




"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_ = "mytext.txt"     ,					lowercase_ = False		)		->						None:
 import socket

 A__			=							1_23_12  # Reserve a port for your service.
 A__			=							socket.socket()  # Create a socket object
 A__			=							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:
  A__, A__			=							sock.accept()  # Establish connection with client.
  print(f"""Got connection from {addr}"""		)
  A__			=							conn.recv(10_24		)
  print(f"""Server received: {data = }"""		)

  with open(lowercase_     ,					"rb"		) as in_file:
   A__			=							in_file.read(10_24		)
   while data:
    conn.send(lowercase_		)
    print(f"""Sent {data!r}"""		)
    A__			=							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 argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 # Initialise PyTorch model
 A__			=							json.load(open(lowercase_     ,					"r"		)		)
 A__			=							GPTNeoConfig(
     hidden_size=config_json["n_embd"]     ,					num_layers=config_json["n_layer"]     ,					num_heads=config_json["n_head"]     ,					attention_types=config_json["attention_types"]     ,					max_position_embeddings=config_json["n_positions"]     ,					resid_dropout=config_json["res_dropout"]     ,					embed_dropout=config_json["embed_dropout"]     ,					attention_dropout=config_json["attn_dropout"]     ,					)
 print(f"""Building PyTorch model from configuration: {config}"""		)
 A__			=							GPTNeoForCausalLM(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_gpt_neo(lowercase_     ,					lowercase_     ,					lowercase_		)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=			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 mesh-tf model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      SCREAMING_SNAKE_CASE							=			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





