

"""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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ = None     ,				):
					"""simple docstring"""
					# Model states
					for i, state in enumerate(lowercase__ ):
										A     =   F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
										A     =   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__ ):
										A     =   opt.state_dict()
										A     =   F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
										A     =   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__ ):
										A     =   scheduler.state_dict()
										A     =   F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
										A     =   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:
										A     =   scaler.state_dict()
										A     =   os.path.join(lowercase__     ,				lowercase__ )
										torch.save(lowercase__     ,				lowercase__ )
										logger.info(F"""Gradient scaler state saved in {output_scaler_file}""" )
					# Random number generator states
					A     =   {}
					A     =   F"""{RNG_STATE_NAME}_{process_index}.pkl"""
					A     =   random.getstate()
					A     =   np.random.get_state()
					A     =   torch.get_rng_state()
					if is_xpu_available():
										A     =   torch.xpu.get_rng_state_all()
					else:
										A     =   torch.cuda.get_rng_state_all()
					if is_tpu_available():
										A     =   xm.get_rng_state()
					A     =   os.path.join(lowercase__     ,				lowercase__ )
					torch.save(lowercase__     ,				lowercase__ )
					logger.info(F"""Random states saved in {output_states_file}""" )
					return output_dir
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=None     ,				**lowercase__     ,				):
					"""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:
										A     =   "cpu"
					elif map_location == "on_device":
										A     =   PartialState().device
					# Model states
					for i, model in enumerate(lowercase__ ):
										A     =   F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
										A     =   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__ ):
										A     =   F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
										A     =   os.path.join(lowercase__     ,				lowercase__ )
										A     =   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__ ):
										A     =   F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
										A     =   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:
										A     =   os.path.join(lowercase__     ,				lowercase__ )
										scaler.load_state_dict(torch.load(lowercase__ ) )
										logger.info("GradScaler state loaded successfully" )

					# Random states
					try:
										A     =   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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ = 0 ):
					"""simple docstring"""
					A     =   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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ = 0 ):
					"""simple docstring"""
					A     =   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" ) )











def  a_		(							__snake_case					)	->			str:

			'''simple docstring'''


			return " ".join([word[::-1] for word in input_str.split()]					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
