





'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor
class 			_lowercase						(       unittest.TestCase					):







     def __init__( self    ,   A__    ,   A__=7    ,   A__=3    ,   A__=18    ,   A__=30    ,   A__=4_00    ,   A__=True    ,   A__=None    ,   A__=True    ,   A__=None    ,   A__=True    ,   ) ->	Dict:
      snake_case				=			size if size is not None else {'''shortest_edge''': 20}
      snake_case				=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			num_channels
      snake_case				=			image_size
      snake_case				=			min_resolution
      snake_case				=			max_resolution
      snake_case				=			do_resize
      snake_case				=			size
      snake_case				=			do_center_crop
      snake_case				=			crop_size
      snake_case				=			do_flip_channel_order







     def     UpperCamelCase			( self    ) ->	Tuple:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }




@require_torch
@require_vision
class 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					MobileViTImageProcessor if is_vision_available() else None







     def     UpperCamelCase			( self    ) ->	str:
      snake_case				=			MobileViTImageProcessingTester(self    )







     @property
     def     UpperCamelCase			( self    ) ->	int:
      return self.image_processor_tester.prepare_image_processor_dict()







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(A__    ,   '''do_resize'''    )    )
      self.assertTrue(hasattr(A__    ,   '''size'''    )    )
      self.assertTrue(hasattr(A__    ,   '''do_center_crop'''    )    )
      self.assertTrue(hasattr(A__    ,   '''center_crop'''    )    )
      self.assertTrue(hasattr(A__    ,   '''do_flip_channel_order'''    )    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 20}    )
      self.assertEqual(image_processor.crop_size    ,   {'''height''': 18, '''width''': 18}    )

      snake_case				=			self.image_processing_class.from_dict(self.image_processor_dict    ,   size=42    ,   crop_size=84    )
      self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 42}    )
      self.assertEqual(image_processor.crop_size    ,   {'''height''': 84, '''width''': 84}    )







     def     UpperCamelCase			( self    ) ->	Any:
      pass







     def     UpperCamelCase			( self    ) ->	Dict:
      # Initialize image_processing
      snake_case				=			self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      snake_case				=			prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=A__    )
      for image in image_inputs:
       self.assertIsInstance(A__    ,   Image.Image    )

      # Test not batched input
      snake_case				=			image_processing(image_inputs[0]    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )    ,   )

      # Test batched
      snake_case				=			image_processing(A__    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )    ,   )







     def     UpperCamelCase			( self    ) ->	Dict:
      # Initialize image_processing
      snake_case				=			self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      snake_case				=			prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=A__    ,   numpify=A__    )
      for image in image_inputs:
       self.assertIsInstance(A__    ,   np.ndarray    )

      # Test not batched input
      snake_case				=			image_processing(image_inputs[0]    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )    ,   )

      # Test batched
      snake_case				=			image_processing(A__    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )    ,   )







     def     UpperCamelCase			( self    ) ->	str:
      # Initialize image_processing
      snake_case				=			self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      snake_case				=			prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=A__    ,   torchify=A__    )
      for image in image_inputs:
       self.assertIsInstance(A__    ,   torch.Tensor    )

      # Test not batched input
      snake_case				=			image_processing(image_inputs[0]    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )    ,   )

      # Test batched
      snake_case				=			image_processing(A__    ,   return_tensors='''pt'''    ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )    ,   )








'''simple docstring'''







import argparse
import copy

def    __UpperCamelCase	(	a    : Union[str, Any]     ) ->Tuple:
 snake_case				=			{}

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

 return dict_of_neighbours

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple     ) ->int:

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

 snake_case				=			[]

 snake_case				=			start_node

 snake_case				=			0
 while visiting not in first_solution:
  snake_case				=			1_0000
  for k in dict_of_neighbours[visiting]:
   if int(k[1]     ) < int(a     ) and k[0] not in first_solution:
    snake_case				=			k[1]
    snake_case				=			k[0]

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

 first_solution.append(a     )

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

 snake_case				=			(
     distance_of_first_solution
     + int(dict_of_neighbours[first_solution[-2]][position][1]     )
     - 1_0000
 )
 return first_solution, distance_of_first_solution

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : str     ) ->str:
 snake_case				=			[]

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

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

   snake_case				=			0

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

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

 snake_case				=			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    __UpperCamelCase	(	a    : Any					,   a    : Optional[Any]					,   a    : int					,   a    : Optional[int]					,   a    : Union[str, Any]     ) ->List[Any]:
 snake_case				=			1
 snake_case				=			first_solution
 snake_case				=			[]
 snake_case				=			distance_of_first_solution
 snake_case				=			solution

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

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

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

  snake_case				=			count + 1

 return best_solution_ever, best_cost

def    __UpperCamelCase	(	a    : Union[str, Any]=None     ) ->Optional[Any]:
 snake_case				=			generate_neighbours(args.File     )

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

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

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


if __name__ == "__main__":
   _lowercase       						=			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())

