






"""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


lowercase_    					=					logging.get_logger(__name__)

if is_tf_available():
       import tensorflow as tf


@dataclass
class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Tuple									=			"tf"
   A_						:     Optional[str]									=			field(
       default=_lowerCAmelCase       ,     metadata={"help": "Name of TPU"}       ,     )

   A_						:     Optional[str]									=			field(
       default=_lowerCAmelCase       ,     metadata={"help": "Zone of TPU"}       ,     )

   A_						:     Optional[str]									=			field(
       default=_lowerCAmelCase       ,     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=_lowerCAmelCase       ,     metadata={"help": "Whether to activate the XLA compilation or not"}						)


   @cached_property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     requires_backends(self,	['''tf''']  )
     logger.info('''Tensorflow: setting up strategy'''  )

     __A						      =			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:
       __A						      =			tf.distribute.OneDeviceStrategy(device='''/cpu:0'''  )
     else:
       try:
         if self.tpu_name:
           __A						      =			tf.distribute.cluster_resolver.TPUClusterResolver(
               self.tpu_name,	zone=self.tpu_zone,	project=self.gcp_project  )
         else:
           __A						      =			tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
         if self.tpu_name:
           raise RuntimeError(f'Couldn\'t connect to TPU {self.tpu_name}!'  )
         else:
           __A						      =			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(_lowerCamelCase  )
         tf.tpu.experimental.initialize_tpu_system(_lowerCamelCase  )

         __A						      =			tf.distribute.TPUStrategy(_lowerCamelCase  )

       elif len(_lowerCamelCase  ) == 0:
         __A						      =			tf.distribute.OneDeviceStrategy(device='''/cpu:0'''  )
       elif len(_lowerCamelCase  ) == 1:
         __A						      =			tf.distribute.OneDeviceStrategy(device='''/gpu:0'''  )
       elif len(_lowerCamelCase  ) > 1:
         # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
         __A						      =			tf.distribute.MirroredStrategy()
       else:
         raise ValueError('''Cannot find the proper strategy, please check your environment properties.'''  )

     return strategy


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     requires_backends(self,	['''tf''']  )
     return self._setup_strategy


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     requires_backends(self,	['''tf''']  )
     return self._setup_strategy.num_replicas_in_sync


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     return False  # TF Logging is handled by Keras not the Trainer


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     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.'''  )
     __A						      =			self.per_gpu_train_batch_size or self.per_device_train_batch_size
     return per_device_batch_size * self.n_replicas


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     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.'''  )
     __A						      =			self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
     return per_device_batch_size * self.n_replicas






   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     requires_backends(self,	['''tf''']  )
     warnings.warn(
         '''The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.''',	_lowerCamelCase,	)
     return self._setup_strategy.num_replicas_in_sync












"""simple docstring"""


import itertools
from pathlib import Path



def  lowerCAmelCase    (   __UpperCamelCase			):




  """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  lowerCAmelCase    (   __UpperCamelCase = "keylog.txt"			):




  """simple docstring"""







  __A						      =			Path(__UpperCamelCase			).parent.joinpath(__UpperCamelCase			).read_text().splitlines()

  return find_secret_passcode(__UpperCamelCase			)


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




