

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


_SCREAMING_SNAKE_CASE          = get_logger(__name__)





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





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

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

  # Scheduler states
  for i, scheduler in enumerate(snake_case_     ):
    __UpperCAmelCase  :     List[str]		=    F"{SCHEDULER_NAME}.bin" if i == 0 else F"{SCHEDULER_NAME}_{i}.bin"
    __UpperCAmelCase  :     Any		=    os.path.join(snake_case_						,					snake_case_     )
    scheduler.load_state_dict(torch.load(snake_case_     )     )
  logger.info('All scheduler states loaded successfully'     )

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

  # Random states
  try:
    __UpperCAmelCase  :     Optional[Any]		=    torch.load(os.path.join(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    _snake_case		(snake_case_				:    Dict						,					snake_case_				:    Tuple						,					snake_case_				:    int = 0     )       ->		List[Any]:
  __UpperCAmelCase  :     str		=    Path(snake_case_     ) / F"custom_checkpoint_{index}.pkl"
  logger.info(F"Saving the state of {get_pretty_name(snake_case_     )} to {save_location}"     )
  torch.save(obj.state_dict()						,					snake_case_     )







def    _snake_case		(snake_case_				:    Optional[Any]						,					snake_case_				:    Any						,					snake_case_				:    int = 0     )       ->		Tuple:
  __UpperCAmelCase  :     Dict		=    F"{path}/custom_checkpoint_{index}.pkl"
  logger.info(F"Loading the state of {get_pretty_name(snake_case_     )} from {load_location}"     )
  obj.load_state_dict(torch.load(snake_case_						,					map_location='cpu'     )     )








from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any


class     lowerCamelCase_   :
 _lowerCAmelCase       		=  ('''_front''', '''_back''', '''_len''')


 @dataclass
 class     lowerCamelCase_   :
  _lowerCAmelCase       		=  None
  _lowerCAmelCase       		=  None
  _lowerCAmelCase       		=  None



 class     lowerCamelCase_   :
  _lowerCAmelCase       		=  ('''_cur''',)




  def __init__( self     ,     _UpperCAmelCase       ):
    __UpperCAmelCase  :     Tuple		=    cur




  def __iter__( self       ):
    return self




  def 						a					( self       ):
    if self._cur is None:
      # finished iterating
      raise StopIteration
    __UpperCAmelCase  :     str		=    self._cur.val
    __UpperCAmelCase  :     Dict		=    self._cur.next_node

    return val




 def __init__( self     ,     _UpperCAmelCase = None       ):
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     int		=    0

   if iterable is not None:
     # append every value to the deque
     for val in iterable:
       self.append(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    self._Node(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
   if self.is_empty():
     # front = back
     __UpperCAmelCase  :     Dict		=    node
     __UpperCAmelCase  :     List[str]		=    1
   else:
     # connect nodes
     __UpperCAmelCase  :     Any		=    node
     __UpperCAmelCase  :     Tuple		=    self._back
     __UpperCAmelCase  :     Optional[Any]		=    node  # assign new back to the new node

     self._len += 1

     # make sure there were no errors
     assert not self.is_empty(), "Error on appending value."




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    self._Node(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
   if self.is_empty():
     # front = back
     __UpperCAmelCase  :     str		=    node
     __UpperCAmelCase  :     List[str]		=    1
   else:
     # connect nodes
     __UpperCAmelCase  :     List[str]		=    self._front
     __UpperCAmelCase  :     Any		=    node
     __UpperCAmelCase  :     Dict		=    node  # assign new front to the new node

     self._len += 1

     # make sure there were no errors
     assert not self.is_empty(), "Error on appending value."




 def 						a					( self     ,     _UpperCAmelCase       ):
   for val in iterable:
     self.append(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   for val in iterable:
     self.appendleft(_UpperCAmelCase       )




 def 						a					( self       ):
   assert not self.is_empty(), "Deque is empty."

   __UpperCAmelCase  :     List[Any]		=    self._back
   __UpperCAmelCase  :     Optional[int]		=    self._back.prev_node  # set new back
   # drop the last node - python will deallocate memory automatically
   __UpperCAmelCase  :     int		=    None

   self._len -= 1

   return topop.val




 def 						a					( self       ):
   assert not self.is_empty(), "Deque is empty."

   __UpperCAmelCase  :     List[Any]		=    self._front
   __UpperCAmelCase  :     Union[str, Any]		=    self._front.next_node  # set new front and drop the first node
   __UpperCAmelCase  :     Union[str, Any]		=    None

   self._len -= 1

   return topop.val




 def 						a					( self       ):
   return self._front is None




 def __len__( self       ):
   return self._len




 def __eq__( self     ,     _UpperCAmelCase       ):

   if not isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
     return NotImplemented

   __UpperCAmelCase  :     List[str]		=    self._front
   __UpperCAmelCase  :     str		=    other._front

   # if the length of the dequeues are not the same, they are not equal
   if len(self       ) != len(_UpperCAmelCase       ):
     return False

   while me is not None and oth is not None:
     # compare every value
     if me.val != oth.val:
       return False
     __UpperCAmelCase  :     Any		=    me.next_node
     __UpperCAmelCase  :     str		=    oth.next_node

   return True




 def __iter__( self       ):
   return Deque._Iterator(self._front       )




 def __repr__( self       ):
   __UpperCAmelCase  :     int		=    []
   __UpperCAmelCase  :     Dict		=    self._front
   while aux is not None:
     # append the values in a list to display
     values_list.append(aux.val       )
     __UpperCAmelCase  :     List[str]		=    aux.next_node

   return F"[{', '.join(repr(_UpperCAmelCase       ) for val in values_list       )}]"


if __name__ == "__main__":
      import doctest

      doctest.testmod()





