





import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	Dict  	=      (DDIMScheduler,)
  __lowerCAmelCase      :	Union[str, Any]  	=      (("""eta""", 0.0), ("""num_inference_steps""", 50))


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       A_					      =					{
           '''num_train_timesteps''': 1_0_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }

       config.update(**__snake_case   )
       return config


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





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

       A_      ,						A_					      =					1_0, 0.0

       A_					      =					self.dummy_model()
       A_					      =					self.dummy_sample_deter

       scheduler.set_timesteps(__snake_case   )

       for t in scheduler.timesteps:
            A_					      =					model(__snake_case   ,__snake_case   )
            A_					      =					scheduler.step(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ).prev_sample

       return sample


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=__snake_case   )

       A_					      =					self.scheduler_classes[0]
       A_					      =					self.get_scheduler_config(steps_offset=1   )
       A_					      =					scheduler_class(**__snake_case   )
       scheduler.set_timesteps(5   )
       assert torch.equal(scheduler.timesteps   ,torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]   )   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,[0.002, 0.02, 0.2, 2]   ):
            self.check_over_configs(beta_start=__snake_case   ,beta_end=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       self.check_over_configs(thresholding=__snake_case   )
       for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=__snake_case   ,prediction_type=__snake_case   ,sample_max_value=__snake_case   ,)


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for t in [1, 1_0, 4_9]:
            self.check_over_forward(time_step=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for t, num_inference_steps in zip([1, 1_0, 5_0]   ,[1_0, 5_0, 5_0_0]   ):
            self.check_over_forward(time_step=__snake_case   ,num_inference_steps=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       for t, eta in zip([1, 1_0, 4_9]   ,[0.0, 0.5, 1.0]   ):
            self.check_over_forward(time_step=__snake_case   ,eta=__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





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

       assert torch.sum(torch.abs(scheduler._get_variance(0   ,0   ) - 0.0   )   ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_2_0   ,4_0_0   ) - 0.1_4771   )   ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_8_0   ,9_6_0   ) - 0.3_2460   )   ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0   ,0   ) - 0.0   )   ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ,4_8_6   ) - 0.0_0979   )   ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ,9_9_8   ) - 0.02   )   ) < 1E-5


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.full_loop()

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

       assert abs(result_sum.item() - 172.0067   ) < 1E-2
       assert abs(result_mean.item() - 0.22_3967   ) < 1E-3


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.full_loop(prediction_type='''v_prediction'''   )

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

       assert abs(result_sum.item() - 52.5302   ) < 1E-2
       assert abs(result_mean.item() - 0.0684   ) < 1E-3


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.full_loop(set_alpha_to_one=__snake_case   ,beta_start=0.01   )
       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 149.8295   ) < 1E-2
       assert abs(result_mean.item() - 0.1951   ) < 1E-3




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.full_loop(set_alpha_to_one=__snake_case   ,beta_start=0.01   )
       A_					      =					torch.sum(torch.abs(__snake_case   )   )
       A_					      =					torch.mean(torch.abs(__snake_case   )   )

       assert abs(result_sum.item() - 149.0784   ) < 1E-2
       assert abs(result_mean.item() - 0.1941   ) < 1E-3










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() = }''')



