
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[int]	   =      (CMStochasticIterativeScheduler,)
 __lowerCAmelCase	:  int	   =      10


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   {
       """num_train_timesteps""": 2_01,
       """sigma_min""": 0.0_0_2,
       """sigma_max""": 8_0.0,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   10

   lowercase__	: Union[str, Any]     		=   self.get_scheduler_config()
   lowercase__	: List[str]     		=   self.scheduler_classes[0](**SCREAMING_SNAKE_CASE_)

   scheduler.set_timesteps(SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   scheduler.timesteps[0]
   lowercase__	: str     		=   scheduler.timesteps[1]

   lowercase__	: List[Any]     		=   self.dummy_sample
   lowercase__	: str     		=   0.1 * sample

   lowercase__	: List[Any]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_).prev_sample
   lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_).prev_sample

   self.assertEqual(output_a.shape       ,	sample.shape)
   self.assertEqual(output_a.shape       ,	output_a.shape)


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [10, 50, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for clip_denoised in [True, False]:
     self.check_over_configs(clip_denoised=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   1
   scheduler.set_timesteps(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   scheduler.timesteps

   lowercase__	: int     		=   torch.manual_seed(0)

   lowercase__	: List[str]     		=   self.dummy_model()
   lowercase__	: str     		=   self.dummy_sample_deter * scheduler.init_noise_sigma

   for i, t in enumerate(SCREAMING_SNAKE_CASE_):
     # 1. scale model input
     lowercase__	: Dict     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict noise residual
     lowercase__	: Optional[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 3. predict previous sample x_t-1
     lowercase__	: Optional[int]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     lowercase__	: int     		=   pred_prev_sample

   lowercase__	: int     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Any     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_9_2.7_6_1_4) < 1E-2
   assert abs(result_mean.item() - 0.2_5_1_0) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Union[str, Any]     		=   self.get_scheduler_config()
   lowercase__	: Optional[int]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   [1_06, 0]
   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   scheduler.timesteps

   lowercase__	: List[Any]     		=   torch.manual_seed(0)

   lowercase__	: Optional[int]     		=   self.dummy_model()
   lowercase__	: Tuple     		=   self.dummy_sample_deter * scheduler.init_noise_sigma

   for t in timesteps:
     # 1. scale model input
     lowercase__	: Any     		=   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict noise residual
     lowercase__	: Optional[int]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 3. predict previous sample x_t-1
     lowercase__	: Any     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     lowercase__	: Any     		=   pred_prev_sample

   lowercase__	: Optional[int]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Union[str, Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 3_4_7.6_3_5_7) < 1E-2
   assert abs(result_mean.item() - 0.4_5_2_7) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   [39, 30, 12, 15, 0]

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""`timesteps` must be in descending order."""):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Dict     		=   self.get_scheduler_config()
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   [39, 30, 12, 1, 0]
   lowercase__	: str     		=   len(SCREAMING_SNAKE_CASE_)

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""Can only pass one of `num_inference_steps` or `timesteps`."""):
     scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_       ,	timesteps=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   self.scheduler_classes[0]
   lowercase__	: Dict     		=   self.get_scheduler_config()
   lowercase__	: Any     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[int]     		=   [scheduler.config.num_train_timesteps]

   with self.assertRaises(
       SCREAMING_SNAKE_CASE_       ,	msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,	):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)










def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						assert column_title.isupper()
						__snake_case				=		0
						__snake_case				=		len(_lowerCAmelCase			) - 1
						__snake_case				=		0

						while index >= 0:
												__snake_case				=		(ord(column_title[index]			) - 64) * pow(26   ,						_lowerCAmelCase			)
												answer += value
												power += 1
												index -= 1

						return answer


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

	testmod()





