





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[Any]			=			(DEISMultistepScheduler,)
     __snake_case    :	List[Any]			=			(("num_inference_steps", 25),)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]						,**lowerCamelCase__						:    List[str]       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{
               """num_train_timesteps""": 1000,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """solver_order""": 2,
           }

           config.update(**lowerCamelCase__       )
           return config






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    Optional[Any]=0						,**lowerCamelCase__						:    str       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						dict(self.forward_default_kwargs       )
           SCREAMING_SNAKE_CASE		=						kwargs.pop("""num_inference_steps"""						,lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						self.dummy_sample
           SCREAMING_SNAKE_CASE		=						0.1 * sample
           SCREAMING_SNAKE_CASE		=						[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(**lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__       )
                 scheduler.set_timesteps(lowerCamelCase__       )
                 # copy over dummy past residuals
                 SCREAMING_SNAKE_CASE		=						dummy_past_residuals[: scheduler.config.solver_order]

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

                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						sample, sample
                 for t in range(lowerCamelCase__						,time_step + scheduler.config.solver_order + 1       ):
                       SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__       ).prev_sample
                       SCREAMING_SNAKE_CASE		=						new_scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__       ).prev_sample

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           pass






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    List[Any]=0						,**lowerCamelCase__						:    Union[str, Any]       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						dict(self.forward_default_kwargs       )
           SCREAMING_SNAKE_CASE		=						kwargs.pop("""num_inference_steps"""						,lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						self.dummy_sample
           SCREAMING_SNAKE_CASE		=						0.1 * sample
           SCREAMING_SNAKE_CASE		=						[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE		=						self.get_scheduler_config()
                 SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__       )
                 scheduler.set_timesteps(lowerCamelCase__       )

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

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

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

                 SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__       ).prev_sample
                 SCREAMING_SNAKE_CASE		=						new_scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__       ).prev_sample

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]						,lowerCamelCase__						:    Dict=None						,**lowerCamelCase__						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


           if scheduler is None:
                 SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
                 SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(**lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						10
           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__       ).prev_sample

           return sample






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						dict(self.forward_default_kwargs       )

           SCREAMING_SNAKE_CASE		=						kwargs.pop("""num_inference_steps"""						,lowerCamelCase__       )

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE		=						self.get_scheduler_config()
                 SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__       )

                 SCREAMING_SNAKE_CASE		=						self.dummy_sample
                 SCREAMING_SNAKE_CASE		=						0.1 * sample

                 if num_inference_steps is not None and hasattr(lowerCamelCase__						,"""set_timesteps"""       ):
                       scheduler.set_timesteps(lowerCamelCase__       )
                 elif num_inference_steps is not None and not hasattr(lowerCamelCase__						,"""set_timesteps"""       ):
                       SCREAMING_SNAKE_CASE		=						num_inference_steps

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

                 SCREAMING_SNAKE_CASE		=						scheduler.timesteps[5]
                 SCREAMING_SNAKE_CASE		=						scheduler.timesteps[6]

                 SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__       ).prev_sample
                 SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__       ).prev_sample

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DEISMultistepScheduler(**self.get_scheduler_config()       )
           SCREAMING_SNAKE_CASE		=						self.full_loop(scheduler=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__       )       )

           assert abs(result_mean.item() - 0.23916       ) < 1e-3

           SCREAMING_SNAKE_CASE		=						DPMSolverSinglestepScheduler.from_config(scheduler.config       )
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepScheduler.from_config(scheduler.config       )
           SCREAMING_SNAKE_CASE		=						UniPCMultistepScheduler.from_config(scheduler.config       )
           SCREAMING_SNAKE_CASE		=						DEISMultistepScheduler.from_config(scheduler.config       )

           SCREAMING_SNAKE_CASE		=						self.full_loop(scheduler=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__       )       )

           assert abs(result_mean.item() - 0.23916       ) < 1e-3






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Dict:




           '''simple docstring'''


           for timesteps in [25, 50, 100, 999, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Dict:




           '''simple docstring'''


           self.check_over_configs(thresholding=lowerCamelCase__       )
           for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=lowerCamelCase__						,prediction_type=lowerCamelCase__						,sample_max_value=lowerCamelCase__						,algorithm_type="""deis"""						,solver_order=lowerCamelCase__						,solver_type=lowerCamelCase__						,)






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




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		Optional[Any]:




           '''simple docstring'''


           for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       solver_order=lowerCamelCase__						,solver_type=lowerCamelCase__						,prediction_type=lowerCamelCase__						,algorithm_type=lowerCamelCase__						,)
                                   SCREAMING_SNAKE_CASE		=						self.full_loop(
                                       solver_order=lowerCamelCase__						,solver_type=lowerCamelCase__						,prediction_type=lowerCamelCase__						,algorithm_type=lowerCamelCase__						,)
                                   assert not torch.isnan(lowerCamelCase__       ).any(), "Samples have nan numbers"






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		Dict:




           '''simple docstring'''


           self.check_over_configs(lower_order_final=lowerCamelCase__       )
           self.check_over_configs(lower_order_final=lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Any:




           '''simple docstring'''


           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                 self.check_over_forward(num_inference_steps=lowerCamelCase__						,time_step=0       )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop()
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__       )       )

           assert abs(result_mean.item() - 0.23916       ) < 1e-3






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.full_loop(prediction_type="""v_prediction"""       )
           SCREAMING_SNAKE_CASE		=						torch.mean(torch.abs(lowerCamelCase__       )       )

           assert abs(result_mean.item() - 0.091       ) < 1e-3






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE		=						self.get_scheduler_config(thresholding=lowerCamelCase__						,dynamic_thresholding_ratio=0       )
           SCREAMING_SNAKE_CASE		=						scheduler_class(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						10
           SCREAMING_SNAKE_CASE		=						self.dummy_model()
           SCREAMING_SNAKE_CASE		=						self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowerCamelCase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						scheduler.step(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__       ).prev_sample

           assert sample.dtype == torch.floataa









import random

class 		UpperCamelCase__ :



     '''simple docstring'''

     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    str       )     ->		tuple[list[int], list[int]]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[ord(lowerCamelCase__       ) for i in text]
           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						[]
           for i in plain:
                 SCREAMING_SNAKE_CASE		=						random.randint(1						,300       )
                 SCREAMING_SNAKE_CASE		=						(i + k) * k
                 cipher.append(lowerCamelCase__       )
                 key.append(lowerCamelCase__       )
           return cipher, key






     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    list[int]						,lowerCamelCase__						:    list[int]       )     ->		str:




           '''simple docstring'''


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


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


