



# 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 = 1000000    ):
    __a							=    limit + 1
    __a							=    [0] * limit
    for first_term in range(1       ,			_UpperCAmelCase    ):
        for n in range(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
            __a							=    first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    __a							=    sum(1 for x in frequency[1:limit] if x == 10    )

    return count


if __name__ == "__main__":
 print(f'{solution() = }')





