


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 	_snake_case						(   a__							):
 SCREAMING_SNAKE_CASE__   = (DDIMScheduler,)
 SCREAMING_SNAKE_CASE__   = (('eta', 0.0), ('num_inference_steps', 50))




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:Any											= {
           "num_train_timesteps": 1000,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "clip_sample": True,
       }

       config.update(**__A					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:Optional[int]											= self.scheduler_classes[0]
       a		:Tuple											= self.get_scheduler_config(**__A					)
       a		:Optional[int]											= scheduler_class(**__A					)

       a		:int											= 10, 0.0

       a		:Optional[int]											= self.dummy_model()
       a		:Any											= self.dummy_sample_deter

       scheduler.set_timesteps(__A					)

       for t in scheduler.timesteps:
             a		:Tuple											= model(__A      ,						__A					)
             a		:int											= scheduler.step(__A      ,						__A      ,						__A      ,						__A					).prev_sample

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):







       for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=__A					)




 def  SCREAMING_SNAKE_CASE__       (	self					):







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

       a		:List[Any]											= self.scheduler_classes[0]
       a		:int											= self.get_scheduler_config(steps_offset=1					)
       a		:Optional[Any]											= scheduler_class(**__A					)
       scheduler.set_timesteps(5					)
       assert torch.equal(scheduler.timesteps      ,						torch.LongTensor([801, 601, 401, 201, 1]					)					)




 def  SCREAMING_SNAKE_CASE__       (	self					):







       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  SCREAMING_SNAKE_CASE__       (	self					):







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




 def  SCREAMING_SNAKE_CASE__       (	self					):







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




 def  SCREAMING_SNAKE_CASE__       (	self					):







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




 def  SCREAMING_SNAKE_CASE__       (	self					):







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




 def  SCREAMING_SNAKE_CASE__       (	self					):







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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       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  SCREAMING_SNAKE_CASE__       (	self					):







       for t in [1, 10, 49]:
             self.check_over_forward(time_step=__A					)




 def  SCREAMING_SNAKE_CASE__       (	self					):







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




 def  SCREAMING_SNAKE_CASE__       (	self					):







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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= self.scheduler_classes[0]
       a		:Any											= self.get_scheduler_config()
       a		:List[Any]											= 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(420      ,						400					) - 0.1_4771					)					) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980      ,						960					) - 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(487      ,						486					) - 0.0_0979					)					) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999      ,						998					) - 0.02					)					) < 1e-5




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= self.full_loop()

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

       assert abs(result_sum.item() - 172.0067					) < 1e-2
       assert abs(result_mean.item() - 0.22_3967					) < 1e-3




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[Any]											= self.full_loop(prediction_type='''v_prediction'''					)

       a		:int											= torch.sum(torch.abs(__A					)					)
       a		:str											= torch.mean(torch.abs(__A					)					)

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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= self.full_loop(set_alpha_to_one=__A      ,						beta_start=0.01					)
       a		:Union[str, Any]											= torch.sum(torch.abs(__A					)					)
       a		:List[Any]											= torch.mean(torch.abs(__A					)					)

       assert abs(result_sum.item() - 149.8295					) < 1e-2
       assert abs(result_mean.item() - 0.1951					) < 1e-3




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= self.full_loop(set_alpha_to_one=__A      ,						beta_start=0.01					)
       a		:Union[str, Any]											= torch.sum(torch.abs(__A					)					)
       a		:str											= torch.mean(torch.abs(__A					)					)

       assert abs(result_sum.item() - 149.0784					) < 1e-2
       assert abs(result_mean.item() - 0.1941					) < 1e-3









import numpy as np
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a							,       a		:Union[str, Any]											= a.shape
      a		:Any											= min(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Tuple											= np.eye(UpperCAmelCase_		)
      a		:Any											= a.copy()

      for k in range(t - 1		):
            # select a column of modified matrix A':
            a		:List[str]											= r[k:, [k]]
            # construct first basis vector
            a		:List[str]											= np.zeros_like(UpperCAmelCase_		)
            a		:List[Any]											= 1.0
            # determine scaling factor
            a		:Union[str, Any]											= np.linalg.norm(UpperCAmelCase_		)
            # construct vector v for Householder reflection
            a		:List[str]											= x + np.sign(x[0]		) * alpha * ea
            v /= np.linalg.norm(UpperCAmelCase_		)

            # construct the Householder matrix
            a		:Union[str, Any]											= np.eye(m - k		) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            a		:Tuple											= np.block([[np.eye(UpperCAmelCase_		), np.zeros((k, m - k)		)], [np.zeros((m - k, k)		), q_k]]		)

            a		:List[Any]											= q @ q_k.T
            a		:int											= q_k @ r

      return q, r


if __name__ == "__main__":
     import doctest

     doctest.testmod()





