

def       UpperCAmelCase__    (      _A				:			Tuple							):





  '''simple docstring'''







  a__  =len(_A							)
  for _ in range(
      0     ,				int(((no_of_elements - 1) / 2) + 1							)							):  # we don't need to traverse to end of list as
    for j in range(0     ,				no_of_elements - 1							):
      if (
          lst[j + 1] < lst[j]
      ):  # applying bubble sort algorithm from left to right (or forwards)
        a__  =lst[j + 1]
        a__  =lst[j]
        a__  =temp
      if (
          lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
      ):  # applying bubble sort algorithm from right to left (or backwards)
        a__  =lst[no_of_elements - 1 - j]
        a__  =lst[no_of_elements - 2 - j]
        a__  =temp
  return lst


if __name__ == "__main__":
       print('''enter the list to be sorted''')
       lowerCamelCase									= [int(x) for x in input().split()]  # inputing elements of the list in one line
       lowerCamelCase									= double_sort(lst)
       print('''the sorted list is''')
       print(sorted_lst)



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				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			int     ,				_A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			List[str]=None     ,				_A				:			Dict=None     ,				**_A				:			Tuple     ,				):





  '''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				:			Tuple     ,				_A				:			Tuple     ,				_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				:			Tuple     ,				_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'''							)							)
