
'''simple docstring'''





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						Optional[Any]				=       (DPMSolverMultistepScheduler,)
 lowercase_				:						Any				=       (("num_inference_steps", 25),)







 def        UpperCamelCase     (				self		:     Union[str, Any] ,							**snake_case__		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       {
       "num_train_timesteps": 10_00,
       "beta_start": 0.0001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "solver_order": 2,
       "prediction_type": "epsilon",
       "thresholding": False,
       "sample_max_value": 1.0,
       "algorithm_type": "dpmsolver++",
       "solver_type": "midpoint",
       "lower_order_final": False,
       "lambda_min_clipped": -float("inf"   ),
       "variance_type": None,
   }

   config.update(**snake_case__   )
   return config







 def        UpperCamelCase     (				self		:     Tuple ,							snake_case__		:     Union[str, Any]=0 ,							**snake_case__		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       dict(self.forward_default_kwargs   )
   UpperCAmelCase__			:				List[str]												=       kwargs.pop("num_inference_steps" ,							snake_case__   )
   UpperCAmelCase__			:				List[str]												=       self.dummy_sample
   UpperCAmelCase__			:				Any												=       0.1 * sample
   UpperCAmelCase__			:				Optional[int]												=       [residual + 0.2, residual + 0.15, residual + 0.10]

   for scheduler_class in self.scheduler_classes:
     UpperCAmelCase__			:				Any												=       self.get_scheduler_config(**snake_case__   )
     UpperCAmelCase__			:				Optional[int]												=       scheduler_class(**snake_case__   )
     scheduler.set_timesteps(snake_case__   )
     # copy over dummy past residuals
     UpperCAmelCase__			:				List[Any]												=       dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(snake_case__   )
       UpperCAmelCase__			:				Dict												=       scheduler_class.from_pretrained(snake_case__   )
       new_scheduler.set_timesteps(snake_case__   )
       # copy over dummy past residuals
       UpperCAmelCase__			:				Dict												=       dummy_past_residuals[: new_scheduler.config.solver_order]

     UpperCAmelCase__					, UpperCAmelCase__			:				Any												=       sample, sample
     for t in range(snake_case__ ,							time_step + scheduler.config.solver_order + 1   ):
       UpperCAmelCase__			:				str												=       scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__ ,							**snake_case__   ).prev_sample
       UpperCAmelCase__			:				Dict												=       new_scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__ ,							**snake_case__   ).prev_sample

       assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     Tuple ,							snake_case__		:     Dict=0 ,							**snake_case__		:     str   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       dict(self.forward_default_kwargs   )
   UpperCAmelCase__			:				int												=       kwargs.pop("num_inference_steps" ,							snake_case__   )
   UpperCAmelCase__			:				Union[str, Any]												=       self.dummy_sample
   UpperCAmelCase__			:				int												=       0.1 * sample
   UpperCAmelCase__			:				Tuple												=       [residual + 0.2, residual + 0.15, residual + 0.10]

   for scheduler_class in self.scheduler_classes:
     UpperCAmelCase__			:				Tuple												=       self.get_scheduler_config()
     UpperCAmelCase__			:				Any												=       scheduler_class(**snake_case__   )
     scheduler.set_timesteps(snake_case__   )

     # copy over dummy past residuals (must be after setting timesteps)
     UpperCAmelCase__			:				Tuple												=       dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(snake_case__   )
       UpperCAmelCase__			:				Tuple												=       scheduler_class.from_pretrained(snake_case__   )
       # copy over dummy past residuals
       new_scheduler.set_timesteps(snake_case__   )

       # copy over dummy past residual (must be after setting timesteps)
       UpperCAmelCase__			:				Tuple												=       dummy_past_residuals[: new_scheduler.config.solver_order]

     UpperCAmelCase__			:				Optional[int]												=       scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__ ,							**snake_case__   ).prev_sample
     UpperCAmelCase__			:				Optional[Any]												=       new_scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__ ,							**snake_case__   ).prev_sample

     assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"







 def        UpperCamelCase     (				self		:     int ,							snake_case__		:     Optional[Any]=None ,							**snake_case__		:     Union[str, Any]   ):



