

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					(UnCLIPScheduler,)



   def 	__magic_name__   (							self							:					Optional[int],		**__A							:					Union[str, Any]						):
     UpperCAmelCase	:							int						=			{
         '''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(**__A						)
     return config



   def 	__magic_name__   (							self							:					Tuple						):
     for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__A						)



   def 	__magic_name__   (							self							:					List[Any]						):
     for variance in ["fixed_small_log", "learned_range"]:
       self.check_over_configs(variance_type=__A						)



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



   def 	__magic_name__   (							self							:					Dict						):
     for clip_sample_range in [1, 5, 1_0, 2_0]:
       self.check_over_configs(clip_sample_range=__A						)



   def 	__magic_name__   (							self							:					int						):
     for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(prediction_type=__A						)



   def 	__magic_name__   (							self							:					int						):
     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=__A,		prev_timestep=__A						)



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							List[Any]						=			self.scheduler_classes[0]
     UpperCAmelCase	:							List[Any]						=			self.get_scheduler_config(variance_type='''fixed_small_log'''						)
     UpperCAmelCase	:							List[Any]						=			scheduler_class(**__A						)

     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						) - 0.0_5_4_9_6_2_5						)						) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.9_9_9_4_9_8_7						)						) < 1E-5



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Union[str, Any]						=			self.scheduler_classes[0]
     UpperCAmelCase	:							List[Any]						=			self.get_scheduler_config(variance_type='''learned_range'''						)
     UpperCAmelCase	:							str						=			scheduler_class(**__A						)

     UpperCAmelCase	:							int						=			0.5

     assert scheduler._get_variance(1,		predicted_variance=__A						) - -1_0.1_7_1_2_7_9_0 < 1E-5
     assert scheduler._get_variance(4_8_7,		predicted_variance=__A						) - -5.7_9_9_8_0_5_2 < 1E-5
     assert scheduler._get_variance(9_9_9,		predicted_variance=__A						) - -0.0_0_1_0_0_1_1 < 1E-5



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							str						=			self.scheduler_classes[0]
     UpperCAmelCase	:							List[Any]						=			self.get_scheduler_config()
     UpperCAmelCase	:							Optional[int]						=			scheduler_class(**__A						)

     UpperCAmelCase	:							int						=			scheduler.timesteps

     UpperCAmelCase	:							Tuple						=			self.dummy_model()
     UpperCAmelCase	:							int						=			self.dummy_sample_deter
     UpperCAmelCase	:							Optional[Any]						=			torch.manual_seed(0						)

     for i, t in enumerate(__A						):
       # 1. predict noise residual
       UpperCAmelCase	:							Optional[int]						=			model(__A,		__A						)

       # 2. predict previous mean of sample x_t-1
       UpperCAmelCase	:							Optional[int]						=			scheduler.step(__A,		__A,		__A,		generator=__A						).prev_sample

       UpperCAmelCase	:							List[Any]						=			pred_prev_sample

     UpperCAmelCase	:							int						=			torch.sum(torch.abs(__A						)						)
     UpperCAmelCase	:							Tuple						=			torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5						) < 1E-2
     assert abs(result_mean.item() - 0.3_2_8_4_7_4_3						) < 1E-3



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							int						=			self.scheduler_classes[0]
     UpperCAmelCase	:							Tuple						=			self.get_scheduler_config()
     UpperCAmelCase	:							Union[str, Any]						=			scheduler_class(**__A						)

     scheduler.set_timesteps(2_5						)

     UpperCAmelCase	:							Dict						=			scheduler.timesteps

     UpperCAmelCase	:							int						=			self.dummy_model()
     UpperCAmelCase	:							Optional[int]						=			self.dummy_sample_deter
     UpperCAmelCase	:							Any						=			torch.manual_seed(0						)

     for i, t in enumerate(__A						):
       # 1. predict noise residual
       UpperCAmelCase	:							str						=			model(__A,		__A						)

       if i + 1 == timesteps.shape[0]:
         UpperCAmelCase	:							Optional[int]						=			None
       else:
         UpperCAmelCase	:							List[str]						=			timesteps[i + 1]

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

       UpperCAmelCase	:							List[Any]						=			pred_prev_sample

     UpperCAmelCase	:							Tuple						=			torch.sum(torch.abs(__A						)						)
     UpperCAmelCase	:							Optional[int]						=			torch.mean(torch.abs(__A						)						)

     assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3						) < 1E-2
     assert abs(result_mean.item() - 0.3_3_6_2_0_3_8						) < 1E-3



   def 	__magic_name__   (							self							:					List[str]						):
     pass





   def 	__magic_name__   (							self							:					List[Any]						):
     pass








def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      int      )  ->						str:
  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''      )
  UpperCAmelCase	:							List[Any]						=			str(bin(UpperCAmelCase      )      )[2:]  # remove the leading "0b"
  UpperCAmelCase	:							List[str]						=			str(bin(UpperCAmelCase      )      )[2:]
  UpperCAmelCase	:							Optional[Any]						=			max(len(UpperCAmelCase      )    ,			len(UpperCAmelCase      )      )
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)      )      )
      for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase      )    ,			b_binary.zfill(UpperCAmelCase      )      )      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





