"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class       _lowercase		( __UpperCamelCase	):
				lowerCamelCase__							=							(DPMSolverMultistepScheduler,)
				lowerCamelCase__							=							(('num_inference_steps', 2_5),)

				def 							A			(  self							:     int							,     **_lowercase							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		{
										    """num_train_timesteps""": 1_0_0_0,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """solver_order""": 2,
										    """prediction_type""": """epsilon""",
										    """thresholding""": False,
										    """sample_max_value""": 1.0,
										    """algorithm_type""": """dpmsolver++""",
										    """solver_type""": """midpoint""",
										    """lower_order_final""": False,
										    """lambda_min_clipped""": -float("""inf"""  ),
										    """variance_type""": None,
										}

										config.update(**__lowercase  )
										return config

				def 							A			(  self							:     Tuple							,     _lowercase							:     List[Any]=0							,     **_lowercase							:     Optional[int]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		dict(self.forward_default_kwargs  )
										UpperCAmelCase__       						=		kwargs.pop("""num_inference_steps"""							,     __lowercase  )
										UpperCAmelCase__       						=		self.dummy_sample
										UpperCAmelCase__       						=		0.1 * sample
										UpperCAmelCase__       						=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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

				def 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										pass

				def 							A			(  self							:     Optional[Any]							,     _lowercase							:     str=0							,     **_lowercase							:     Tuple  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		dict(self.forward_default_kwargs  )
										UpperCAmelCase__       						=		kwargs.pop("""num_inference_steps"""							,     __lowercase  )
										UpperCAmelCase__       						=		self.dummy_sample
										UpperCAmelCase__       						=		0.1 * sample
										UpperCAmelCase__       						=		[residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase__       						=		self.get_scheduler_config()
																UpperCAmelCase__       						=		scheduler_class(**__lowercase  )
																scheduler.set_timesteps(__lowercase  )

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

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

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

																UpperCAmelCase__       						=		scheduler.step(__lowercase							,     __lowercase							,     __lowercase							,     **__lowercase  ).prev_sample
																UpperCAmelCase__       						=		new_scheduler.step(__lowercase							,     __lowercase							,     __lowercase							,     **__lowercase  ).prev_sample

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

				def 							A			(  self							:     Union[str, Any]							,     _lowercase							:     Dict=None							,     **_lowercase							:     Tuple  ):


										'''simple docstring'''
										if scheduler is None:
																UpperCAmelCase__       						=		self.scheduler_classes[0]
																UpperCAmelCase__       						=		self.get_scheduler_config(**__lowercase  )
																UpperCAmelCase__       						=		scheduler_class(**__lowercase  )

										UpperCAmelCase__       						=		1_0
										UpperCAmelCase__       						=		self.dummy_model()
										UpperCAmelCase__       						=		self.dummy_sample_deter
										scheduler.set_timesteps(__lowercase  )

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

										return sample

				def 							A			(  self							:     Tuple  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		dict(self.forward_default_kwargs  )

										UpperCAmelCase__       						=		kwargs.pop("""num_inference_steps"""							,     __lowercase  )

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase__       						=		self.get_scheduler_config()
																UpperCAmelCase__       						=		scheduler_class(**__lowercase  )

																UpperCAmelCase__       						=		self.dummy_sample
																UpperCAmelCase__       						=		0.1 * sample

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

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

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

																UpperCAmelCase__       						=		scheduler.step(__lowercase							,     __lowercase							,     __lowercase							,     **__lowercase  ).prev_sample
																UpperCAmelCase__       						=		scheduler.step(__lowercase							,     __lowercase							,     __lowercase							,     **__lowercase  ).prev_sample

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

				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=__lowercase  )

				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										self.check_over_configs(thresholding=__lowercase  )
										for order in [1, 2, 3]:
																for solver_type in ["midpoint", "heun"]:
																						for threshold in [0.5, 1.0, 2.0]:
																												for prediction_type in ["epsilon", "sample"]:
																																		self.check_over_configs(
																																		    thresholding=__lowercase							,     prediction_type=__lowercase							,     sample_max_value=__lowercase							,     algorithm_type="""dpmsolver++"""							,     solver_order=__lowercase							,     solver_type=__lowercase							,     )

				def 							A			(  self							:     str  ):


										'''simple docstring'''
										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=__lowercase  )

