



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__    (				_SCREAMING_SNAKE_CASE							:      List[str]=None     ):
				"""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=__A					,  help="""The config file to use for the default values in the launching script."""     )

				if subparsers is not None:
								parser.set_defaults(func=__A     )
				return parser

def 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      List[str]     ):
				"""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(__A     ):
								__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(__A     ),
				    'PyTorch NPU available': str(__A     ),
				    '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(__A					,  __A     )
				    else f"\t{accelerate_config}"
				)
				print(__A     )

				__a	  =		accelerate_config

				return info

def 	lowerCAmelCase__    (				):
				"""simple docstring"""







				__a	  =		env_command_parser()
				__a	  =		parser.parse_args()
				env_command(__A     )
				return 0


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





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_						:    str				=      {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Optional[int]				=      [
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A_						:    int				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





