
import itertools
from pathlib import Path






def        lowerCamelCase__    (    __lowerCAmelCase  :  list[str]    ):




			"""simple docstring"""


			lowerCAmelCase_      =     [tuple(_lowerCAmelCase    ) for login in logins]

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

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

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

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






def        lowerCamelCase__    (    __lowerCAmelCase  :  str = "keylog.txt"    ):




			"""simple docstring"""


			lowerCAmelCase_      =     Path(_lowerCAmelCase    ).parent.joinpath(_lowerCAmelCase    ).read_text().splitlines()

			return find_secret_passcode(_lowerCAmelCase    )


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





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class      _lowerCAmelCase (     __a	):
				_lowercase      		=(VQDiffusionScheduler,)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**_UpperCamelCase					)
							return config




				def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

							lowerCAmelCase_      =     torch.randint(0    ,    _UpperCamelCase    ,    (batch_size, height * width)					)

							return sample




				@property
				def 					__a						( self					)    ->				List[str]:
							assert False




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							def model(_UpperCamelCase    ,    _UpperCamelCase    ,    *_UpperCamelCase					):
										lowerCAmelCase_     ,  lowerCAmelCase_      =     sample.shape
										lowerCAmelCase_      =     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
										lowerCAmelCase_      =     F.log_softmax(logits.double()    ,    dim=1					).float()
										return return_value

							return model




				def 					__a						( self					)    ->				str:
							for timesteps in [2, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							for num_vec_classes in [5, 100, 1_000, 4_000]:
										self.check_over_configs(num_vec_classes=_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[str]:
							pass