   '''simple docstring'''




   if scheduler is None:
     UpperCAmelCase__			:				str												=       self.scheduler_classes[0]
     UpperCAmelCase__			:				Tuple												=       self.get_scheduler_config(**snake_case__   )
     UpperCAmelCase__			:				List[Any]												=       scheduler_class(**snake_case__   )

   UpperCAmelCase__			:				Dict												=       10
   UpperCAmelCase__			:				Tuple												=       self.dummy_model()
   UpperCAmelCase__			:				Union[str, Any]												=       self.dummy_sample_deter
   scheduler.set_timesteps(snake_case__   )

   for i, t in enumerate(scheduler.timesteps   ):
     UpperCAmelCase__			:				Tuple												=       model(snake_case__ ,							snake_case__   )
     UpperCAmelCase__			:				Optional[Any]												=       scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__   ).prev_sample

   return sample







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       dict(self.forward_default_kwargs   )

   UpperCAmelCase__			:				Tuple												=       kwargs.pop("num_inference_steps" ,							snake_case__   )

   for scheduler_class in self.scheduler_classes:
     UpperCAmelCase__			:				Union[str, Any]												=       self.get_scheduler_config()
     UpperCAmelCase__			:				List[str]												=       scheduler_class(**snake_case__   )

     UpperCAmelCase__			:				List[Any]												=       self.dummy_sample
     UpperCAmelCase__			:				str												=       0.1 * sample

     if num_inference_steps is not None and hasattr(snake_case__ ,							"set_timesteps"   ):
       scheduler.set_timesteps(snake_case__   )
     elif num_inference_steps is not None and not hasattr(snake_case__ ,							"set_timesteps"   ):
       UpperCAmelCase__			:				str												=       num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     UpperCAmelCase__			:				str												=       [residual + 0.2, residual + 0.15, residual + 0.10]
     UpperCAmelCase__			:				Tuple												=       dummy_past_residuals[: scheduler.config.solver_order]

     UpperCAmelCase__			:				int												=       scheduler.timesteps[5]
     UpperCAmelCase__			:				List[Any]												=       scheduler.timesteps[6]

     UpperCAmelCase__			:				Dict												=       scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__ ,							**snake_case__   ).prev_sample
     UpperCAmelCase__			:				Optional[Any]												=       scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__ ,							**snake_case__   ).prev_sample

     self.assertEqual(output_a.shape ,							sample.shape   )
     self.assertEqual(output_a.shape ,							output_a.shape   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   for timesteps in [25, 50, 1_00, 9_99, 10_00]:
     self.check_over_configs(num_train_timesteps=snake_case__   )







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   self.check_over_configs(thresholding=snake_case__   )
   for order in [1, 2, 3]:
     for solver_type in ["midpoint", "heun"]:
       for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(
               thresholding=snake_case__ ,							prediction_type=snake_case__ ,							sample_max_value=snake_case__ ,							algorithm_type="dpmsolver++" ,							solver_order=snake_case__ ,							solver_type=snake_case__ ,							)







 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=snake_case__   )







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
     for solver_type in ["midpoint", "heun"]:
       for order in [1, 2, 3]:
         for prediction_type in ["epsilon", "sample"]:
           if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
             if order == 3:
               continue
           else:
             self.check_over_configs(
                 solver_order=snake_case__ ,							solver_type=snake_case__ ,							prediction_type=snake_case__ ,							algorithm_type=snake_case__ ,							)
           UpperCAmelCase__			:				Tuple												=       self.full_loop(
               solver_order=snake_case__ ,							solver_type=snake_case__ ,							prediction_type=snake_case__ ,							algorithm_type=snake_case__ ,							)
           assert not torch.isnan(snake_case__   ).any(), "Samples have nan numbers"







 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   self.check_over_configs(lower_order_final=snake_case__   )
   self.check_over_configs(lower_order_final=snake_case__   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   self.check_over_configs(lambda_min_clipped=-float("inf"   )   )
   self.check_over_configs(lambda_min_clipped=-5.1   )







 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   self.check_over_configs(variance_type=snake_case__   )
   self.check_over_configs(variance_type="learned_range"   )







