ["trailing", "leading"]:
   self.check_over_configs(timestep_spacing=lowercase__							)
 def a (				self       :     Tuple							)  ->   List[str]:






  for rescale_betas_zero_snr in [True, False]:
   self.check_over_configs(rescale_betas_zero_snr=lowercase__							)
 def a (				self       :     str							)  ->   List[Any]:
  self.check_over_configs(thresholding=lowercase__							)
  for threshold in [0.5, 1.0, 2.0]:
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(
        thresholding=lowercase__				,	prediction_type=lowercase__				,	sample_max_value=lowercase__				,	)
 def a (				self       :     List[Any]							)  ->   Optional[int]:






  for t in [1, 10, 49]:
   self.check_over_forward(time_step=lowercase__							)
 def a (				self       :     Any							)  ->   int:






  for t, num_inference_steps in zip([1, 10, 50]				,	[10, 50, 5_00]							):
   self.check_over_forward(time_step=lowercase__				,	num_inference_steps=lowercase__							)
 def a (				self       :     int							)  ->   Any:






  for t, eta in zip([1, 10, 49]				,	[0.0, 0.5, 1.0]							):
   self.check_over_forward(time_step=lowercase__				,	eta=lowercase__							)
 def a (				self       :     int							)  ->   List[str]:
  __lowerCAmelCase    							=						self.scheduler_classes[0]
  __lowerCAmelCase    							=						self.get_scheduler_config()
  __lowerCAmelCase    							=						scheduler_class(**lowercase__							)

  assert torch.sum(torch.abs(scheduler._get_variance(0				,	0							) - 0.0							)							) < 1e-5
  assert torch.sum(torch.abs(scheduler._get_variance(4_20				,	4_00							) - 0.1_4_7_7_1							)							) < 1e-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_80				,	9_60							) - 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_87				,	4_86							) - 0.0_0_9_7_9							)							) < 1e-5
  assert torch.sum(torch.abs(scheduler._get_variance(9_99				,	9_98							) - 0.0_2							)							) < 1e-5
 def a (				self       :     Dict							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						self.full_loop()

  __lowerCAmelCase    							=						torch.sum(torch.abs(lowercase__							)							)
  __lowerCAmelCase    							=						torch.mean(torch.abs(lowercase__							)							)

  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 a (				self       :     Tuple							)  ->   int:
  __lowerCAmelCase    							=						self.full_loop(prediction_type="""v_prediction"""							)

  __lowerCAmelCase    							=						torch.sum(torch.abs(lowercase__							)							)
  __lowerCAmelCase    							=						torch.mean(torch.abs(lowercase__							)							)

  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 a (				self       :     int							)  ->   str:
  __lowerCAmelCase    							=						self.full_loop(set_alpha_to_one=lowercase__				,	beta_start=0.0_1							)
  __lowerCAmelCase    							=						torch.sum(torch.abs(lowercase__							)							)
  __lowerCAmelCase    							=						torch.mean(torch.abs(lowercase__							)							)

  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 a (				self       :     Dict							)  ->   List[str]:
  __lowerCAmelCase    							=						self.full_loop(set_alpha_to_one=lowercase__				,	beta_start=0.0_1							)
  __lowerCAmelCase    							=						torch.sum(torch.abs(lowercase__							)							)
  __lowerCAmelCase    							=						torch.mean(torch.abs(lowercase__							)							)

  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 					UpperCamelCase_						( snake_case_	:	int				) ->					int:






 '''simple docstring'''

 if number != int(snake_case_				):
  raise ValueError("""factorial() only accepts integral values"""				)
 if number < 0:
  raise ValueError("""factorial() not defined for negative values"""				)
 __lowerCAmelCase    							=						1
 for i in range(1						,		number + 1				):
  value *= i
 return value







def 					UpperCamelCase_						( snake_case_	:	int				) ->					int:






 '''simple docstring'''

 if not isinstance(snake_case_						,		snake_case_				):
  raise ValueError("""factorial() only accepts integral values"""				)
 if n < 0:
  raise ValueError("""factorial() not defined for negative values"""				)
 return 1 if n == 0 or n == 1 else n * factorial(n - 1				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _A      :				List[Any] 				=    int(input('''Enter a positive integer: ''').strip() or 0)
 print(f'factorial{n} is {factorial(n)}')






