





'''simple docstring'''



# Copyright 2021 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 pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_SCREAMING_SNAKE_CASE   =      '''Create a default config file for Accelerate with only a few flags set.'''




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str="no"						,    lowerCamelCase_     :					str = default_json_config_file						,    lowerCamelCase_     :					bool = False			):
			__lowercase			  =							Path(lowerCamelCase_			)
			path.parent.mkdir(parents=lowerCamelCase_						,    exist_ok=lowerCamelCase_			)
			if path.exists():
						print(
						    f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."			)
						return False
			__lowercase			  =							mixed_precision.lower()
			if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
						raise ValueError(
						    f"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"			)
			__lowercase			  =							{
			    '''compute_environment''': '''LOCAL_MACHINE''',
			    '''mixed_precision''': mixed_precision,
			}
			if torch.cuda.is_available():
						__lowercase			  =							torch.cuda.device_count()
						__lowercase			  =							num_gpus
						__lowercase			  =							False
						if num_gpus > 1:
									__lowercase			  =							'''MULTI_GPU'''
						else:
									__lowercase			  =							'''NO'''
			elif is_xpu_available() and use_xpu:
						__lowercase			  =							torch.xpu.device_count()
						__lowercase			  =							num_xpus
						__lowercase			  =							False
						if num_xpus > 1:
									__lowercase			  =							'''MULTI_XPU'''
						else:
									__lowercase			  =							'''NO'''
			elif is_npu_available():
						__lowercase			  =							torch.npu.device_count()
						__lowercase			  =							num_npus
						__lowercase			  =							False
						if num_npus > 1:
									__lowercase			  =							'''MULTI_NPU'''
						else:
									__lowercase			  =							'''NO'''
			else:
						__lowercase			  =							0
						__lowercase			  =							True
						__lowercase			  =							1
						__lowercase			  =							'''NO'''
			__lowercase			  =							ClusterConfig(**lowerCamelCase_			)
			config.to_json_file(lowerCamelCase_			)
			return path




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any						,    lowerCamelCase_     :					Tuple			):
			__lowercase			  =							parser.add_parser('''default'''						,    parents=lowerCamelCase_						,    help=lowerCamelCase_						,    formatter_class=lowerCamelCase_			)
			parser.add_argument(
			    '''--config_file'''						,    default=lowerCamelCase_						,    help=(
			        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
			        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
			        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
			        '''with \'huggingface\'.'''
			    )						,    dest='''save_location'''						,    )

			parser.add_argument(
			    '''--mixed_precision'''						,    choices=['''no''', '''fp16''', '''bf16''']						,    type=lowerCamelCase_						,    help='''Whether or not to use mixed precision training. '''
			    '''Choose between FP16 and BF16 (bfloat16) training. '''
			    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''						,    default='''no'''						,    )
			parser.set_defaults(func=lowerCamelCase_			)
			return parser




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[Any]			):
			__lowercase			  =							write_basic_config(args.mixed_precision						,    args.save_location			)
			if config_file:
						print(f"accelerate configuration saved at {config_file}"			)












'''simple docstring'''



import heapq




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict			):
			__lowercase			  =							[]

			# for each node and his adjacency list add them and the rank of the node to queue
			# using heapq module the queue will be filled like a Priority Queue
			# heapq works with a min priority queue, so I used -1*len(v) to build it
			for key, value in graph.items():
						# O(log(n))
						heapq.heappush(lowerCamelCase_						,    [-1 * len(lowerCamelCase_			), (key, value)]			)

			# chosen_vertices = set of chosen vertices
			__lowercase			  =							set()

			# while queue isn't empty and there are still edges
			#   (queue[0][0] is the rank of the node with max rank)
			while queue and queue[0][0] != 0:
						# extract vertex with max rank from queue and add it to chosen_vertices
						__lowercase			  =							heapq.heappop(lowerCamelCase_			)[1][0]
						chosen_vertices.add(lowerCamelCase_			)

						# Remove all arcs adjacent to argmax
						for elem in queue:
									# if v haven't adjacent node, skip
									if elem[0] == 0:
												continue
									# if argmax is reachable from elem
									# remove argmax from elem's adjacent list and update his rank
									if argmax in elem[1][1]:
												__lowercase			  =							elem[1][1].index(lowerCamelCase_			)
												del elem[1][1][index]
												elem[0] += 1
        # re-order the queue
						heapq.heapify(lowerCamelCase_			)
			return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_SCREAMING_SNAKE_CASE   =      {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')