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
     self.check_over_forward(num_inference_steps=snake_case__ ,							time_step=0   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       self.full_loop()
   UpperCAmelCase__			:				Dict												=       torch.mean(torch.abs(snake_case__   )   )

   assert abs(result_mean.item() - 0.3301   ) < 1e-3







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.full_loop(thresholding=snake_case__ ,							dynamic_thresholding_ratio=0.87 ,							sample_max_value=0.5   )
   UpperCAmelCase__			:				List[str]												=       torch.mean(torch.abs(snake_case__   )   )

   assert abs(result_mean.item() - 1.1364   ) < 1e-3







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       self.full_loop(prediction_type="v_prediction"   )
   UpperCAmelCase__			:				List[Any]												=       torch.mean(torch.abs(snake_case__   )   )

   assert abs(result_mean.item() - 0.2251   ) < 1e-3







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[Any]												=       self.full_loop(prediction_type="v_prediction" ,							use_karras_sigmas=snake_case__   )
   UpperCAmelCase__			:				int												=       torch.mean(torch.abs(snake_case__   )   )

   assert abs(result_mean.item() - 0.2096   ) < 1e-3







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
   UpperCAmelCase__			:				Tuple												=       self.full_loop(scheduler=snake_case__   )
   UpperCAmelCase__			:				Dict												=       torch.mean(torch.abs(snake_case__   )   )

   assert abs(result_mean.item() - 0.3301   ) < 1e-3

   UpperCAmelCase__			:				Optional[int]												=       DPMSolverSinglestepScheduler.from_config(scheduler.config   )
   UpperCAmelCase__			:				List[str]												=       UniPCMultistepScheduler.from_config(scheduler.config   )
   UpperCAmelCase__			:				Optional[int]												=       DEISMultistepScheduler.from_config(scheduler.config   )
   UpperCAmelCase__			:				Any												=       DPMSolverMultistepScheduler.from_config(scheduler.config   )

   UpperCAmelCase__			:				List[str]												=       self.full_loop(scheduler=snake_case__   )
   UpperCAmelCase__			:				Tuple												=       torch.mean(torch.abs(snake_case__   )   )

   assert abs(result_mean.item() - 0.3301   ) < 1e-3







 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       self.scheduler_classes[0]
   UpperCAmelCase__			:				Optional[int]												=       self.get_scheduler_config(thresholding=snake_case__ ,							dynamic_thresholding_ratio=0   )
   UpperCAmelCase__			:				Any												=       scheduler_class(**snake_case__   )

   UpperCAmelCase__			:				int												=       10
   UpperCAmelCase__			:				Optional[Any]												=       self.dummy_model()
   UpperCAmelCase__			:				Dict												=       self.dummy_sample_deter.half()
   scheduler.set_timesteps(snake_case__   )

   for i, t in enumerate(scheduler.timesteps   ):
     UpperCAmelCase__			:				Union[str, Any]												=       model(snake_case__ ,							snake_case__   )
     UpperCAmelCase__			:				Tuple												=       scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__   ).prev_sample

   assert sample.dtype == torch.floataa







 def        UpperCamelCase     (				self		:     Optional[int] ,							**snake_case__		:     int   ):



   '''simple docstring'''




   for scheduler_class in self.scheduler_classes:
     UpperCAmelCase__			:				Tuple												=       self.get_scheduler_config(**snake_case__   )
     UpperCAmelCase__			:				Any												=       scheduler_class(**snake_case__   )

     scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
     assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps





'''simple docstring'''





def snake_case_    (				lowercase__      ,					lowercase__ = 1_3		):
  UpperCAmelCase__			:				Optional[Any]												=       ""
  for c in s:
    if "A" <= c <= "Z":
      out += chr(ord("A"		) + (ord(lowercase__		) - ord("A"		) + n) % 2_6		)
    elif "a" <= c <= "z":
      out += chr(ord("a"		) + (ord(lowercase__		) - ord("a"		) + n) % 2_6		)
    else:
      out += c
  return out







def snake_case_    (				):
  UpperCAmelCase__			:				Dict												=       input("Enter message: "		)

  UpperCAmelCase__			:				Optional[int]												=       dencrypt(lowercase__      ,					1_3		)
  print("Encryption:"      ,					lowercase__		)

  UpperCAmelCase__			:				Union[str, Any]												=       dencrypt(lowercase__      ,					1_3		)
  print("Decryption: "      ,					lowercase__		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



