

def     UpperCAmelCase__       (    _A   :				str = "mytext.txt"			,   _A   :				bool = False							):





  '''simple docstring'''







  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(_A			,   '''rb'''							) as in_file:
      a__						=in_file.read(10_24							)
      while data:
        conn.send(_A							)
        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()



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowerCamelCase     =      get_logger(__name__)




def     UpperCAmelCase__       (    _A   :				str			,   _A   :				List[dict]			,   _A   :				list			,   _A   :				list			,   _A   :				int			,   _A   :				GradScaler = None			,   ):





  '''simple docstring'''







  for i, state in enumerate(_A							):
    a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Model weights saved in {output_model_file}"""							)
  # Optimizer states
  for i, opt in enumerate(_A							):
    a__						=opt.state_dict()
    a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__						=scheduler.state_dict()
    a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
  # GradScaler state
  if scaler is not None:
    a__						=scaler.state_dict()
    a__						=os.path.join(_A			,   _A							)
    torch.save(_A			,   _A							)
    logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
  # Random number generator states
  a__						={}
  a__						=F"""{RNG_STATE_NAME}_{process_index}.pkl"""
  a__						=random.getstate()
  a__						=np.random.get_state()
  a__						=torch.get_rng_state()
  if is_xpu_available():
    a__						=torch.xpu.get_rng_state_all()
  else:
    a__						=torch.cuda.get_rng_state_all()
  if is_tpu_available():
    a__						=xm.get_rng_state()
  a__						=os.path.join(_A			,   _A							)
  torch.save(_A			,   _A							)
  logger.info(F"""Random states saved in {output_states_file}"""							)
  return output_dir




def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				int			,   _A   :				int			,   _A   :				Union[str, Any]			,   _A   :				Tuple			,   _A   :				Optional[int]=None			,   _A   :				List[Any]=None			,   **_A   :				Dict			,   ):





  '''simple docstring'''







  if map_location not in [None, "cpu", "on_device"]:
    raise TypeError(
        '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
  if map_location is None:
    a__						='''cpu'''
  elif map_location == "on_device":
    a__						=PartialState().device
  # Model states
  for i, model in enumerate(_A							):
    a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    models[i].load_state_dict(torch.load(_A			,   map_location=_A							)			,   **_A							)
  logger.info('''All model weights loaded successfully'''							)

  # Optimizer states
  for i, opt in enumerate(_A							):
    a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    a__						=torch.load(_A			,   map_location=_A							)
    optimizers[i].load_state_dict(_A							)
  logger.info('''All optimizer states loaded successfully'''							)

  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    scheduler.load_state_dict(torch.load(_A							)							)
  logger.info('''All scheduler states loaded successfully'''							)

  # GradScaler state
  if scaler is not None:
    a__						=os.path.join(_A			,   _A							)
    scaler.load_state_dict(torch.load(_A							)							)
    logger.info('''GradScaler state loaded successfully'''							)

  # Random states
  try:
    a__						=torch.load(os.path.join(_A			,   F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
    random.setstate(states['''random_state''']							)
    np.random.set_state(states['''numpy_random_seed''']							)
    torch.set_rng_state(states['''torch_manual_seed''']							)
    if is_xpu_available():
      torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
    else:
      torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
    if is_tpu_available():
      xm.set_rng_state(states['''xm_seed''']							)
    logger.info('''All random states loaded successfully'''							)
  except Exception:
    logger.info('''Could not load random states'''							)




def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				Any			,   _A   :				int = 0							):





  '''simple docstring'''







  a__						=Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
  logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
  torch.save(obj.state_dict()			,   _A							)




def     UpperCAmelCase__       (    _A   :				int			,   _A   :				Any			,   _A   :				int = 0							):





  '''simple docstring'''







  a__						=F"""{path}/custom_checkpoint_{index}.pkl"""
  logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
  obj.load_state_dict(torch.load(_A			,   map_location='''cpu'''							)							)
