"""simple docstring"""







import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							str    =    (HeunDiscreteScheduler,)
   __UpperCAmelCase	:							Dict    =    10







   def    _A				(	self ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					{
          'num_train_timesteps': 1100,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config







   def    _A				(	self	):


      '''simple docstring'''

      for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)







   def    _A				(	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=_SCREAMING_SNAKE_CASE ,				beta_end=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

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







   def    _A				(	self	):


      '''simple docstring'''

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







   def    _A				(	self	):


      '''simple docstring'''

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







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.scheduler_classes[0]
      __lowerCamelCase    		=					self.get_scheduler_config()
      __lowerCamelCase    		=					scheduler_class(**_SCREAMING_SNAKE_CASE	)

      scheduler.set_timesteps(self.num_inference_steps	)

      __lowerCamelCase    		=					self.dummy_model()
      __lowerCamelCase    		=					self.dummy_sample_deter * scheduler.init_noise_sigma
      __lowerCamelCase    		=					sample.to(_SCREAMING_SNAKE_CASE	)

      for i, t in enumerate(scheduler.timesteps	):
         __lowerCamelCase    		=					scheduler.scale_model_input(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase    		=					scheduler.step(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					output.prev_sample

      __lowerCamelCase    		=					torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      __lowerCamelCase    		=					torch.mean(torch.abs(_SCREAMING_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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.scheduler_classes[0]
      __lowerCamelCase    		=					self.get_scheduler_config(prediction_type='v_prediction'	)
      __lowerCamelCase    		=					scheduler_class(**_SCREAMING_SNAKE_CASE	)

      scheduler.set_timesteps(self.num_inference_steps	)

      __lowerCamelCase    		=					self.dummy_model()
      __lowerCamelCase    		=					self.dummy_sample_deter * scheduler.init_noise_sigma
      __lowerCamelCase    		=					sample.to(_SCREAMING_SNAKE_CASE	)

      for i, t in enumerate(scheduler.timesteps	):
         __lowerCamelCase    		=					scheduler.scale_model_input(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase    		=					scheduler.step(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					output.prev_sample

      __lowerCamelCase    		=					torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      __lowerCamelCase    		=					torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6_9_3_4E-0_7	) < 1E-2
         assert abs(result_mean.item() - 6.1_1_1_2E-1_0	) < 1E-3
      else:
         # CUDA
         assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7	) < 1E-2
         assert abs(result_mean.item() - 0.0002	) < 1E-3







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.scheduler_classes[0]
      __lowerCamelCase    		=					self.get_scheduler_config()
      __lowerCamelCase    		=					scheduler_class(**_SCREAMING_SNAKE_CASE	)

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

      __lowerCamelCase    		=					self.dummy_model()
      __lowerCamelCase    		=					self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE	) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         __lowerCamelCase    		=					scheduler.scale_model_input(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase    		=					scheduler.step(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					output.prev_sample

      __lowerCamelCase    		=					torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      __lowerCamelCase    		=					torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      if str(_SCREAMING_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(_SCREAMING_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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.scheduler_classes[0]
      __lowerCamelCase    		=					self.get_scheduler_config()
      __lowerCamelCase    		=					scheduler_class(**_SCREAMING_SNAKE_CASE ,				use_karras_sigmas=_SCREAMING_SNAKE_CASE	)

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

      __lowerCamelCase    		=					self.dummy_model()
      __lowerCamelCase    		=					self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE	) * scheduler.init_noise_sigma
      __lowerCamelCase    		=					sample.to(_SCREAMING_SNAKE_CASE	)

      for t in scheduler.timesteps:
         __lowerCamelCase    		=					scheduler.scale_model_input(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase    		=					scheduler.step(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					output.prev_sample

      __lowerCamelCase    		=					torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      __lowerCamelCase    		=					torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 0.0_0015	) < 1E-2
      assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7	) < 1E-2











import itertools
from pathlib import Path







def       lowerCamelCase_  (    _a       :						list[str]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					[tuple(_a    ) for login in logins]

    UpperCAmelCase_     :       str  		=					{char for login in split_logins for char in login}

    for permutation in itertools.permutations(_a    ):
        UpperCAmelCase_     :       str  		=					True
        for login in logins:
            if not (
                permutation.index(login[0]    )
                < permutation.index(login[1]    )
                < permutation.index(login[2]    )
            ):
                UpperCAmelCase_     :       Optional[int]  		=					False
                break

        if satisfied:
            return int("""""".join(_a    )    )

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







def       lowerCamelCase_  (    _a       :						str = "keylog.txt"    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					Path(_a    ).parent.joinpath(_a    ).read_text().splitlines()

    return find_secret_passcode(_a    )


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