





def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:str = "mytext.txt"    ,    SCREAMING_SNAKE_CASE		:bool = False    )			->    Optional[int]:
 import socket

 __lowerCAmelCase     :       int						=   12_312  # Reserve a port for your service.
 __lowerCAmelCase     :       Union[str, Any]						=   socket.socket()  # Create a socket object
 __lowerCAmelCase     :       Optional[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:
  __lowerCAmelCase     :       Any						=   sock.accept()  # Establish connection with client.
  print(F'''Got connection from {addr}'''    )
  __lowerCAmelCase     :       List[Any]						=   conn.recv(1_024    )
  print(F'''Server received: {data = }'''    )

  with open(lowercase_    ,    """rb"""    ) as in_file:
   __lowerCAmelCase     :       Tuple						=   in_file.read(1_024    )
   while data:
    conn.send(lowercase_    )
    print(F'''Sent {data!r}'''    )
    __lowerCAmelCase     :       Optional[int]						=   in_file.read(1_024    )

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

 sock.shutdown(1    )
 sock.close()


if __name__ == "__main__":
      send_file()
import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

       if len(lowercase_ ) > 1:
              logger.info(F"averaging the weights of {ckpt_files}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
 fire.Fire(convert_pl_to_hf)



