


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      int     		=		(UnCLIPScheduler,)






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				{
         "num_train_timesteps": 1000,
         "variance_type": "fixed_small_log",
         "clip_sample": True,
         "clip_sample_range": 1.0,
         "prediction_type": "epsilon",
     }

     config.update(**__A )
     return config






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """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=__A						,				prev_timestep=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				self.scheduler_classes[0]
     lowerCamelCase :					Tuple    =				self.get_scheduler_config(variance_type="fixed_small_log" )
     lowerCamelCase :					List[str]    =				scheduler_class(**__A )

     assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.00_00e-10 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0549625 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9994987 ) ) < 1e-5






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				self.scheduler_classes[0]
     lowerCamelCase :					int    =				self.get_scheduler_config(variance_type="learned_range" )
     lowerCamelCase :					Union[str, Any]    =				scheduler_class(**__A )

     lowerCamelCase :					Optional[int]    =				0.5

     assert scheduler._get_variance(1						,				predicted_variance=__A ) - -10.1712790 < 1e-5
     assert scheduler._get_variance(487						,				predicted_variance=__A ) - -5.7998052 < 1e-5
     assert scheduler._get_variance(999						,				predicted_variance=__A ) - -0.0010011 < 1e-5






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				self.scheduler_classes[0]
     lowerCamelCase :					List[str]    =				self.get_scheduler_config()
     lowerCamelCase :					Union[str, Any]    =				scheduler_class(**__A )

     lowerCamelCase :					Any    =				scheduler.timesteps

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

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

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

       lowerCamelCase :					List[Any]    =				pred_prev_sample

     lowerCamelCase :					str    =				torch.sum(torch.abs(__A ) )
     lowerCamelCase :					List[str]    =				torch.mean(torch.abs(__A ) )

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				self.scheduler_classes[0]
     lowerCamelCase :					int    =				self.get_scheduler_config()
     lowerCamelCase :					int    =				scheduler_class(**__A )

     scheduler.set_timesteps(25 )

     lowerCamelCase :					str    =				scheduler.timesteps

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

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

       if i + 1 == timesteps.shape[0]:
         lowerCamelCase :					Union[str, Any]    =				None
       else:
         lowerCamelCase :					Union[str, Any]    =				timesteps[i + 1]

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

       lowerCamelCase :					List[Any]    =				pred_prev_sample

     lowerCamelCase :					List[str]    =				torch.sum(torch.abs(__A ) )
     lowerCamelCase :					List[str]    =				torch.mean(torch.abs(__A ) )

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






   def _snake_case    (		self ):






     """simple docstring"""





     pass






   def _snake_case    (		self ):






     """simple docstring"""





     pass







def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for i in range(len(SCREAMING_SNAKE_CASE_	) - 1      ,						0      ,						-1	):
    lowerCamelCase :					Tuple    =				False

    for j in range(SCREAMING_SNAKE_CASE_      ,						0      ,						-1	):
      if unsorted[j] < unsorted[j - 1]:
        lowerCamelCase    ,	lowerCamelCase :					int    =				unsorted[j - 1], unsorted[j]
        lowerCamelCase :					Optional[int]    =				True

    for j in range(SCREAMING_SNAKE_CASE_	):
      if unsorted[j] > unsorted[j + 1]:
        lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				unsorted[j + 1], unsorted[j]
        lowerCamelCase :					Optional[Any]    =				True

    if not swapped:
      break
  return unsorted


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case             =     input('''Enter numbers separated by a comma:\n''').strip()
   _snake_case             =     [int(item) for item in user_input.split(''',''')]
   print(f'''{cocktail_shaker_sort(unsorted) = }''')



