


"""simple docstring"""






from __future__ import annotations

from statistics import mean




def        A_					(					UpperCAmelCase__				)     ->     List[Any]:
   a   :	List[str]						=    2
   a   :	Union[str, Any]						=    list(UpperCAmelCase__				)
   a   :	Any						=    [0] * len(UpperCAmelCase__				)
   a   :	Any						=    0
   while True:
      a   :	str						=    True
      for i, burst_time in enumerate(UpperCAmelCase__				):
         if rem_burst_times[i] > 0:
            a   :	str						=    False
            if rem_burst_times[i] > quantum:
               t += quantum
               rem_burst_times[i] -= quantum
            else:
               t += rem_burst_times[i]
               a   :	int						=    t - burst_time
               a   :	Dict						=    0
      if done is True:
         return waiting_times




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Tuple:
   return [burst + waiting for burst, waiting in zip(UpperCAmelCase__       ,   UpperCAmelCase__				)]


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__				:  Dict						= [3, 5, 7]
 SCREAMING_SNAKE_CASE__				:  Dict						= calculate_waiting_times(burst_times)
 SCREAMING_SNAKE_CASE__				:  Tuple						= calculate_turn_around_times(burst_times, waiting_times)
 print("Process ID \tBurst Time \tWaiting Time \tTurnaround Time")
 for i, burst_time in enumerate(burst_times):
  print(
      F'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
      F'{turn_around_times[i]}'
  )
 print(F'\nAverage waiting time = {mean(waiting_times):.5f}')
 print(F'Average turn around time = {mean(turn_around_times):.5f}')







"""simple docstring"""
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
_a   :      List[str]       =				{
    """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 					a__ ( a :					dict[str, list[str]]		):

  """simple docstring"""







  _snake_case  :    List[Any]            = {node: {node} for node in graph}

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

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

    # Contract edge (u, v) to new node uv
    _snake_case  :    Union[str, Any]            = u + v
    _snake_case  :    Union[str, Any]            = list(set(graph_copy[u] + graph_copy[v]		)		)
    uv_neighbors.remove(a		)
    uv_neighbors.remove(a		)
    _snake_case  :    Union[str, Any]            = uv_neighbors
    for neighbor in uv_neighbors:
      graph_copy[neighbor].append(a		)

    _snake_case  :    Dict            = 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(a		)
      if v in graph_copy[neighbor]:
        graph_copy[neighbor].remove(a		)

    # Find cutset.
  _snake_case  :    List[Any]            = [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))

