



# 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     (      _UpperCAmelCase=None    ):
				if subparsers is not None:
								__a							=    subparsers.add_parser('''env'''    )
				else:
								__a							=    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 					__snake_case     (      _UpperCAmelCase    ):
				__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(_UpperCAmelCase    ):
								__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(_UpperCAmelCase    ),
				    '''PyTorch NPU available''': str(_UpperCAmelCase    ),
				    '''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(_UpperCAmelCase       ,			_UpperCAmelCase    )
				    else f'\t{accelerate_config}'
				)
				print(_UpperCAmelCase    )

				__a							=    accelerate_config

				return info





def 					__snake_case     (      ):
				__a							=    env_command_parser()
				__a							=    parser.parse_args()
				env_command(_UpperCAmelCase    )
				return 0


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










def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):

    def get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    ) -> str:
        __a							=    []
        __a							=    min(len(_stra    )       ,			len(_stra    )    ) // 2
        for i, l in enumerate(_stra    ):
            __a							=    int(max(0       ,			i - limit    )    )
            __a							=    int(min(i + limit + 1       ,			len(_stra    )    )    )
            if l in _stra[left:right]:
                matched.append(_UpperCAmelCase    )
                __a							=    f'{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}'

        return "".join(_UpperCAmelCase    )

    # matching characters
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    len(_UpperCAmelCase    )

    # transposition
    __a							=    (
        len([(ca, ca) for ca, ca in zip(_UpperCAmelCase       ,			_UpperCAmelCase    ) if ca != ca]    ) // 2
    )

    if not match_count:
        __a							=    0.0
    else:
        __a							=    (
            1
            / 3
            * (
                match_count / len(_UpperCAmelCase    )
                + match_count / len(_UpperCAmelCase    )
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __a							=    0
    for ca, ca in zip(stra[:4]       ,			stra[:4]    ):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(jaro_winkler('''hello''', '''world'''))





