





'''simple docstring'''






def      UpperCamelCase__   (	lowerCAmelCase						):
      _lowerCAmelCase							  =							len(lowerCAmelCase						)

      # Set the gap value to be decreased by a factor of 1.3
      # after each iteration
      _lowerCAmelCase							  =							1.3

      # Continue sorting until the gap is 1
      while gap > 1:
            # Decrease the gap value
            _lowerCAmelCase							  =							int(gap / shrink						)

            # Sort the elements using insertion sort
            for i in range(lowerCAmelCase							,			len(lowerCAmelCase						)						):
                  _lowerCAmelCase							  =							collection[i]
                  _lowerCAmelCase							  =							i
                  while j >= gap and collection[j - gap] > temp:
                        _lowerCAmelCase							  =							collection[j - gap]
                        j -= gap
                  _lowerCAmelCase							  =							temp

      return collection


if __name__ == "__main__":
      import doctest

      doctest.testmod()













'''simple docstring'''






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


A__       :     Optional[Any]          =get_logger(__name__)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = None							,			):





      """simple docstring"""


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





      """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:
            _lowerCAmelCase							  =							"""cpu"""
      elif map_location == "on_device":
            _lowerCAmelCase							  =							PartialState().device
      # Model states
      for i, model in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            models[i].load_state_dict(torch.load(lowerCAmelCase							,			map_location=lowerCAmelCase						)							,			**lowerCAmelCase						)
      logger.info("""All model weights loaded successfully"""						)

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

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

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

      # Random states
      try:
            _lowerCAmelCase							  =							torch.load(os.path.join(lowerCAmelCase							,			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__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = 0						):





      """simple docstring"""


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





def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = 0						):





      """simple docstring"""


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






