import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Union[str, Any]	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      FunnelConfig.from_json_file(__UpperCamelCase	)
  print(f"""Building PyTorch model from configuration: {config}"""	)
  SCREAMING_SNAKE_CASE__										=      FunnelBaseModel(__UpperCamelCase	) if base_model else FunnelModel(__UpperCamelCase	)

  # Load weights from tf checkpoint
  load_tf_weights_in_funnel(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

  # Save pytorch-model
  print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
  torch.save(model.state_dict() ,     __UpperCamelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: Optional[Any]							   =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
   )
   __lowerCamelCase			: Any							   =		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
   )

import socket



def 	__SCREAMING_SNAKE_CASE  (					)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      socket.socket(socket.AF_INET ,     socket.SOCK_STREAM	)
  SCREAMING_SNAKE_CASE__										=      socket.gethostname()
  SCREAMING_SNAKE_CASE__										=      1_23_12

  sock.connect((host, port)	)
  sock.send(B"""Hello server!"""	)

  with open("""Received_file""" ,     """wb"""	) as out_file:
    print("""File opened"""	)
    print("""Receiving data..."""	)
    while True:
      SCREAMING_SNAKE_CASE__										=      sock.recv(10_24	)
      if not data:
        break
      out_file.write(__UpperCamelCase	)

  print("""Successfully received the file"""	)
  sock.close()
  print("""Connection closed"""	)


if __name__ == "__main__":
   main()
