code: 
  
  
  '''simple docstring'''
  
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional, Tuple
  
  from .training_args import TrainingArguments
  from .utils import cached_property, is_tf_available, logging, requires_backends
  
  
  lowerCAmelCase   :Dict								=    logging.get_logger(__name__)
  
  if is_tf_available():
  							import tensorflow as tf
  @dataclass
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """tf"""
  		A_			:    Optional[str]   					=       field(
  		    default=lowercase__	,  metadata={"""help""": """Name of TPU"""}	,  )
  
  		A_			:    Optional[str]   					=       field(
  		    default=lowercase__	,  metadata={"""help""": """Zone of TPU"""}	,  )
  
  		A_			:    Optional[str]   					=       field(
  		    default=lowercase__	,  metadata={"""help""": """Name of Cloud TPU-enabled project"""}	,  )
  
  		A_			:    float   					=       field(
  		    default=1.0	,  metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}	,  )
  
  		A_			:    bool   					=       field(default=lowercase__	,  metadata={"""help""": """Whether to activate the XLA compilation or not"""}							)
  
  
  
  
  
  
  
  		@cached_property
  		def      __lowerCAmelCase	(      self	:		int    )						->				Tuple["tf.distribute.Strategy", int]:
  			requires_backends(self						,  ['tf']    )
  			logger.info('Tensorflow: setting up strategy'    )
  
  			__magic_name__ :					Union[str, Any]  			=  tf.config.list_physical_devices('GPU'    )
  
  			# Set to float16 at first
  			if self.fpaa:
  				tf.keras.mixed_precision.set_global_policy('mixed_float16'    )
  
  			if self.no_cuda:
  				__magic_name__ :					Optional[int]  			=  tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
  			else:
  				try:
  					if self.tpu_name:
  						__magic_name__ :					str  			=  tf.distribute.cluster_resolver.TPUClusterResolver(
  						    self.tpu_name						,  zone=self.tpu_zone						,  project=self.gcp_project    )
  					else:
  						__magic_name__ :					Dict  			=  tf.distribute.cluster_resolver.TPUClusterResolver()
  				except ValueError:
  					if self.tpu_name:
  						raise RuntimeError(F'Couldn\'t connect to TPU {self.tpu_name}!'    )
  					else:
  						__magic_name__ :					str  			=  None
  
  				if tpu:
  					# Set to bfloat16 in case of TPU
  					if self.fpaa:
  						tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'    )
  
  					tf.config.experimental_connect_to_cluster(_A    )
  					tf.tpu.experimental.initialize_tpu_system(_A    )
  
  					__magic_name__ :					Optional[Any]  			=  tf.distribute.TPUStrategy(_A    )
  
  				elif len(_A    ) == 0:
  					__magic_name__ :					Optional[int]  			=  tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
  				elif len(_A    ) == 1:
  					__magic_name__ :					Any  			=  tf.distribute.OneDeviceStrategy(device='/gpu:0'    )
  				elif len(_A    ) > 1:
  					# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
  					__magic_name__ :					int  			=  tf.distribute.MirroredStrategy()
  				else:
  					raise ValueError('Cannot find the proper strategy, please check your environment properties.'    )
  
  			return strategy
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				"tf.distribute.Strategy":
  			requires_backends(self						,  ['tf']    )
  			return self._setup_strategy
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		int    )						->				int:
  			requires_backends(self						,  ['tf']    )
  			return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Any    )						->				List[str]:
  			return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				int:
  			if self.per_gpu_train_batch_size:
  				logger.warning(
  				    'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
  				    'version. Using `--per_device_train_batch_size` is preferred.'    )
  			__magic_name__ :					Optional[int]  			=  self.per_gpu_train_batch_size or self.per_device_train_batch_size
  			return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		int    )						->				int:
  			if self.per_gpu_eval_batch_size:
  				logger.warning(
  				    'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
  				    'version. Using `--per_device_eval_batch_size` is preferred.'    )
  			__magic_name__ :					List[Any]  			=  self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
  			return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				int:
  			requires_backends(self						,  ['tf']    )
  			warnings.warn(
  			    'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'						,  _A						,  )
  			return self._setup_strategy.num_replicas_in_sync
style_context: 
  
  
  '''simple docstring'''
  
  import itertools
  from pathlib import Path
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  [tuple(lowerCAmelCase	) for login in logins]
  
  	__magic_name__ :					int  			=  {char for login in split_logins for char in login}
  
  	for permutation in itertools.permutations(lowerCAmelCase	):
  		__magic_name__ :					List[Any]  			=  True
  		for login in logins:
  			if not (
  			    permutation.index(login[0]	)
  			    < permutation.index(login[1]	)
  			    < permutation.index(login[2]	)
  			):
  				__magic_name__ :					Tuple  			=  False
  				break
  
  		if satisfied:
  			return int(''.join(lowerCAmelCase	)	)
  
  	raise Exception('Unable to find the secret passcode'	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str = "keylog.txt"	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[Any]  			=  Path(lowerCAmelCase	).parent.joinpath(lowerCAmelCase	).read_text().splitlines()
  
  	return find_secret_passcode(lowerCAmelCase	)
  
  
  if __name__ == "__main__":
  							print(F'{solution() = }')