




import argparse
import copy




def 			__magic_name__				( A		:					int   ):






			'''simple docstring'''







			a    =			{}

			with open(A   ) as f:
						for line in f:
									if line.split()[0] not in dict_of_neighbours:
												a    =			[]
												_list.append([line.split()[1], line.split()[2]]   )
												a    =			_list
									else:
												dict_of_neighbours[line.split()[0]].append(
												    [line.split()[1], line.split()[2]]   )
									if line.split()[1] not in dict_of_neighbours:
												a    =			[]
												_list.append([line.split()[0], line.split()[2]]   )
												a    =			_list
									else:
												dict_of_neighbours[line.split()[1]].append(
												    [line.split()[0], line.split()[2]]   )

			return dict_of_neighbours




def 			__magic_name__				( A		:					Optional[int],  A		:					Dict   ):






			'''simple docstring'''







			with open(A   ) as f:
						a    =			f.read(1   )
			a    =			start_node

			a    =			[]

			a    =			start_node

			a    =			0
			while visiting not in first_solution:
						a    =			10000
						for k in dict_of_neighbours[visiting]:
									if int(k[1]   ) < int(A   ) and k[0] not in first_solution:
												a    =			k[1]
												a    =			k[0]

						first_solution.append(A   )
						a    =			distance_of_first_solution + int(A   )
						a    =			best_node

			first_solution.append(A   )

			a    =			0
			for k in dict_of_neighbours[first_solution[-2]]:
						if k[0] == start_node:
									break
						position += 1

			a    =			(
			    distance_of_first_solution
			    + int(dict_of_neighbours[first_solution[-2]][position][1]   )
			    - 10000
			)
			return first_solution, distance_of_first_solution




def 			__magic_name__				( A		:					Tuple,  A		:					List[str]   ):






			'''simple docstring'''







			a    =			[]

			for n in solution[1:-1]:
						a    =			solution.index(A   )
						for kn in solution[1:-1]:
									a    =			solution.index(A   )
									if n == kn:
												continue

									a    =			copy.deepcopy(A   )
									a    =			kn
									a    =			n

									a    =			0

									for k in _tmp[:-1]:
												a    =			_tmp[_tmp.index(A   ) + 1]
												for i in dict_of_neighbours[k]:
															if i[0] == next_node:
																		a    =			distance + int(i[1]   )
									_tmp.append(A   )

									if _tmp not in neighborhood_of_solution:
												neighborhood_of_solution.append(_tmp   )

			a    =			len(neighborhood_of_solution[0]   ) - 1

			neighborhood_of_solution.sort(key=lambda A   : x[index_of_last_item_in_the_list]   )
			return neighborhood_of_solution




def 			__magic_name__				( A		:					Any,  A		:					List[str],  A		:					Optional[int],  A		:					Dict,  A		:					List[Any]   ):






			'''simple docstring'''







			a    =			1
			a    =			first_solution
			a    =			[]
			a    =			distance_of_first_solution
			a    =			solution

			while count <= iters:
						a    =			find_neighborhood(A,  A   )
						a    =			0
						a    =			neighborhood[index_of_best_solution]
						a    =			len(A   ) - 1

						a    =			False
						while not found:
									a    =			0
									while i < len(A   ):
												if best_solution[i] != solution[i]:
															a    =			best_solution[i]
															a    =			solution[i]
															break
												a    =			i + 1

									if [first_exchange_node, second_exchange_node] not in tabu_list and [
									    second_exchange_node,
									    first_exchange_node,
									] not in tabu_list:
												tabu_list.append([first_exchange_node, second_exchange_node]   )
												a    =			True
												a    =			best_solution[:-1]
												a    =			neighborhood[index_of_best_solution][best_cost_index]
												if cost < best_cost:
															a    =			cost
															a    =			solution
									else:
												a    =			index_of_best_solution + 1
												a    =			neighborhood[index_of_best_solution]

						if len(A   ) >= size:
									tabu_list.pop(0   )

						a    =			count + 1

			return best_solution_ever, best_cost




def 			__magic_name__				( A		:					Optional[Any]=None   ):






			'''simple docstring'''







			a    =			generate_neighbours(args.File   )

			a    ,		a    =			generate_first_solution(
			    args.File,  A   )

			a    ,		a    =			tabu_search(
			    A,  A,  A,  args.Iterations,  args.Size,  )

			print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""   )


if __name__ == "__main__":
		__lowerCAmelCase		:				Any       			=		argparse.ArgumentParser(description='Tabu Search')
		parser.add_argument(
		    '-f',
		    '--File',
		    type=str,
		    help='Path to the file containing the data',
		    required=True,
		)
		parser.add_argument(
		    '-i',
		    '--Iterations',
		    type=int,
		    help='How many iterations the algorithm should perform',
		    required=True,
		)
		parser.add_argument(
		    '-s', '--Size', type=int, help='Size of the tabu list', required=True
		)

		# Pass the arguments to main method
		main(parser.parse_args())











'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError('level must be between -255.0 (black) and 255.0 (white)'    )
   return img.point(A__    )


if __name__ == "__main__":
   # Load image
   with Image.open("image_data/lena.jpg") as img:
      # Change brightness to 100
      _lowerCamelCase	:       List[str]      	=	change_brightness(img, 100)
      brigt_img.save("image_data/lena_brightness.png", format="png")

