


'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 	snake_case       (       SCREAMING_SNAKE_CASE_     ):





  """simple docstring"""




  _lowerCamelCase						       =    (DDIMParallelScheduler,)
  _lowerCamelCase						       =    (("""eta""", 0.0), ("""num_inference_steps""", 50))






  def     snake_case						(       self     ,    **UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    {
       'num_train_timesteps': 1000,
       'beta_start': 0.0_001,
       'beta_end': 0.02,
       'beta_schedule': 'linear',
       'clip_sample': True,
   }

   config.update(**UpperCamelCase	)
   return config






  def     snake_case						(       self     ,    **UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    self.scheduler_classes[0]
   lowerCamelCase_						=    self.get_scheduler_config(**UpperCamelCase	)
   lowerCamelCase_						=    scheduler_class(**UpperCamelCase	)

   lowerCamelCase_						=    10, 0.0

   lowerCamelCase_						=    self.dummy_model()
   lowerCamelCase_						=    self.dummy_sample_deter

   scheduler.set_timesteps(UpperCamelCase	)

   for t in scheduler.timesteps:
    lowerCamelCase_						=    model(UpperCamelCase     ,    UpperCamelCase	)
    lowerCamelCase_						=    scheduler.step(UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase	).prev_sample

   return sample






  def     snake_case						(       self	):







   """simple docstring"""
   for timesteps in [100, 500, 1000]:
    self.check_over_configs(num_train_timesteps=UpperCamelCase	)






  def     snake_case						(       self	):







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

   lowerCamelCase_						=    self.scheduler_classes[0]
   lowerCamelCase_						=    self.get_scheduler_config(steps_offset=1	)
   lowerCamelCase_						=    scheduler_class(**UpperCamelCase	)
   scheduler.set_timesteps(5	)
   assert torch.equal(scheduler.timesteps     ,    torch.LongTensor([801, 601, 401, 201, 1]	)	)






  def     snake_case						(       self	):







   """simple docstring"""
   for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]     ,    [0.002, 0.02, 0.2, 2]	):
    self.check_over_configs(beta_start=UpperCamelCase     ,    beta_end=UpperCamelCase	)






  def     snake_case						(       self	):







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






  def     snake_case						(       self	):







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






  def     snake_case						(       self	):







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






  def     snake_case						(       self	):







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






  def     snake_case						(       self	):







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






  def     snake_case						(       self	):







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






  def     snake_case						(       self	):







   """simple docstring"""
   for t in [1, 10, 49]:
    self.check_over_forward(time_step=UpperCamelCase	)






  def     snake_case						(       self	):







   """simple docstring"""
   for t, num_inference_steps in zip([1, 10, 50]     ,    [10, 50, 500]	):
    self.check_over_forward(time_step=UpperCamelCase     ,    num_inference_steps=UpperCamelCase	)






  def     snake_case						(       self	):







   """simple docstring"""
   for t, eta in zip([1, 10, 49]     ,    [0.0, 0.5, 1.0]	):
    self.check_over_forward(time_step=UpperCamelCase     ,    eta=UpperCamelCase	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    self.scheduler_classes[0]
   lowerCamelCase_						=    self.get_scheduler_config()
   lowerCamelCase_						=    scheduler_class(**UpperCamelCase	)

   assert torch.sum(torch.abs(scheduler._get_variance(0     ,    0	) - 0.0	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(420     ,    400	) - 0.14_771	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(980     ,    960	) - 0.32_460	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(0     ,    0	) - 0.0	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(487     ,    486	) - 0.00_979	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(999     ,    998	) - 0.02	)	) < 1e-5






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    self.scheduler_classes[0]
   lowerCamelCase_						=    self.get_scheduler_config()
   lowerCamelCase_						=    scheduler_class(**UpperCamelCase	)

   lowerCamelCase_						=    10, 0.0
   scheduler.set_timesteps(UpperCamelCase	)

   lowerCamelCase_						=    self.dummy_model()
   lowerCamelCase_						=    self.dummy_sample_deter
   lowerCamelCase_						=    self.dummy_sample_deter + 0.1
   lowerCamelCase_						=    self.dummy_sample_deter - 0.1

   lowerCamelCase_						=    samplea.shape[0]
   lowerCamelCase_						=    torch.stack([samplea, samplea, samplea]     ,    dim=0	)
   lowerCamelCase_						=    torch.arange(UpperCamelCase	)[0:3, None].repeat(1     ,    UpperCamelCase	)

   lowerCamelCase_						=    model(samples.flatten(0     ,    1	)     ,    timesteps.flatten(0     ,    1	)	)
   lowerCamelCase_						=    scheduler.batch_step_no_noise(UpperCamelCase     ,    timesteps.flatten(0     ,    1	)     ,    samples.flatten(0     ,    1	)     ,    UpperCamelCase	)

   lowerCamelCase_						=    torch.sum(torch.abs(UpperCamelCase	)	)
   lowerCamelCase_						=    torch.mean(torch.abs(UpperCamelCase	)	)

   assert abs(result_sum.item() - 1147.7904	) < 1e-2
   assert abs(result_mean.item() - 0.4_982	) < 1e-3






  def     snake_case						(       self	):







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

   lowerCamelCase_						=    torch.sum(torch.abs(UpperCamelCase	)	)
   lowerCamelCase_						=    torch.mean(torch.abs(UpperCamelCase	)	)

   assert abs(result_sum.item() - 172.0067	) < 1e-2
   assert abs(result_mean.item() - 0.223_967	) < 1e-3






  def     snake_case						(       self	):







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

   lowerCamelCase_						=    torch.sum(torch.abs(UpperCamelCase	)	)
   lowerCamelCase_						=    torch.mean(torch.abs(UpperCamelCase	)	)

   assert abs(result_sum.item() - 52.5302	) < 1e-2
   assert abs(result_mean.item() - 0.0_684	) < 1e-3






  def     snake_case						(       self	):







   """simple docstring"""
   # We specify different beta, so that the first alpha is 0.99
   lowerCamelCase_						=    self.full_loop(set_alpha_to_one=UpperCamelCase     ,    beta_start=0.01	)
   lowerCamelCase_						=    torch.sum(torch.abs(UpperCamelCase	)	)
   lowerCamelCase_						=    torch.mean(torch.abs(UpperCamelCase	)	)

   assert abs(result_sum.item() - 149.8295	) < 1e-2
   assert abs(result_mean.item() - 0.1_951	) < 1e-3







  def     snake_case						(       self	):







   """simple docstring"""
   # We specify different beta, so that the first alpha is 0.99
   lowerCamelCase_						=    self.full_loop(set_alpha_to_one=UpperCamelCase     ,    beta_start=0.01	)
   lowerCamelCase_						=    torch.sum(torch.abs(UpperCamelCase	)	)
   lowerCamelCase_						=    torch.mean(torch.abs(UpperCamelCase	)	)

   assert abs(result_sum.item() - 149.0784	) < 1e-2
   assert abs(result_mean.item() - 0.1_941	) < 1e-3








"""simple docstring"""







def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					str	       =    0
			for i in range(1      ,							10_01						):
						total += i**i
			return str(__a						)[-10:]


if __name__ == "__main__":
	print(solution())