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
																for solver_type in ["midpoint", "heun"]:
																						for order in [1, 2, 3]:
																												for prediction_type in ["epsilon", "sample"]:
																																		if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																								if order == 3:
																																														continue
																																		else:
																																								self.check_over_configs(
																																								    solver_order=__lowercase							,     solver_type=__lowercase							,     prediction_type=__lowercase							,     algorithm_type=__lowercase							,     )
																																		UpperCAmelCase__       						=		self.full_loop(
																																		    solver_order=__lowercase							,     solver_type=__lowercase							,     prediction_type=__lowercase							,     algorithm_type=__lowercase							,     )
																																		assert not torch.isnan(__lowercase  ).any(), "Samples have nan numbers"

				def 							A			(  self							:     Union[str, Any]  ):


										'''simple docstring'''
										self.check_over_configs(lower_order_final=__lowercase  )
										self.check_over_configs(lower_order_final=__lowercase  )

				def 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										self.check_over_configs(lambda_min_clipped=-float("""inf"""  )  )
										self.check_over_configs(lambda_min_clipped=-5.1  )

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										self.check_over_configs(variance_type=__lowercase  )
										self.check_over_configs(variance_type="""learned_range"""  )

				def 							A			(  self							:     Optional[Any]  ):


										'''simple docstring'''
										for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																self.check_over_forward(num_inference_steps=__lowercase							,     time_step=0  )

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.full_loop()
										UpperCAmelCase__       						=		torch.mean(torch.abs(__lowercase  )  )

										assert abs(result_mean.item() - 0.3301  ) < 1e-3

				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.full_loop(thresholding=__lowercase							,     dynamic_thresholding_ratio=0.87							,     sample_max_value=0.5  )
										UpperCAmelCase__       						=		torch.mean(torch.abs(__lowercase  )  )

										assert abs(result_mean.item() - 1.1364  ) < 1e-3

				def 							A			(  self							:     str  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.full_loop(prediction_type="""v_prediction"""  )
										UpperCAmelCase__       						=		torch.mean(torch.abs(__lowercase  )  )

										assert abs(result_mean.item() - 0.2251  ) < 1e-3

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.full_loop(prediction_type="""v_prediction"""							,     use_karras_sigmas=__lowercase  )
										UpperCAmelCase__       						=		torch.mean(torch.abs(__lowercase  )  )

										assert abs(result_mean.item() - 0.2096  ) < 1e-3

				def 							A			(  self							:     Tuple  ):


										'''simple docstring'''
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										UpperCAmelCase__       						=		DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
										UpperCAmelCase__       						=		self.full_loop(scheduler=__lowercase  )
										UpperCAmelCase__       						=		torch.mean(torch.abs(__lowercase  )  )

										assert abs(result_mean.item() - 0.3301  ) < 1e-3

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

										UpperCAmelCase__       						=		self.full_loop(scheduler=__lowercase  )
										UpperCAmelCase__       						=		torch.mean(torch.abs(__lowercase  )  )

										assert abs(result_mean.item() - 0.3301  ) < 1e-3

				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.scheduler_classes[0]
										UpperCAmelCase__       						=		self.get_scheduler_config(thresholding=__lowercase							,     dynamic_thresholding_ratio=0  )
										UpperCAmelCase__       						=		scheduler_class(**__lowercase  )

										UpperCAmelCase__       						=		1_0
										UpperCAmelCase__       						=		self.dummy_model()
										UpperCAmelCase__       						=		self.dummy_sample_deter.half()
										scheduler.set_timesteps(__lowercase  )

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

										assert sample.dtype == torch.floataa

				def 							A			(  self							:     List[str]							,     **_lowercase							:     Optional[int]  ):


										'''simple docstring'''
										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase__       						=		self.get_scheduler_config(**__lowercase  )
																UpperCAmelCase__       						=		scheduler_class(**__lowercase  )

																scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
																assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps









"""simple docstring"""



import itertools
from pathlib import Path




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->int:
 UpperCAmelCase__ 						=				[tuple(_SCREAMING_SNAKE_CASE			) for login in logins]

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

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

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

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




def       snake_case__		(   _SCREAMING_SNAKE_CASE = "keylog.txt"			)    ->int:
 UpperCAmelCase__ 						=				Path(_SCREAMING_SNAKE_CASE			).parent.joinpath(_SCREAMING_SNAKE_CASE			).read_text().splitlines()

 return find_secret_passcode(_SCREAMING_SNAKE_CASE			)


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



