

# 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	(      SCREAMING_SNAKE_CASE=None      ):
	if subparsers is not None:
		UpperCAmelCase__:	List[str]									= subparsers.add_parser("env"      )
	else:
		UpperCAmelCase__:	List[str]									= argparse.ArgumentParser("Accelerate env command"      )

	parser.add_argument(
	    "--config_file"	,default=SCREAMING_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=SCREAMING_SNAKE_CASE      )
	return parser



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Any									= torch.__version__
	UpperCAmelCase__:	Any									= torch.cuda.is_available()
	UpperCAmelCase__:	Optional[Any]									= is_xpu_available()
	UpperCAmelCase__:	List[str]									= is_npu_available()

	UpperCAmelCase__:	List[str]									= "Not found"
	# Get the default from the config file.
	if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	List[str]									= load_config_from_file(args.config_file      ).to_dict()

	UpperCAmelCase__:	str									= {
	    "`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(SCREAMING_SNAKE_CASE      ),
	    "PyTorch NPU available": str(SCREAMING_SNAKE_CASE      ),
	    "System RAM": f"{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB",
	}
	if pt_cuda_available:
		UpperCAmelCase__:	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:"      )
	UpperCAmelCase__:	List[str]									= (
	    "\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()]      )
	    if isinstance(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	    else f"\t{accelerate_config}"
	)
	print(SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	Union[str, Any]									= accelerate_config

	return info



def 			_A	(      ):
	UpperCAmelCase__:	Dict									= env_command_parser()
	UpperCAmelCase__:	int									= parser.parse_args()
	env_command(SCREAMING_SNAKE_CASE      )
	return 0


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


def 			_A	(      SCREAMING_SNAKE_CASE      ):  # noqa: E741
	UpperCAmelCase__:	int									= len(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= 0
	UpperCAmelCase__:	Optional[int]									= [0] * n
	UpperCAmelCase__:	List[str]									= [False] * n
	UpperCAmelCase__:	List[str]									= [False] * n

	def dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
		if parent == root:
			out_edge_count += 1
		UpperCAmelCase__:	List[str]									= True
		UpperCAmelCase__:	List[Any]									= at

		for to in l[at]:
			if to == parent:
				pass
			elif not visited[to]:
				UpperCAmelCase__:	str									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
				UpperCAmelCase__:	Optional[Any]									= min(low[at]	,low[to]      )

				# AP found via bridge
				if at < low[to]:
					UpperCAmelCase__:	List[Any]									= True
				# AP found via cycle
				if at == low[to]:
					UpperCAmelCase__:	str									= True
			else:
				UpperCAmelCase__:	Union[str, Any]									= min(low[at]	,SCREAMING_SNAKE_CASE      )
		return out_edge_count

	for i in range(SCREAMING_SNAKE_CASE      ):
		if not visited[i]:
			UpperCAmelCase__:	Optional[Any]									= 0
			UpperCAmelCase__:	Union[str, Any]									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,-1	,SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Tuple									= out_edge_count > 1

	for x in range(len(SCREAMING_SNAKE_CASE      )      ):
		if is_art[x] is True:
			print(SCREAMING_SNAKE_CASE      )


# Adjacency list of graph
_lowerCAmelCase  :      Any			 ={
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}
compute_ap(data)