

# 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      A__					(			__A  :						List[Any]=None			)					->Tuple:
      if subparsers is not None:
            __A    =subparsers.add_parser('''env'''			)
      else:
            __A    =argparse.ArgumentParser('''Accelerate env command'''			)

      parser.add_argument(
          '''--config_file'''     ,				default=_lowercase     ,				help='''The config file to use for the default values in the launching script.'''			)

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





def      A__					(			__A  :						Optional[int]			)					->Optional[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(_lowercase			):
            __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(_lowercase			),
          'PyTorch NPU available': str(_lowercase			),
          'System RAM': F'''{psutil.virtual_memory().total / 10_24 ** 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(_lowercase     ,				_lowercase			)
          else F'''\t{accelerate_config}'''
      )
      print(_lowercase			)

      __A    =accelerate_config

      return info





def      A__					(			)					->int:
      __A    =env_command_parser()
      __A    =parser.parse_args()
      env_command(_lowercase			)
      return 0


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









# Copyright 2023 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase:	List[Any] = {
    """configuration_efficientnet""": [
        """EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientNetConfig""",
        """EfficientNetOnnxConfig""",
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = ["""EfficientNetImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = [
			    """EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """EfficientNetForImageClassification""",
			    """EfficientNetModel""",
			    """EfficientNetPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_efficientnet import (
			    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    EfficientNetConfig,
			    EfficientNetOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_efficientnet import EfficientNetImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_efficientnet import (
						    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    EfficientNetForImageClassification,
						    EfficientNetModel,
						    EfficientNetPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:	List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)


