

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any=None      )							->      Optional[int]:





    '''simple docstring'''






    if subparsers is not None:
        A__          =   subparsers.add_parser('env'      )
    else:
        A__          =   argparse.ArgumentParser('Accelerate env command'      )

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

    if subparsers is not None:
        parser.set_defaults(func=SCREAMING_SNAKE_CASE__      )
    return parser
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   torch.__version__
    A__          =   torch.cuda.is_available()
    A__          =   is_xpu_available()
    A__          =   is_npu_available()

    A__          =   'Not found'
    # Get the default from the config file.
    if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE__      ):
        A__          =   load_config_from_file(args.config_file      ).to_dict()

    A__          =   {
        '`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(SCREAMING_SNAKE_CASE__      ),
        'PyTorch NPU available': str(SCREAMING_SNAKE_CASE__      ),
        'System RAM': f'{psutil.virtual_memory().total / 1024 ** 3:.2f} GB',
    }
    if pt_cuda_available:
        A__          =   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:'      )
    A__          =   (
        '\n'.join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]      )
        if isinstance(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else f'\t{accelerate_config}'
    )
    print(SCREAMING_SNAKE_CASE__      )

    A__          =   accelerate_config

    return info
def 						_snake_case(    )							->      int:





    '''simple docstring'''






    A__          =   env_command_parser()
    A__          =   parser.parse_args()
    env_command(SCREAMING_SNAKE_CASE__      )
    return 0


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




from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

