





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       lowerCAmelCase__(	lowercase       :    str   ,     lowercase       :    List[dict]   ,     lowercase       :    list   ,     lowercase       :    list   ,     lowercase       :    int   ,     lowercase       :    GradScaler = None   ,     ) ->    List[str]:







  for i, state in enumerate(_A				):
    __snake_case   :				List[str]        =       f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
    __snake_case   :				List[str]        =       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				):
    __snake_case   :				Any        =       opt.state_dict()
    __snake_case   :				Optional[Any]        =       f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
    __snake_case   :				Union[str, Any]        =       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				):
    __snake_case   :				Optional[Any]        =       scheduler.state_dict()
    __snake_case   :				List[Any]        =       f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
    __snake_case   :				Any        =       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:
    __snake_case   :				Union[str, Any]        =       scaler.state_dict()
    __snake_case   :				List[str]        =       os.path.join(_A   ,     _A				)
    torch.save(_A   ,     _A				)
    logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""				)
  # Random number generator states
  __snake_case   :				List[Any]        =       {}
  __snake_case   :				Optional[Any]        =       f"""{RNG_STATE_NAME}_{process_index}.pkl"""
  __snake_case   :				Optional[Any]        =       random.getstate()
  __snake_case   :				List[str]        =       np.random.get_state()
  __snake_case   :				str        =       torch.get_rng_state()
  if is_xpu_available():
    __snake_case   :				Tuple        =       torch.xpu.get_rng_state_all()
  else:
    __snake_case   :				Tuple        =       torch.cuda.get_rng_state_all()
  if is_tpu_available():
    __snake_case   :				Union[str, Any]        =       xm.get_rng_state()
  __snake_case   :				int        =       os.path.join(_A   ,     _A				)
  torch.save(_A   ,     _A				)
  logger.info(f"""Random states saved in {output_states_file}"""				)
  return output_dir







def       lowerCAmelCase__(	lowercase       :    Union[str, Any]   ,     lowercase       :    int   ,     lowercase       :    int   ,     lowercase       :    Union[str, Any]   ,     lowercase       :    Tuple   ,     lowercase       :    Optional[int]=None   ,     lowercase       :    List[Any]=None   ,     **lowercase       :    Dict   ,     ) ->    Tuple:







  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:
    __snake_case   :				str        =       "cpu"
  elif map_location == "on_device":
    __snake_case   :				Tuple        =       PartialState().device
  # Model states
  for i, model in enumerate(_A				):
    __snake_case   :				Optional[int]        =       f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
    __snake_case   :				List[str]        =       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				):
    __snake_case   :				str        =       f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
    __snake_case   :				List[Any]        =       os.path.join(_A   ,     _A				)
    __snake_case   :				Optional[int]        =       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				):
    __snake_case   :				int        =       f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
    __snake_case   :				Optional[Any]        =       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:
    __snake_case   :				List[Any]        =       os.path.join(_A   ,     _A				)
    scaler.load_state_dict(torch.load(_A				)				)
    logger.info("GradScaler state loaded successfully"				)

  # Random states
  try:
    __snake_case   :				Any        =       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       lowerCAmelCase__(	lowercase       :    Optional[Any]   ,     lowercase       :    Any   ,     lowercase       :    int = 0				) ->    List[Any]:
  __snake_case   :				int        =       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       lowerCAmelCase__(	lowercase       :    int   ,     lowercase       :    Any   ,     lowercase       :    int = 0				) ->    str:
  __snake_case   :				List[str]        =       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"				)				)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase       = {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase       = [
         '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''NatForImageClassification''',
         '''NatModel''',
         '''NatPreTrainedModel''',
         '''NatBackbone''',
     ]

if TYPE_CHECKING:
     from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nat import (
              NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
              NatBackbone,
              NatForImageClassification,
              NatModel,
              NatPreTrainedModel,
          )

else:
     import sys

     _UpperCamelCase       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

