

'''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				: Union[str, Any] 							=						get_logger(__name__)


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


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Any       ,   SCREAMING_SNAKE_CASE      :   List[str]       ,   SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   Any=None       ,   SCREAMING_SNAKE_CASE      :   Dict=None       ,   **SCREAMING_SNAKE_CASE      :   Dict       ,   ):
  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:
    UpperCAmelCase													=	'cpu'
  elif map_location == "on_device":
    UpperCAmelCase													=	PartialState().device
  # Model states
  for i, model in enumerate(SCREAMING_SNAKE_CASE						):
    UpperCAmelCase													=	f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
    UpperCAmelCase													=	os.path.join(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    models[i].load_state_dict(torch.load(SCREAMING_SNAKE_CASE       ,   map_location=SCREAMING_SNAKE_CASE						)       ,   **SCREAMING_SNAKE_CASE						)
  logger.info('All model weights loaded successfully'						)

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

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

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

  # Random states
  try:
    UpperCAmelCase													=	torch.load(os.path.join(SCREAMING_SNAKE_CASE       ,   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__		(						SCREAMING_SNAKE_CASE      :   Optional[Any]       ,   SCREAMING_SNAKE_CASE      :   int       ,   SCREAMING_SNAKE_CASE      :   int = 0						):
  UpperCAmelCase													=	Path(SCREAMING_SNAKE_CASE						) / f'''custom_checkpoint_{index}.pkl'''
  logger.info(f'''Saving the state of {get_pretty_name(SCREAMING_SNAKE_CASE						)} to {save_location}'''						)
  torch.save(obj.state_dict()       ,   SCREAMING_SNAKE_CASE						)



def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Union[str, Any]       ,   SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   int = 0						):
  UpperCAmelCase													=	f'''{path}/custom_checkpoint_{index}.pkl'''
  logger.info(f'''Loading the state of {get_pretty_name(SCREAMING_SNAKE_CASE						)} from {load_location}'''						)
  obj.load_state_dict(torch.load(SCREAMING_SNAKE_CASE       ,   map_location='cpu'						)						)





'''simple docstring'''




from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[Any]						):
  if is_torch_available():
    import torch

    if isinstance(SCREAMING_SNAKE_CASE       ,   torch.Tensor						):
      return True

  return isinstance(SCREAMING_SNAKE_CASE       ,   np.ndarray						)



class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	fields(self     )

    # Safety and consistency checks
    if not len(a_     ):
      raise ValueError(F'''{self.__class__.__name__} has no fields.'''     )

    UpperCAmelCase													=	getattr(self   , class_fields[0].name     )
    UpperCAmelCase													=	all(getattr(self   , field.name     ) is None for field in class_fields[1:]     )

    if other_fields_are_none and isinstance(a_   , a_     ):
      for key, value in first_field.items():
        UpperCAmelCase													=	value
    else:
      for field in class_fields:
        UpperCAmelCase													=	getattr(self   , field.name     )
        if v is not None:
          UpperCAmelCase													=	v
  def __delitem__(     self   , *a_   , **a_     )							->				int:


    """simple docstring"""
    raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Tuple:


    """simple docstring"""
    raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				int:


    """simple docstring"""
    raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Dict:


    """simple docstring"""
    raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''     )
  def __getitem__(     self   , a_     )							->				Tuple:


    """simple docstring"""
    if isinstance(a_   , a_     ):
      UpperCAmelCase													=	dict(self.items()     )
      return inner_dict[k]
    else:
      return self.to_tuple()[k]
  def __setattr__(     self   , a_   , a_     )							->				Dict:


    """simple docstring"""
    if name in self.keys() and value is not None:
      # Don't call self.__setitem__ to avoid recursion errors
      super().__setitem__(a_   , a_     )
    super().__setattr__(a_   , a_     )
  def __setitem__(     self   , a_   , a_     )							->				str:


    """simple docstring"""
    # Will raise a KeyException if needed
    super().__setitem__(a_   , a_     )
    # Don't call self.__setattr__ to avoid recursion errors
    super().__setattr__(a_   , a_     )





  def 						snake_case_			(     self     )							->				Tuple[Any]:


    """simple docstring"""
    return tuple(self[k] for k in self.keys()     )


