from ..utils import requires_backends


__snake_case         =     None







class       __snake_case :
 def __init__(	self		, *snake_case__		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       requires_backends(self		, ['''detectron2''']    )






 @classmethod
 def 						UpperCAmelCase__							(	cls		, *snake_case__		, **snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       requires_backends(cls		, ['''detectron2''']    )




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					:	int          =				get_logger(__name__)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase = None      ,							):
   # Model states
   for i, state in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= 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							):
      UpperCAmelCase_				:   Tuple							= opt.state_dict()
      UpperCAmelCase_				:   Any							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= 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							):
      UpperCAmelCase_				:   Tuple							= scheduler.state_dict()
      UpperCAmelCase_				:   Tuple							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   Tuple							= 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:
      UpperCAmelCase_				:   Union[str, Any]							= scaler.state_dict()
      UpperCAmelCase_				:   Union[str, Any]							= os.path.join(__lowercase      ,							__lowercase							)
      torch.save(__lowercase      ,							__lowercase							)
      logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''							)
   # Random number generator states
   UpperCAmelCase_				:   Union[str, Any]							= {}
   UpperCAmelCase_				:   Tuple							= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
   UpperCAmelCase_				:   List[Any]							= random.getstate()
   UpperCAmelCase_				:   Any							= np.random.get_state()
   UpperCAmelCase_				:   List[Any]							= torch.get_rng_state()
   if is_xpu_available():
      UpperCAmelCase_				:   Optional[int]							= torch.xpu.get_rng_state_all()
   else:
      UpperCAmelCase_				:   Any							= torch.cuda.get_rng_state_all()
   if is_tpu_available():
      UpperCAmelCase_				:   List[str]							= xm.get_rng_state()
   UpperCAmelCase_				:   Any							= os.path.join(__lowercase      ,							__lowercase							)
   torch.save(__lowercase      ,							__lowercase							)
   logger.info(F'''Random states saved in {output_states_file}'''							)
   return output_dir
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase=None      ,							__lowercase=None      ,							**__lowercase      ,							):
   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_				:   Optional[Any]							= '''cpu'''
   elif map_location == "on_device":
      UpperCAmelCase_				:   Optional[int]							= PartialState().device
   # Model states
   for i, model in enumerate(__lowercase							):
      UpperCAmelCase_				:   List[Any]							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= 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							):
      UpperCAmelCase_				:   List[str]							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      UpperCAmelCase_				:   Tuple							= 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							):
      UpperCAmelCase_				:   Dict							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= 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:
      UpperCAmelCase_				:   List[str]							= os.path.join(__lowercase      ,							__lowercase							)
      scaler.load_state_dict(torch.load(__lowercase							)							)
      logger.info('''GradScaler state loaded successfully'''							)

   # Random states
   try:
      UpperCAmelCase_				:   Optional[Any]							= 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   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Optional[int]							= 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   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Dict							= 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'''							)							)