





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class   snake_case_							(						A__		):
  A_     				=       (UnCLIPScheduler,)






  def  UpperCAmelCase__		(				self					:	Optional[Any]  ,    **_snake_case					:	str     )->Dict:






   '''simple docstring'''

   __lowerCAmelCase	:			Union[str, Any]         =  {
       """num_train_timesteps""": 1000,
       """variance_type""": """fixed_small_log""",
       """clip_sample""": True,
       """clip_sample_range""": 1.0,
       """prediction_type""": """epsilon""",
   }

   config.update(**__UpperCAmelCase     )
   return config






  def  UpperCAmelCase__		(				self					:	Any     )->Optional[int]:






   '''simple docstring'''

   for timesteps in [1, 5, 100, 1000]:
    self.check_over_configs(num_train_timesteps=__UpperCAmelCase     )






  def  UpperCAmelCase__		(				self					:	List[str]     )->Any:






   '''simple docstring'''

   for variance in ["fixed_small_log", "learned_range"]:
    self.check_over_configs(variance_type=__UpperCAmelCase     )






  def  UpperCAmelCase__		(				self					:	int     )->Optional[Any]:






   '''simple docstring'''

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






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






   '''simple docstring'''

   for clip_sample_range in [1, 5, 10, 20]:
    self.check_over_configs(clip_sample_range=__UpperCAmelCase     )






  def  UpperCAmelCase__		(				self					:	List[str]     )->Any:






   '''simple docstring'''

   for prediction_type in ["epsilon", "sample"]:
    self.check_over_configs(prediction_type=__UpperCAmelCase     )






  def  UpperCAmelCase__		(				self					:	Dict     )->Dict:






   '''simple docstring'''

   for time_step in [0, 500, 999]:
    for prev_timestep in [None, 5, 100, 250, 500, 750]:
     if prev_timestep is not None and prev_timestep >= time_step:
      continue

     self.check_over_forward(time_step=__UpperCAmelCase  ,    prev_timestep=__UpperCAmelCase     )






  def  UpperCAmelCase__		(				self					:	List[Any]     )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase	:			Tuple         =  self.scheduler_classes[0]
   __lowerCAmelCase	:			Optional[int]         =  self.get_scheduler_config(variance_type="""fixed_small_log"""     )
   __lowerCAmelCase	:			List[str]         =  scheduler_class(**__UpperCAmelCase     )

   assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0000E-10     )     ) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.0_549_625     )     ) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.9_994_987     )     ) < 1E-5






  def  UpperCAmelCase__		(				self					:	List[str]     )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase	:			List[Any]         =  self.scheduler_classes[0]
   __lowerCAmelCase	:			str         =  self.get_scheduler_config(variance_type="""learned_range"""     )
   __lowerCAmelCase	:			Any         =  scheduler_class(**__UpperCAmelCase     )

   __lowerCAmelCase	:			Union[str, Any]         =  0.5

   assert scheduler._get_variance(1  ,    predicted_variance=__UpperCAmelCase     ) - -10.1_712_790 < 1E-5
   assert scheduler._get_variance(487  ,    predicted_variance=__UpperCAmelCase     ) - -5.7_998_052 < 1E-5
   assert scheduler._get_variance(999  ,    predicted_variance=__UpperCAmelCase     ) - -0.0_010_011 < 1E-5






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






   '''simple docstring'''

   __lowerCAmelCase	:			Tuple         =  self.scheduler_classes[0]
   __lowerCAmelCase	:			Union[str, Any]         =  self.get_scheduler_config()
   __lowerCAmelCase	:			Optional[Any]         =  scheduler_class(**__UpperCAmelCase     )

   __lowerCAmelCase	:			List[str]         =  scheduler.timesteps

   __lowerCAmelCase	:			Union[str, Any]         =  self.dummy_model()
   __lowerCAmelCase	:			Union[str, Any]         =  self.dummy_sample_deter
   __lowerCAmelCase	:			Union[str, Any]         =  torch.manual_seed(0     )

   for i, t in enumerate(__UpperCAmelCase     ):
    # 1. predict noise residual
    __lowerCAmelCase	:			Optional[Any]         =  model(__UpperCAmelCase  ,    __UpperCAmelCase     )

    # 2. predict previous mean of sample x_t-1
    __lowerCAmelCase	:			Tuple         =  scheduler.step(__UpperCAmelCase  ,    __UpperCAmelCase  ,    __UpperCAmelCase  ,    generator=__UpperCAmelCase     ).prev_sample

    __lowerCAmelCase	:			str         =  pred_prev_sample

   __lowerCAmelCase	:			List[str]         =  torch.sum(torch.abs(__UpperCAmelCase     )     )
   __lowerCAmelCase	:			int         =  torch.mean(torch.abs(__UpperCAmelCase     )     )

   assert abs(result_sum.item() - 252.2_682_495     ) < 1E-2
   assert abs(result_mean.item() - 0.3_284_743     ) < 1E-3






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






   '''simple docstring'''

   __lowerCAmelCase	:			str         =  self.scheduler_classes[0]
   __lowerCAmelCase	:			Dict         =  self.get_scheduler_config()
   __lowerCAmelCase	:			Union[str, Any]         =  scheduler_class(**__UpperCAmelCase     )

   scheduler.set_timesteps(25     )

   __lowerCAmelCase	:			str         =  scheduler.timesteps

   __lowerCAmelCase	:			List[str]         =  self.dummy_model()
   __lowerCAmelCase	:			Optional[Any]         =  self.dummy_sample_deter
   __lowerCAmelCase	:			Dict         =  torch.manual_seed(0     )

   for i, t in enumerate(__UpperCAmelCase     ):
    # 1. predict noise residual
    __lowerCAmelCase	:			Optional[Any]         =  model(__UpperCAmelCase  ,    __UpperCAmelCase     )

    if i + 1 == timesteps.shape[0]:
     __lowerCAmelCase	:			Tuple         =  None
    else:
     __lowerCAmelCase	:			str         =  timesteps[i + 1]

    # 2. predict previous mean of sample x_t-1
    __lowerCAmelCase	:			Any         =  scheduler.step(
        __UpperCAmelCase  ,    __UpperCAmelCase  ,    __UpperCAmelCase  ,    prev_timestep=__UpperCAmelCase  ,    generator=__UpperCAmelCase     ).prev_sample

    __lowerCAmelCase	:			Union[str, Any]         =  pred_prev_sample

   __lowerCAmelCase	:			str         =  torch.sum(torch.abs(__UpperCAmelCase     )     )
   __lowerCAmelCase	:			Any         =  torch.mean(torch.abs(__UpperCAmelCase     )     )

   assert abs(result_sum.item() - 258.2_044_983     ) < 1E-2
   assert abs(result_mean.item() - 0.3_362_038     ) < 1E-3






  def  UpperCAmelCase__		(				self					:	Union[str, Any]     )->Dict:






   '''simple docstring'''

   pass






  def  UpperCAmelCase__		(				self					:	Optional[Any]     )->int:






   '''simple docstring'''

   pass


"""simple docstring"""




import numpy as np

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return (2 / (1 + np.exp(-2 * vector     ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()


