






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   List[str]        =			(KDPMaDiscreteScheduler,)
				_snake_case				:   List[Any]        =			10







				def 				__a   ( self     :List[str]  ,     **lowerCamelCase__     :Union[str, Any]     ):
					UpperCamelCase__ :int						=      {
					    """num_train_timesteps""": 11_00,
					    """beta_start""": 0.0001,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**lowerCamelCase__     )
					return config







				def 				__a   ( self     :Any     ):
					for timesteps in [10, 50, 1_00, 10_00]:
						self.check_over_configs(num_train_timesteps=lowerCamelCase__     )







				def 				__a   ( self     :List[str]     ):
					for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]  ,     [0.0002, 0.002, 0.02]     ):
						self.check_over_configs(beta_start=lowerCamelCase__  ,     beta_end=lowerCamelCase__     )







				def 				__a   ( self     :Tuple     ):
					for schedule in ["linear", "scaled_linear"]:
						self.check_over_configs(beta_schedule=lowerCamelCase__     )







				def 				__a   ( self     :List[str]     ):
					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=lowerCamelCase__     )







				def 				__a   ( self     :int     ):
					UpperCamelCase__ :Optional[int]						=      self.scheduler_classes[0]
					UpperCamelCase__ :Optional[Any]						=      self.get_scheduler_config(prediction_type="""v_prediction"""     )
					UpperCamelCase__ :int						=      scheduler_class(**lowerCamelCase__     )

					scheduler.set_timesteps(self.num_inference_steps     )

					UpperCamelCase__ :Optional[Any]						=      self.dummy_model()
					UpperCamelCase__ :List[Any]						=      self.dummy_sample_deter * scheduler.init_noise_sigma
					UpperCamelCase__ :Dict						=      sample.to(lowerCamelCase__     )

					for i, t in enumerate(scheduler.timesteps     ):
						UpperCamelCase__ :Dict						=      scheduler.scale_model_input(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :Dict						=      model(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :int						=      scheduler.step(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )
						UpperCamelCase__ :int						=      output.prev_sample

					UpperCamelCase__ :Dict						=      torch.sum(torch.abs(lowerCamelCase__     )     )
					UpperCamelCase__ :Tuple						=      torch.mean(torch.abs(lowerCamelCase__     )     )

					if torch_device in ["cpu", "mps"]:
						assert abs(result_sum.item() - 4.6934e-07     ) < 1e-2
						assert abs(result_mean.item() - 6.1112e-10     ) < 1e-3
					else:
						# CUDA
						assert abs(result_sum.item() - 4.693_4286_5017_0972e-07     ) < 1e-2
						assert abs(result_mean.item() - 0.0002     ) < 1e-3







				def 				__a   ( self     :Optional[int]     ):
					if torch_device == "mps":
						return
					UpperCamelCase__ :Optional[int]						=      self.scheduler_classes[0]
					UpperCamelCase__ :Union[str, Any]						=      self.get_scheduler_config()
					UpperCamelCase__ :List[Any]						=      scheduler_class(**lowerCamelCase__     )

					scheduler.set_timesteps(self.num_inference_steps     )

					UpperCamelCase__ :Any						=      self.dummy_model()
					UpperCamelCase__ :Tuple						=      self.dummy_sample_deter * scheduler.init_noise_sigma
					UpperCamelCase__ :Tuple						=      sample.to(lowerCamelCase__     )

					for i, t in enumerate(scheduler.timesteps     ):
						UpperCamelCase__ :List[Any]						=      scheduler.scale_model_input(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :str						=      model(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :Union[str, Any]						=      scheduler.step(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )
						UpperCamelCase__ :Union[str, Any]						=      output.prev_sample

					UpperCamelCase__ :Tuple						=      torch.sum(torch.abs(lowerCamelCase__     )     )
					UpperCamelCase__ :Any						=      torch.mean(torch.abs(lowerCamelCase__     )     )

					if torch_device in ["cpu", "mps"]:
						assert abs(result_sum.item() - 20.4125     ) < 1e-2
						assert abs(result_mean.item() - 0.0266     ) < 1e-3
					else:
						# CUDA
						assert abs(result_sum.item() - 20.4125     ) < 1e-2
						assert abs(result_mean.item() - 0.0266     ) < 1e-3







				def 				__a   ( self     :str     ):
					if torch_device == "mps":
						return
					UpperCamelCase__ :str						=      self.scheduler_classes[0]
					UpperCamelCase__ :int						=      self.get_scheduler_config()
					UpperCamelCase__ :List[str]						=      scheduler_class(**lowerCamelCase__     )

					scheduler.set_timesteps(self.num_inference_steps  ,     device=lowerCamelCase__     )

					UpperCamelCase__ :Optional[int]						=      self.dummy_model()
					UpperCamelCase__ :Optional[int]						=      self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
						UpperCamelCase__ :Union[str, Any]						=      scheduler.scale_model_input(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :List[Any]						=      model(lowerCamelCase__  ,     lowerCamelCase__     )

						UpperCamelCase__ :Union[str, Any]						=      scheduler.step(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )
						UpperCamelCase__ :int						=      output.prev_sample

					UpperCamelCase__ :str						=      torch.sum(torch.abs(lowerCamelCase__     )     )
					UpperCamelCase__ :List[str]						=      torch.mean(torch.abs(lowerCamelCase__     )     )

					if str(lowerCamelCase__     ).startswith("""cpu"""     ):
						# The following sum varies between 148 and 156 on mps. Why?
						assert abs(result_sum.item() - 20.4125     ) < 1e-2
						assert abs(result_mean.item() - 0.0266     ) < 1e-3
					else:
						# CUDA
						assert abs(result_sum.item() - 20.4125     ) < 1e-2
						assert abs(result_mean.item() - 0.0266     ) < 1e-3

import random






class     UpperCAmelCase						:



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   str					)						->      tuple[list[int], list[int]]:


          '''simple docstring'''


          snake_case			:  int     							= [ord(snake_case__					) for i in text]
          snake_case			:  Optional[int]     							= []
          snake_case			:  int     							= []
          for i in plain:
                 snake_case			:  List[Any]     							= random.randint(1       ,		3_00					)
                 snake_case			:  List[Any]     							= (i + k) * k
                 cipher.append(snake_case__					)
                 key.append(snake_case__					)
          return cipher, key



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   list[int]       ,		snake_case__						:   list[int]					)						->      str:


          '''simple docstring'''


          snake_case			:  int     							= []
          for i in range(len(snake_case__					)					):
                 snake_case			:  List[str]     							= int((cipher[i] - (key[i]) ** 2) / key[i]					)
                 plain.append(chr(snake_case__					)					)
          return "".join(snake_case__					)


if __name__ == "__main__":
   __lowerCamelCase,  __lowerCamelCase				=			Onepad().encrypt("""Hello""")
   print(c, k)
   print(Onepad().decrypt(c, k))





