


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_					( __lowerCamelCase=None     ):


							if subparsers is not None:
														__snake_case		:      Tuple										=       subparsers.add_parser("env"     )
							else:
														__snake_case		:      List[str]										=       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_					( __lowerCamelCase     ):
							__snake_case		:      str										=       torch.__version__
							__snake_case		:      int										=       torch.cuda.is_available()
							__snake_case		:      int										=       is_xpu_available()
							__snake_case		:      Tuple										=       is_npu_available()

							__snake_case		:      List[Any]										=       'Not found'
							# Get the default from the config file.
							if args.config_file is not None or os.path.isfile(_UpperCAmelCase     ):
														__snake_case		:      Tuple										=       load_config_from_file(args.config_file     ).to_dict()

							__snake_case		:      Dict										=       {
							    '`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:
														__snake_case		:      Optional[int]										=       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:"     )
							__snake_case		:      str										=       (
							    '\n'.join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()]     )
							    if isinstance(_UpperCAmelCase							,     _UpperCAmelCase     )
							    else F'\t{accelerate_config}'
							)
							print(_UpperCAmelCase     )

							__snake_case		:      Dict										=       accelerate_config

							return info







def     lowerCAmelCase_					( ):
							__snake_case		:      int										=       env_command_parser()
							__snake_case		:      Optional[int]										=       parser.parse_args()
							env_command(_UpperCAmelCase     )
							return 0


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





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       Counter(__lowerCamelCase     ).most_common(1     )[0][0]
							return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

