





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	str  	=      (HeunDiscreteScheduler,)
  __lowerCAmelCase      :	List[str]  	=      10


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       A_					      =					{
           '''num_train_timesteps''': 1_1_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__snake_case   )
       return config


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       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=__snake_case   ,beta_end=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for schedule in ["linear", "scaled_linear", "exp"]:
            self.check_over_configs(beta_schedule=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for clip_sample_range in [1.0, 2.0, 3.0]:
            self.check_over_configs(clip_sample_range=__snake_case   ,clip_sample=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config()
       A_					      =					scheduler_class(**__snake_case   )

       scheduler.set_timesteps(self.num_inference_steps   )

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter * scheduler.init_noise_sigma
       A_					      =					sample.to(__snake_case   )

       for i, t in enumerate(scheduler.timesteps   ):
            A_					      =					scheduler.scale_model_input(__snake_case   ,__snake_case   )

            A_					      =					model(__snake_case   ,__snake_case   )

            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   )
            A_					      =					output.prev_sample

       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 0.1233   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 0.1233   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config(prediction_type='''v_prediction'''   )
       A_					      =					scheduler_class(**__snake_case   )

       scheduler.set_timesteps(self.num_inference_steps   )

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter * scheduler.init_noise_sigma
       A_					      =					sample.to(__snake_case   )

       for i, t in enumerate(scheduler.timesteps   ):
            A_					      =					scheduler.scale_model_input(__snake_case   ,__snake_case   )

            A_					      =					model(__snake_case   ,__snake_case   )

            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   )
            A_					      =					output.prev_sample

       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.69_34E-07   ) < 1E-2
            assert abs(result_mean.item() - 6.11_12E-10   ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config()
       A_					      =					scheduler_class(**__snake_case   )

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

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter.to(__snake_case   ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            A_					      =					scheduler.scale_model_input(__snake_case   ,__snake_case   )

            A_					      =					model(__snake_case   ,__snake_case   )

            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   )
            A_					      =					output.prev_sample

       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       if str(__snake_case   ).startswith('''cpu'''   ):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 0.1233   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3
       elif str(__snake_case   ).startswith('''mps'''   ):
            # Larger tolerance on mps
            assert abs(result_mean.item() - 0.0002   ) < 1E-2
       else:
            # CUDA
            assert abs(result_sum.item() - 0.1233   ) < 1E-2
            assert abs(result_mean.item() - 0.0002   ) < 1E-3




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config()
       A_					      =					scheduler_class(**__snake_case   ,use_karras_sigmas=__snake_case   )

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

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter.to(__snake_case   ) * scheduler.init_noise_sigma
       A_					      =					sample.to(__snake_case   )

       for t in scheduler.timesteps:
            A_					      =					scheduler.scale_model_input(__snake_case   ,__snake_case   )

            A_					      =					model(__snake_case   ,__snake_case   )

            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   )
            A_					      =					output.prev_sample

       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 0.0_0015   ) < 1E-2
       assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07   ) < 1E-2










import itertools
from pathlib import Path

def     UpperCAmelCase_						(						_UpperCAmelCase       :list[str]							)  ->							int:
     '''simple docstring'''


     A_					      =					[tuple(_UpperCAmelCase							) for login in logins]

     A_					      =					{char for login in split_logins for char in login}

     for permutation in itertools.permutations(_UpperCAmelCase							):
          A_					      =					True
          for login in logins:
               if not (
                   permutation.index(login[0]							)
                   < permutation.index(login[1]							)
                   < permutation.index(login[2]							)
               ):
                    A_					      =					False
                    break

          if satisfied:
               return int(''''''.join(_UpperCAmelCase							)							)

     raise Exception('''Unable to find the secret passcode'''							)

def     UpperCAmelCase_						(						_UpperCAmelCase       :str = "keylog.txt"							)  ->							int:
     '''simple docstring'''


     A_					      =					Path(_UpperCAmelCase							).parent.joinpath(_UpperCAmelCase							).read_text().splitlines()

     return find_secret_passcode(_UpperCAmelCase							)


if __name__ == "__main__":
      print(f'''{solution() = }''')



