





'''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 						__lowerCamelCase			(	UpperCAmelCase_=None							)   ->Any:





	if subparsers is not None:
		snake_case__	       =   subparsers.add_parser('env'							)
	else:
		snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Dict:
	snake_case__	       =   torch.__version__
	snake_case__	       =   torch.cuda.is_available()
	snake_case__	       =   is_xpu_available()
	snake_case__	       =   is_npu_available()

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

	snake_case__	       =   {
	    '`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 / 10_24 ** 3:.2f} GB''',
	}
	if pt_cuda_available:
		snake_case__	       =   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__	       =   (
	    '\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__							)

	snake_case__	       =   accelerate_config

	return info




def 						__lowerCamelCase			(	)   ->int:
	snake_case__	       =   env_command_parser()
	snake_case__	       =   parser.parse_args()
	env_command(snake_case__							)
	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

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).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]))
