





# 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       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]=None			)   ->							Tuple:




		'''simple docstring'''
		if subparsers is not None:
				lowercase__					:      Dict      =		subparsers.add_parser('env'			)
		else:
				lowercase__					:      Any      =		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       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__					:      Dict      =		torch.__version__
		lowercase__					:      Optional[Any]      =		torch.cuda.is_available()
		lowercase__					:      Dict      =		is_xpu_available()
		lowercase__					:      Any      =		is_npu_available()

		lowercase__					:      int      =		'Not found'
		# Get the default from the config file.
		if args.config_file is not None or os.path.isfile(_lowercase			):
				lowercase__					:      Union[str, Any]      =		load_config_from_file(args.config_file			).to_dict()

		lowercase__					:      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(_lowercase			),
		    'PyTorch NPU available': str(_lowercase			),
		    'System RAM': f"""{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB""",
		}
		if pt_cuda_available:
				lowercase__					:      Optional[Any]      =		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:'			)
		lowercase__					:      Tuple      =		(
		    '\n'.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]			)
		    if isinstance(_lowercase   ,    _lowercase			)
		    else f"""\t{accelerate_config}"""
		)
		print(_lowercase			)

		lowercase__					:      int      =		accelerate_config

		return info


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		env_command_parser()
		lowercase__					:      str      =		parser.parse_args()
		env_command(_lowercase			)
		return 0


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









import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						List[str],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						int			):
			if dst_width < 0 or dst_height < 0:
					raise ValueError('Destination width/height should be > 0'			)

			lowercase__					:      str      =		img
			lowercase__					:      Tuple      =		img.shape[1]
			lowercase__					:      str      =		img.shape[0]
			lowercase__					:      Tuple      =		dst_width
			lowercase__					:      Any      =		dst_height

			lowercase__					:      Tuple      =		self.src_w / self.dst_w
			lowercase__					:      Union[str, Any]      =		self.src_h / self.dst_h

			lowercase__					:      List[Any]      =		(
			    np.ones((self.dst_h, self.dst_w, 3),					np.uinta			) * 255
			)

	def    snake_case__(						self:						Dict			):
			for i in range(self.dst_h			):
					for j in range(self.dst_w			):
							lowercase__					:      Tuple      =		self.img[self.get_y(lowerCamelCase_			)][self.get_x(lowerCamelCase_			)]

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						int			):
			return int(self.ratio_x * x			)





	def    snake_case__(						self:						int,					lowerCamelCase_:						int			):
			return int(self.ratio_y * y			)


if __name__ == "__main__":
			__UpperCamelCase,	__UpperCamelCase:	List[Any] = 8_0_0, 6_0_0
			__UpperCamelCase:	Optional[Any] = imread("""image_data/lena.jpg""", 1)
			__UpperCamelCase:	Union[str, Any] = NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
			)
			waitKey(0)
			destroyAllWindows()


