






"""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


lowerCAmelCase__							= get_logger(__name__)





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





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

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

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

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

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






def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase = 0					):
				_SCREAMING_SNAKE_CASE      :		Any							=						f"""{path}/custom_checkpoint_{index}.pkl"""
				logger.info(f"""Loading the state of {get_pretty_name(lowerCamelCase					)} from {load_location}"""					)
				obj.load_state_dict(torch.load(lowerCamelCase,	map_location='cpu'					)					)









"""simple docstring"""
import json
import logging
import os
import subprocess
from argparse import ArgumentParser


lowerCAmelCase__							= logging.getLogger(__name__)





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						ArgumentParser()
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						parser.parse_known_args()
				for arg in unknown:
								if arg.startswith(('-', '--')					):
												parser.add_argument(arg.split('='					)[0]					)

				return parser.parse_args()





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						parse_args()
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						8_888
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						int(os.environ['SM_NUM_GPUS']					)
				_SCREAMING_SNAKE_CASE      :		Any							=						json.loads(os.environ['SM_HOSTS']					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						len(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Dict							=						os.environ['SM_CURRENT_HOST']
				_SCREAMING_SNAKE_CASE      :		List[str]							=						hosts.index(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						'INFO'

				if num_nodes > 1:
								_SCREAMING_SNAKE_CASE      :		Dict							=						f"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]					)}"""
				else:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						f"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]					)}"""
				try:
								subprocess.run(lowerCamelCase,	shell=lowerCamelCase					)
				except Exception as e:
								logger.info(lowerCamelCase					)


if __name__ == "__main__":
			main()

