



"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  (UnCLIPScheduler,)



       def   lowercase__       (							self    :						Any	,       **_UpperCAmelCase    :						List[str]					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							{
                 "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   lowercase__       (							self    :						Optional[Any]					)				->		Any:


             '''simple docstring'''




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



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


             '''simple docstring'''




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



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


             '''simple docstring'''




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



       def   lowercase__       (							self    :						int					)				->		Optional[int]:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




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



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


             '''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   lowercase__       (							self    :						int					)				->		Optional[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(487					) - 0.054_9625					)					) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.999_4987					)					) < 1e-5



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


             '''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					) - -10.171_2790 < 1e-5
             assert scheduler._get_variance(487	,       predicted_variance=_UpperCAmelCase					) - -5.799_8052 < 1e-5
             assert scheduler._get_variance(999	,       predicted_variance=_UpperCAmelCase					) - -0.001_0011 < 1e-5



       def   lowercase__       (							self    :						List[Any]					)				->		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() - 252.268_2495					) < 1e-2
             assert abs(result_mean.item() - 0.328_4743					) < 1e-3



       def   lowercase__       (							self    :						int					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.scheduler_classes[0]
             UpperCAmelCase_    				=							self.get_scheduler_config()
             UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)

             scheduler.set_timesteps(25					)

             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() - 258.204_4983					) < 1e-2
             assert abs(result_mean.item() - 0.336_2038					) < 1e-3



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


             '''simple docstring'''




             pass







       def   lowercase__       (							self    :						int					)				->		Dict:


             '''simple docstring'''




             pass









"""simple docstring"""





import string
def      a__			(   lowerCAmelCase__   ):
      for key in range(len(string.ascii_uppercase   )   ):
            UpperCAmelCase_    				=							""
            for symbol in message:
                  if symbol in string.ascii_uppercase:
                        UpperCAmelCase_    				=							string.ascii_uppercase.find(lowerCAmelCase__   )
                        UpperCAmelCase_    				=							num - key
                        if num < 0:
                              UpperCAmelCase_    				=							num + len(string.ascii_uppercase   )
                        UpperCAmelCase_    				=							translated + string.ascii_uppercase[num]
                  else:
                        UpperCAmelCase_    				=							translated + symbol
            print(f"""Decryption using Key #{key}: {translated}"""   )
def      a__			(   ):
      UpperCAmelCase_    				=							input("Encrypted message: "   )
      UpperCAmelCase_    				=							message.upper()
      decrypt(lowerCAmelCase__   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




