


from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
__lowerCamelCase       :       Union[str, Any]				   =			{
    "1": ["2", "3", "4", "5"],
    "2": ["1", "3", "4", "5"],
    "3": ["1", "2", "4", "5", "10"],
    "4": ["1", "2", "3", "5", "6"],
    "5": ["1", "2", "3", "4", "7"],
    "6": ["7", "8", "9", "10", "4"],
    "7": ["6", "8", "9", "10", "5"],
    "8": ["6", "7", "9", "10"],
    "9": ["6", "7", "8", "10"],
    "10": ["6", "7", "8", "9", "3"],
}



def 	SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Optional[int]:
				A__     :							Any   				={node: {node} for node in graph}

				A__     :							str   				={node: graph[node][:] for node in graph}

				while len(snake_case_	) > 2:
								# Choose a random edge.
								A__     :							Union[str, Any]   				=random.choice(list(graph_copy.keys()	)	)
								A__     :							Optional[Any]   				=random.choice(graph_copy[u]	)

								# Contract edge (u, v) to new node uv
								A__     :							Tuple   				=u + v
								A__     :							Optional[int]   				=list(set(graph_copy[u] + graph_copy[v]	)	)
								uv_neighbors.remove(snake_case_	)
								uv_neighbors.remove(snake_case_	)
								A__     :							List[str]   				=uv_neighbors
								for neighbor in uv_neighbors:
												graph_copy[neighbor].append(snake_case_	)

								A__     :							Any   				=set(contracted_nodes[u].union(contracted_nodes[v]	)	)

								# Remove nodes u and v.
								del graph_copy[u]
								del graph_copy[v]
								for neighbor in uv_neighbors:
												if u in graph_copy[neighbor]:
																graph_copy[neighbor].remove(snake_case_	)
												if v in graph_copy[neighbor]:
																graph_copy[neighbor].remove(snake_case_	)

    # Find cutset.
				A__     :							Optional[int]   				=[contracted_nodes[node] for node in graph_copy]
				return {
				    (node, neighbor)
				    for node in groups[0]
				    for neighbor in graph[node]
				    if neighbor in groups[1]
				}


if __name__ == "__main__":
						print(partition_graph(TEST_GRAPH))







from __future__ import annotations



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							list[int]:
				A__     :							Optional[int]   				=[0] * len(snake_case_	)
				for i in range(1,	len(snake_case_	)	):
								A__     :							List[Any]   				=duration_times[i - 1] + waiting_times[i - 1]
				return waiting_times



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							list[int]:
				return [
				    duration_time + waiting_times[i]
				    for i, duration_time in enumerate(snake_case_	)
				]



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
				return sum(snake_case_	) / len(snake_case_	)



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
				return sum(snake_case_	) / len(snake_case_	)


if __name__ == "__main__":
						# process id's
						__lowerCamelCase       :       List[str]				   =			[1, 2, 3]

						# ensure that we actually have processes
						if len(processes) == 0:
												print("Zero amount of processes")
												raise SystemExit(0)

						# duration time of all processes
						__lowerCamelCase       :       Dict				   =			[19, 8, 9]

						# ensure we can match each id to a duration time
						if len(duration_times) != len(processes):
												print("Unable to match all id's with their duration time")
												raise SystemExit(0)

						# get the waiting times and the turnaround times
						__lowerCamelCase       :       int				   =			calculate_waiting_times(duration_times)
						__lowerCamelCase       :       Optional[int]				   =			calculate_turnaround_times(duration_times, waiting_times)

						# get the average times
						__lowerCamelCase       :       List[str]				   =			calculate_average_waiting_time(waiting_times)
						__lowerCamelCase       :       Dict				   =			calculate_average_turnaround_time(turnaround_times)

						# print all the results
						print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
						for i, process in enumerate(processes):
												print(
												    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
												    F"{turnaround_times[i]}"
												)
						print(F"Average waiting time = {average_waiting_time}")
						print(F"Average turn around time = {average_turnaround_time}")



