
def 			__UpperCAmelCase		(		a_			:		int = "mytext.txt"    ,  a_			:		Tuple = False				)     ->       None:
   import socket

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

      with open(lowerCamelCase__    ,  "rb"				) as in_file:
         _UpperCAmelCase   	=					in_file.read(10_24				)
         while data:
            conn.send(lowerCamelCase__				)
            print(F'''Sent {data!r}'''				)
            _UpperCAmelCase   	=					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 torch

from transformers import MraConfig, MraForMaskedLM
def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
   if "model" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
   if "norm1" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
   if "norm2" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
   if "norm" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
   if "transformer" in orig_key:
      _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
      _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
   if "mha.attn" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
   if "mha" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
   if "W_q" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
   if "W_k" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
   if "W_v" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
   if "ff.0" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
   if "ff.2" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
   if "ff" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
   if "mlm_class" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
   if "mlm" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
   if "backbone.backbone.encoders" in orig_key:
      _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
   if "cls" not in orig_key:
      _UpperCAmelCase   	=					"mra." + orig_key

   return orig_key
def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
   for key in orig_state_dict.copy().keys():
      _UpperCAmelCase   	=					orig_state_dict.pop(a_				)

      if ("pooler" in key) or ("sen_class" in key):
         continue
      else:
         _UpperCAmelCase   	=					val

   _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
   _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2

   return orig_state_dict
def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
   _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
   _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
   _UpperCAmelCase   	=					MraForMaskedLM(a_				)

   _UpperCAmelCase   	=					convert_checkpoint_helper(config.max_position_embeddings    ,  a_				)

   print(model.load_state_dict(a_				)				)
   model.eval()
   model.save_pretrained(a_				)

   print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The json file for Mra model config.',
  )
  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_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

