
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


_A							=       logging.get_logger(__name__)

if is_tf_available():
      import tensorflow as tf

@dataclass
class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					int    =     """tf"""
 A__    :					Optional[str]    =     field(
     default=__SCREAMING_SNAKE_CASE				,						metadata={"""help""": """Name of TPU"""}				,						)

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

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

 @cached_property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

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

                      UpperCamelCase_											=							tf.distribute.TPUStrategy(__UpperCamelCase  )

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

        return strategy

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        requires_backends(self  ,						["""tf"""]  )
        return self._setup_strategy

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        requires_backends(self  ,						["""tf"""]  )
        return self._setup_strategy.num_replicas_in_sync

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

 @property
 def lowerCamelCase_					(   self  ):




        """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."""  )
        UpperCamelCase_											=							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  ):




        """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."""  )
        UpperCamelCase_											=							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  ):




        """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."""  ,						__UpperCamelCase  ,						)
        return self._setup_strategy.num_replicas_in_sync





_A							=       [chr(i) for i in range(32, 126)]
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(reversed(range(len(alphabets))))
_A							=       []
_A							=       _A							=       _A							=       0




def 					lowerCamelCase__					(    )		->						List[str]:
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       UpperCamelCase_											=							gear_one[0]
       gear_one.append(a__  )
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(a__  )  ) == 0:
              UpperCamelCase_											=							gear_two[0]
              gear_two.append(a__  )
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(a__  )  ) == 0:
                     UpperCamelCase_											=							gear_three[0]
                     gear_three.append(a__  )
                     del gear_three[0]
                     gear_three_pos += 1




def 					lowerCamelCase__					(    a__	:	Optional[int]  )		->						Any:
       UpperCamelCase_											=							alphabets.index(a__  )
       UpperCamelCase_											=							gear_one[target]
       UpperCamelCase_											=							gear_two[target]
       UpperCamelCase_											=							gear_three[target]
       UpperCamelCase_											=							reflector[target]
       UpperCamelCase_											=							gear_three.index(a__  )
       UpperCamelCase_											=							gear_two.index(a__  )
       UpperCamelCase_											=							gear_one.index(a__  )
       code.append(alphabets[target]  )
       rotator()


if __name__ == "__main__":
      _A							=       list(input('''Type your message:\n'''))
      while True:
            try:
                  _A							=       int(input('''Please set token:(must be only digits)\n'''))
                  break
            except Exception as error:
                  print(error)
      for _ in range(token):
            rotator()
      for j in decode:
            engine(j)
      print('''\n''' + ''''''.join(code))
      print(
          F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
          '''this message again you should input same digits as token!'''
      )



