import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class  _UpperCAmelCase       (			lowerCAmelCase__   ):



 '''simple docstring'''




 __A           = (DDIMScheduler,)
 __A           = (("eta", 0.0), ("num_inference_steps", 50))







 def 						__UpperCAmelCase							(						self							:					int				,      **lowercase_							:					Any)		->     List[Any]:


   """simple docstring"""







   _UpperCamelCase				=  {
       "num_train_timesteps": 1000,
       "beta_start": 0.00_01,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "clip_sample": True,
   }

   config.update(**a__)
   return config







 def 						__UpperCAmelCase							(						self							:					str				,      **lowercase_							:					Tuple)		->     Optional[int]:


   """simple docstring"""







   _UpperCamelCase				=  self.scheduler_classes[0]
   _UpperCamelCase				=  self.get_scheduler_config(**a__)
   _UpperCamelCase				=  scheduler_class(**a__)

   _UpperCamelCase	,		_UpperCamelCase				=  10, 0.0

   _UpperCamelCase				=  self.dummy_model()
   _UpperCamelCase				=  self.dummy_sample_deter

   scheduler.set_timesteps(a__)

   for t in scheduler.timesteps:
     _UpperCamelCase				=  model(a__				,      a__)
     _UpperCamelCase				=  scheduler.step(a__				,      a__				,      a__				,      a__).prev_sample

   return sample







 def 						__UpperCAmelCase							(						self							:					Any)		->     Tuple:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					str)		->     Optional[int]:


   """simple docstring"""







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

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







 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Tuple:


   """simple docstring"""







   for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,      [0.0_02, 0.02, 0.2, 2]):
     self.check_over_configs(beta_start=a__				,      beta_end=a__)







 def 						__UpperCAmelCase							(						self							:					str)		->     List[str]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Union[str, Any])		->     Optional[int]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					str)		->     List[str]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     str:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					int)		->     List[Any]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					str)		->     Tuple:


   """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 						__UpperCAmelCase							(						self							:					int)		->     List[Any]:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					List[str])		->     Tuple:


   """simple docstring"""







   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 						__UpperCAmelCase							(						self							:					int)		->     str:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					int)		->     Optional[int]:


   """simple docstring"""







   _UpperCamelCase				=  self.scheduler_classes[0]
   _UpperCamelCase				=  self.get_scheduler_config()
   _UpperCamelCase				=  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_47_71)) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(980				,      960) - 0.3_24_60)) < 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_09_79)) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(999				,      998) - 0.02)) < 1e-5







 def 						__UpperCAmelCase							(						self							:					int)		->     Any:


   """simple docstring"""







   _UpperCamelCase				=  self.full_loop()

   _UpperCamelCase				=  torch.sum(torch.abs(a__))
   _UpperCamelCase				=  torch.mean(torch.abs(a__))

   assert abs(result_sum.item() - 1_72.00_67) < 1e-2
   assert abs(result_mean.item() - 0.22_39_67) < 1e-3







 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     List[str]:


   """simple docstring"""







   _UpperCamelCase				=  self.full_loop(prediction_type="v_prediction")

   _UpperCamelCase				=  torch.sum(torch.abs(a__))
   _UpperCamelCase				=  torch.mean(torch.abs(a__))

   assert abs(result_sum.item() - 52.53_02) < 1e-2
   assert abs(result_mean.item() - 0.06_84) < 1e-3







 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.full_loop(set_alpha_to_one=a__				,      beta_start=0.01)
   _UpperCamelCase				=  torch.sum(torch.abs(a__))
   _UpperCamelCase				=  torch.mean(torch.abs(a__))

   assert abs(result_sum.item() - 1_49.82_95) < 1e-2
   assert abs(result_mean.item() - 0.19_51) < 1e-3







 def 						__UpperCAmelCase							(						self							:					str)		->     str:


   """simple docstring"""







   _UpperCamelCase				=  self.full_loop(set_alpha_to_one=a__				,      beta_start=0.01)
   _UpperCamelCase				=  torch.sum(torch.abs(a__))
   _UpperCamelCase				=  torch.mean(torch.abs(a__))

   assert abs(result_sum.item() - 1_49.07_84) < 1e-2
   assert abs(result_mean.item() - 0.19_41) < 1e-3











"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		list:
       _lowerCamelCase    	=					[]
       _lowerCamelCase    	=					1

       for i in range(len(snake_case		)		):
              if i + 1 < len(snake_case		) and text[i] == text[i + 1]:
                     count += 1
              else:
                     encoded.append((text[i], count)		)
                     _lowerCamelCase    	=					1

       return encoded





def SCREAMING_SNAKE_CASE_   (       snake_case   :					list		)->		str:
       return "".join(char * length for char, length in encoded		)


if __name__ == "__main__":
      from doctest import testmod

      testmod(name="""run_length_encode""", verbose=True)
      testmod(name="""run_length_decode""", verbose=True)






