

'''simple docstring'''



# 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   (	snake_case      : Any=None			) ->  Any:


    """simple docstring"""


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

    parser.add_argument(
        '--config_file'					,		default=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=snake_case			)
    return parser


def       snake_case   (	snake_case      : Dict			) ->  int:


    """simple docstring"""


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

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

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

    lowerCAmelCase 						=							accelerate_config

    return info


def       snake_case   (	) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							env_command_parser()
    lowerCAmelCase 						=							parser.parse_args()
    env_command(snake_case			)
    return 0


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








'''simple docstring'''



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCamelCase						:  str							       =				{
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Dict							       =				[
         "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoXJapaneseForCausalLM",
         "GPTNeoXJapaneseLayer",
         "GPTNeoXJapaneseModel",
         "GPTNeoXJapanesePreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
     from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neox_japanese import (
              GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoXJapaneseForCausalLM,
              GPTNeoXJapaneseLayer,
              GPTNeoXJapaneseModel,
              GPTNeoXJapanesePreTrainedModel,
          )


else:
     import sys

     _UpperCamelCase						:  List[Any]							       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





