
'''simple docstring'''
def       _A	(			snake_case = "mytext.txt"	,							snake_case = False  )    ->			None:
   import socket

   _lowercase    :   Any      =     1_23_12  # Reserve a port for your service.
   _lowercase    :   Union[str, Any]      =     socket.socket()  # Create a socket object
   _lowercase    :   List[Any]      =     socket.gethostname()  # Get local machine name
   sock.bind((host, port)  )  # Bind to the port
   sock.listen(5  )  # Now wait for client connection.

   print("Server listening...."  )

   while True:
      _lowercase  ,   _lowercase    :   Union[str, Any]      =     sock.accept()  # Establish connection with client.
      print(F'''Got connection from {addr}'''  )
      _lowercase    :   Union[str, Any]      =     conn.recv(10_24  )
      print(F'''Server received: {data = }'''  )

      with open(lowerCamelCase__	,							"rb"  ) as in_file:
         _lowercase    :   List[Any]      =     in_file.read(10_24  )
         while data:
            conn.send(lowerCamelCase__  )
            print(F'''Sent {data!r}'''  )
            _lowercase    :   Optional[int]      =     in_file.read(10_24  )

      print("Done sending"  )
      conn.close()
      if testing:  # Allow the test to complete
         break

   sock.shutdown(1  )
   sock.close()


if __name__ == "__main__":
    send_file()









'''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


_snake_case     						=						logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  int			 =							'tf'
   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Zone of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of Cloud TPU-enabled project'}						,   )

   _SCREAMING_SNAKE_CASE					:  float			 =							field(
       default=1.0						,   metadata={'help': 'Power for the Polynomial decay LR scheduler.'}						,   )

   _SCREAMING_SNAKE_CASE					:  bool			 =							field(default=lowerCamelCase_						,   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"						)

      _lowercase    :   Optional[int]      =     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:
         _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
      else:
         try:
            if self.tpu_name:
               _lowercase    :   int      =     tf.distribute.cluster_resolver.TPUClusterResolver(
                   self.tpu_name    ,       zone=self.tpu_zone    ,       project=self.gcp_project						)
            else:
               _lowercase    :   Any      =     tf.distribute.cluster_resolver.TPUClusterResolver()
         except ValueError:
            if self.tpu_name:
               raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''						)
            else:
               _lowercase    :   Tuple      =     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						)

            _lowercase    :   List[str]      =     tf.distribute.TPUStrategy(_UpperCamelCase						)

         elif len(_UpperCamelCase						) == 0:
            _lowercase    :   Optional[Any]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
         elif len(_UpperCamelCase						) == 1:
            _lowercase    :   Optional[int]      =     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`
            _lowercase    :   Any      =     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."						)
      _lowercase    :   str      =     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."						)
      _lowercase    :   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						):





      """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






