import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[Any]      =			(UnCLIPScheduler,)


   def 			lowercase_      (self     :						Union[str, Any]       ,  **__UpperCAmelCase     :						Optional[Any]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  {
             "num_train_timesteps": 1_0_0_0,
             "variance_type": "fixed_small_log",
             "clip_sample": True,
             "clip_sample_range": 1.0,
             "prediction_type": "epsilon",
         }

         config.update(**__UpperCAmelCase		)
         return config


   def 			lowercase_      (self     :						Optional[int]		)						->				Tuple:
         """simple docstring"""



         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase		)


   def 			lowercase_      (self     :						Optional[Any]		)						->				Any:
         """simple docstring"""



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


   def 			lowercase_      (self     :						int		)						->				Union[str, Any]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         for clip_sample_range in [1, 5, 1_0, 2_0]:
               self.check_over_configs(clip_sample_range=__UpperCAmelCase		)


   def 			lowercase_      (self     :						List[str]		)						->				Dict:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Dict		)						->				List[Any]:
         """simple docstring"""



         for time_step in [0, 5_0_0, 9_9_9]:
               for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                     if prev_timestep is not None and prev_timestep >= time_step:
                           continue

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


   def 			lowercase_      (self     :						Dict		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.scheduler_classes[0]
         UpperCAmelCase__								=  self.get_scheduler_config(variance_type="fixed_small_log"		)
         UpperCAmelCase__								=  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(4_8_7		) - 0.0549625		)		) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.9994987		)		) < 1E-5


   def 			lowercase_      (self     :						Dict		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  self.scheduler_classes[0]
         UpperCAmelCase__								=  self.get_scheduler_config(variance_type="learned_range"		)
         UpperCAmelCase__								=  scheduler_class(**__UpperCAmelCase		)

         UpperCAmelCase__								=  0.5

         assert scheduler._get_variance(1       ,  predicted_variance=__UpperCAmelCase		) - -10.1712790 < 1E-5
         assert scheduler._get_variance(4_8_7       ,  predicted_variance=__UpperCAmelCase		) - -5.7998052 < 1E-5
         assert scheduler._get_variance(9_9_9       ,  predicted_variance=__UpperCAmelCase		) - -0.0010011 < 1E-5


   def 			lowercase_      (self     :						Optional[int]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.scheduler_classes[0]
         UpperCAmelCase__								=  self.get_scheduler_config()
         UpperCAmelCase__								=  scheduler_class(**__UpperCAmelCase		)

         UpperCAmelCase__								=  scheduler.timesteps

         UpperCAmelCase__								=  self.dummy_model()
         UpperCAmelCase__								=  self.dummy_sample_deter
         UpperCAmelCase__								=  torch.manual_seed(0		)

         for i, t in enumerate(__UpperCAmelCase		):
               # 1. predict noise residual
               UpperCAmelCase__								=  model(__UpperCAmelCase       ,  __UpperCAmelCase		)

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

               UpperCAmelCase__								=  pred_prev_sample

         UpperCAmelCase__								=  torch.sum(torch.abs(__UpperCAmelCase		)		)
         UpperCAmelCase__								=  torch.mean(torch.abs(__UpperCAmelCase		)		)

         assert abs(result_sum.item() - 252.2682495		) < 1E-2
         assert abs(result_mean.item() - 0.3284743		) < 1E-3


   def 			lowercase_      (self     :						Any		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.scheduler_classes[0]
         UpperCAmelCase__								=  self.get_scheduler_config()
         UpperCAmelCase__								=  scheduler_class(**__UpperCAmelCase		)

         scheduler.set_timesteps(2_5		)

         UpperCAmelCase__								=  scheduler.timesteps

         UpperCAmelCase__								=  self.dummy_model()
         UpperCAmelCase__								=  self.dummy_sample_deter
         UpperCAmelCase__								=  torch.manual_seed(0		)

         for i, t in enumerate(__UpperCAmelCase		):
               # 1. predict noise residual
               UpperCAmelCase__								=  model(__UpperCAmelCase       ,  __UpperCAmelCase		)

               if i + 1 == timesteps.shape[0]:
                     UpperCAmelCase__								=  None
               else:
                     UpperCAmelCase__								=  timesteps[i + 1]

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

               UpperCAmelCase__								=  pred_prev_sample

         UpperCAmelCase__								=  torch.sum(torch.abs(__UpperCAmelCase		)		)
         UpperCAmelCase__								=  torch.mean(torch.abs(__UpperCAmelCase		)		)

         assert abs(result_sum.item() - 258.2044983		) < 1E-2
         assert abs(result_mean.item() - 0.3362038		) < 1E-3


   def 			lowercase_      (self     :						Dict		)						->				Optional[int]:
         """simple docstring"""



         pass





   def 			lowercase_      (self     :						List[Any]		)						->				List[str]:
         """simple docstring"""



         pass







'''simple docstring'''

import math





def __snake_case					(      UpperCAmelCase_						:						float					,				UpperCAmelCase_						:						float		):
 return math.pow(UpperCAmelCase_					,				2		) - a





def __snake_case					(      UpperCAmelCase_						:						float		):
 return 2 * x





def __snake_case					(      UpperCAmelCase_						:						float		):
 lowerCamelCase_      =  2.0

 while start <= a:
  lowerCamelCase_      =  math.pow(UpperCAmelCase_					,				2		)

 return start





def __snake_case					(      UpperCAmelCase_						:						float					,				UpperCAmelCase_						:						int = 9999					,				UpperCAmelCase_						:						float = 0.00_0000_0000_0001		):

 if a < 0:
  raise ValueError("math domain error"		)

 lowerCamelCase_      =  get_initial_point(UpperCAmelCase_		)

 for _ in range(UpperCAmelCase_		):
  lowerCamelCase_      =  value
  lowerCamelCase_      =  value - fx(UpperCAmelCase_					,				UpperCAmelCase_		) / fx_derivative(UpperCAmelCase_		)
  if abs(prev_value - value		) < tolerance:
   return value

 return value


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

     testmod()



