import argparse
import copy





def        lowercase(     UpperCamelCase_    )						->     str:







 '''simple docstring'''
 UpperCamelCase          =	{}

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

 return dict_of_neighbours





def        lowercase(     UpperCamelCase_				, UpperCamelCase_    )						->     List[Any]:







 '''simple docstring'''




 with open(lowerCAmelCase__    ) as f:
  UpperCamelCase          =	f.read(1    )
 UpperCamelCase          =	start_node

 UpperCamelCase          =	[]

 UpperCamelCase          =	start_node

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

  first_solution.append(lowerCAmelCase__    )
  UpperCamelCase          =	distance_of_first_solution + int(lowerCAmelCase__    )
  UpperCamelCase          =	best_node

 first_solution.append(lowerCAmelCase__    )

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

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





def        lowercase(     UpperCamelCase_				, UpperCamelCase_    )						->     str:







 '''simple docstring'''
 UpperCamelCase          =	[]

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

   UpperCamelCase          =	copy.deepcopy(lowerCAmelCase__    )
   UpperCamelCase          =	kn
   UpperCamelCase          =	n

   UpperCamelCase          =	0

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

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

 UpperCamelCase          =	len(neighborhood_of_solution[0]    ) - 1

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





def        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )						->     Any:







 '''simple docstring'''
 UpperCamelCase          =	1
 UpperCamelCase          =	first_solution
 UpperCamelCase          =	[]
 UpperCamelCase          =	distance_of_first_solution
 UpperCamelCase          =	solution

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

  UpperCamelCase          =	False
  while not found:
   UpperCamelCase          =	0
   while i < len(lowerCAmelCase__    ):
    if best_solution[i] != solution[i]:
     UpperCamelCase          =	best_solution[i]
     UpperCamelCase          =	solution[i]
     break
    UpperCamelCase          =	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]    )
    UpperCamelCase          =	True
    UpperCamelCase          =	best_solution[:-1]
    UpperCamelCase          =	neighborhood[index_of_best_solution][best_cost_index]
    if cost < best_cost:
     UpperCamelCase          =	cost
     UpperCamelCase          =	solution
   else:
    UpperCamelCase          =	index_of_best_solution + 1
    UpperCamelCase          =	neighborhood[index_of_best_solution]

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

  UpperCamelCase          =	count + 1

 return best_solution_ever, best_cost





def        lowercase(     UpperCamelCase_=None    )						->     str:







 '''simple docstring'''
 UpperCamelCase          =	generate_neighbours(args.File    )

 UpperCamelCase          =	generate_first_solution(
     args.File				, lowerCAmelCase__    )

 UpperCamelCase          =	tabu_search(
     lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, args.Iterations				, args.Size				, )

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


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE      						=	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"""







import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf
     from tensorflow.python.eager import context
     from tensorflow.python.framework import ops

     from transformers import GradientAccumulator, create_optimizer







@require_tf
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]       ):
        self.assertEqual(len(SCREAMING_SNAKE_CASE_       )    ,			len(SCREAMING_SNAKE_CASE_       )       )
        for a, b in zip(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
          self.assertAlmostEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			delta=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				int								= GradientAccumulator()
        accumulator([tf.constant([1.0, 2.0]       )]       )
        accumulator([tf.constant([-2.0, 1.0]       )]       )
        accumulator([tf.constant([-1.0, 2.0]       )]       )
        with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
          accumulator([tf.constant([1.0, 1.0]       ), tf.constant([2.0, 2.0]       )]       )
        self.assertEqual(accumulator.step    ,			3       )
        self.assertEqual(len(accumulator.gradients       )    ,			1       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[-2.0, 5.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[0.0, 0.0]    ,			tol=1E-2       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[int]								= None
        ops.enable_eager_execution_internal()
        lowerCAmelCase_   :				str								= tf.config.list_physical_devices('CPU'       )
        if len(SCREAMING_SNAKE_CASE_       ) == 1:
          tf.config.set_logical_device_configuration(
              physical_devices[0]    ,			[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]       )
        lowerCAmelCase_   :				Dict								= tf.config.list_logical_devices(device_type='CPU'       )
        lowerCAmelCase_   :				Optional[int]								= tf.distribute.MirroredStrategy(devices=devices[:2]       )

        with strategy.scope():
          lowerCAmelCase_   :				Union[str, Any]								= GradientAccumulator()
          lowerCAmelCase_   :				int								= tf.Variable([4.0, 3.0]       )
          lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= create_optimizer(5E-5    ,			1_0    ,			5       )
          lowerCAmelCase_   :				Union[str, Any]								= tf.Variable([0.0, 0.0]    ,			trainable=SCREAMING_SNAKE_CASE_       )

        def accumulate_on_replica(SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          accumulator([gradient]       )

        def apply_on_replica():
          optimizer.apply_gradients(list(zip(accumulator.gradients    ,			[variable]       )       )       )

        @tf.function
        def accumulate(SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
          with strategy.scope():
            lowerCAmelCase_   :				Tuple								= strategy.experimental_local_results(SCREAMING_SNAKE_CASE_       )
            local_variables[0].assign(SCREAMING_SNAKE_CASE_       )
            local_variables[1].assign(SCREAMING_SNAKE_CASE_       )
            strategy.run(SCREAMING_SNAKE_CASE_    ,			args=(gradient_placeholder,)       )

        @tf.function
        def apply_grad():
          with strategy.scope():
            strategy.run(SCREAMING_SNAKE_CASE_       )

        def _check_local_values(SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          lowerCAmelCase_   :				List[Any]								= strategy.experimental_local_results(accumulator._gradients[0]       )
          self.assertListAlmostEqual(values[0].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )
          self.assertListAlmostEqual(values[1].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )

        accumulate([1.0, 2.0]    ,			[-1.0, 1.0]       )
        accumulate([3.0, -1.0]    ,			[-1.0, -1.0]       )
        accumulate([-2.0, 2.0]    ,			[3.0, -2.0]       )
        self.assertEqual(accumulator.step    ,			3       )
        _check_local_values([2.0, 3.0]    ,			[1.0, -2.0]       )
        apply_grad()
        self.assertListAlmostEqual(variable.value()    ,			[4.0, 3.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        _check_local_values([0.0, 0.0]    ,			[0.0, 0.0]       )





