





import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available


def 							SCREAMING_SNAKE_CASE__  (      __a=None				):
	if subparsers is not None:
		snake_case_						:     Optional[int]     =		subparsers.add_parser('env'				)
	else:
		snake_case_						:     List[Any]     =		argparse.ArgumentParser('Accelerate env command'				)

	parser.add_argument(
	    '--config_file'				,	default=_UpperCAmelCase				,	help='The config file to use for the default values in the launching script.'				)

	if subparsers is not None:
		parser.set_defaults(func=_UpperCAmelCase				)
	return parser


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Dict     =		torch.__version__
	snake_case_						:     str     =		torch.cuda.is_available()
	snake_case_						:     str     =		is_xpu_available()
	snake_case_						:     List[Any]     =		is_npu_available()

	snake_case_						:     Union[str, Any]     =		"Not found"
	# Get the default from the config file.
	if args.config_file is not None or os.path.isfile(_UpperCAmelCase				):
		snake_case_						:     Union[str, Any]     =		load_config_from_file(args.config_file				).to_dict()

	snake_case_						:     List[str]     =		{
	    "`Accelerate` version": version,
	    "Platform": platform.platform(),
	    "Python version": platform.python_version(),
	    "Numpy version": np.__version__,
	    "PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
	    "PyTorch XPU available": str(_UpperCAmelCase				),
	    "PyTorch NPU available": str(_UpperCAmelCase				),
	    "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
	}
	if pt_cuda_available:
		snake_case_						:     int     =		torch.cuda.get_device_name()

	print('\nCopy-and-paste the text below in your GitHub issue\n'				)
	print('\n'.join([f"""- {prop}: {val}""" for prop, val in info.items()]				)				)

	print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:'				)
	snake_case_						:     Tuple     =		(
	    "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]				)
	    if isinstance(_UpperCAmelCase				,	_UpperCAmelCase				)
	    else f"""\t{accelerate_config}"""
	)
	print(_UpperCAmelCase				)

	snake_case_						:     Any     =		accelerate_config

	return info


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Tuple     =		env_command_parser()
	snake_case_						:     Dict     =		parser.parse_args()
	env_command(_UpperCAmelCase				)
	return 0


if __name__ == "__main__":
						raise SystemExit(main())









import warnings

from ..trainer import Trainer
from ..utils import logging


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def __init__( self : Any						,				_A : str=None						,				**_A : Union[str, Any]		)							->      Any:







							"""simple docstring"""


							warnings.warn(
							    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
							    'instead.'						,				_A						,				)
							super().__init__(args=_A						,				**_A		)

