




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


UpperCamelCase__       				=	get_logger(__name__)






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A      ,      _A      ,      _A = None      ,      ):






  """simple docstring"""


  for i, state in enumerate(lowercase__			):
    a_		    =  f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
    a_		    =  os.path.join(lowercase__      ,      lowercase__			)
    save(lowercase__      ,      lowercase__			)
    logger.info(f"Model weights saved in {output_model_file}"			)
  # Optimizer states
  for i, opt in enumerate(lowercase__			):
    a_		    =  opt.state_dict()
    a_		    =  f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
    a_		    =  os.path.join(lowercase__      ,      lowercase__			)
    save(lowercase__      ,      lowercase__			)
    logger.info(f"Optimizer state saved in {output_optimizer_file}"			)
  # Scheduler states
  for i, scheduler in enumerate(lowercase__			):
    a_		    =  scheduler.state_dict()
    a_		    =  f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
    a_		    =  os.path.join(lowercase__      ,      lowercase__			)
    save(lowercase__      ,      lowercase__			)
    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(lowercase__      ,      lowercase__			)
    torch.save(lowercase__      ,      lowercase__			)
    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(lowercase__      ,      lowercase__			)
  torch.save(lowercase__      ,      lowercase__			)
  logger.info(f"Random states saved in {output_states_file}"			)
  return output_dir






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A      ,      _A      ,      _A=None      ,      _A=None      ,      **_A      ,      ):






  """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(lowercase__			):
    a_		    =  f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
    a_		    =  os.path.join(lowercase__      ,      lowercase__			)
    models[i].load_state_dict(torch.load(lowercase__      ,      map_location=lowercase__			)      ,      **lowercase__			)
  logger.info('''All model weights loaded successfully'''			)

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

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

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

  # Random states
  try:
    a_		    =  torch.load(os.path.join(lowercase__      ,      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      ,      _A      ,      _A = 0			):






  """simple docstring"""


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






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






  """simple docstring"""


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



"""simple docstring"""




from __future__ import annotations
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   {}
					for index, val in enumerate(lowercase__ ):
										A     =   target - val
										if compl in chk_map:
															return [chk_map[compl], index]
										A     =   index
					return []


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f"""{two_sum([2, 7, 11, 15], 9) = }""")





