



# 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 		lowerCAmelCase__     (       UpperCamelCase_					:							Any=None						)->	Dict:
    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 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	Union[str, Any]:
    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 / 1_0_2_4 ** 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 		lowerCAmelCase__     (       )->	int:
    A__					=       env_command_parser()
    A__					=       parser.parse_args()
    env_command(UpperCamelCase_						)
    return 0


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





from typing import TYPE_CHECKING

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


_lowercase    	=		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
