

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)






class 	UpperCAmelCase__		(	__snake_case	):






  """simple docstring"""


  a									= ['pixel_values']






  def __init__(							self						:		int     ,       __lowerCamelCase						:		Dict = True     ,       __lowerCamelCase						:		str = None     ,       __lowerCamelCase						:		Optional[int] = PIL.Image.BICUBIC     ,       __lowerCamelCase						:		Dict = True     ,       __lowerCamelCase						:		Tuple = None     ,       __lowerCamelCase						:		Tuple = 1 / 255     ,       __lowerCamelCase						:		List[str] = True     ,       __lowerCamelCase						:		Any = True     ,       __lowerCamelCase						:		List[Any] = None     ,       __lowerCamelCase						:		Optional[int] = None     ,       **__lowerCamelCase						:		Union[str, Any]     ,       )		->							None:
       super().__init__(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   size if size is not None else {'''height''': 256, '''width''': 256}
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       param_name='''crop_size''' )

       SCREAMING_SNAKE_CASE__   =   do_resize
       SCREAMING_SNAKE_CASE__   =   size
       SCREAMING_SNAKE_CASE__   =   resample
       SCREAMING_SNAKE_CASE__   =   do_center_crop
       SCREAMING_SNAKE_CASE__   =   crop_size
       SCREAMING_SNAKE_CASE__   =   do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_normalize
       SCREAMING_SNAKE_CASE__   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__   =   image_std if image_std is not None else IMAGENET_STANDARD_STD






  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Dict = PIL.Image.BICUBIC     ,       __lowerCamelCase						:		Dict = None     ,       **__lowerCamelCase						:		Any     ,       )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase )
       if "height" not in size or "width" not in size:
            raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''' )
       return resize(
           __lowerCamelCase     ,       size=(size['''height'''], size['''width'''])     ,       resample=__lowerCamelCase     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Optional[int] = None     ,       **__lowerCamelCase						:		Tuple     ,       )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase )
       if "height" not in size or "width" not in size:
            raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''' )
       return center_crop(__lowerCamelCase     ,       size=(size['''height'''], size['''width'''])     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		Tuple = None     ,       **__lowerCamelCase						:		int     ,       )		->							Any:
       return rescale(__lowerCamelCase     ,       scale=__lowerCamelCase     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		str     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		str = None     ,       **__lowerCamelCase						:		str     ,       )		->							np.ndarray:
       return normalize(__lowerCamelCase     ,       mean=__lowerCamelCase     ,       std=__lowerCamelCase     ,       data_format=__lowerCamelCase     ,       **__lowerCamelCase )







  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Union[str, Any] = None     ,       __lowerCamelCase						:		str = None     ,       __lowerCamelCase						:		int=None     ,       __lowerCamelCase						:		List[str] = None     ,       __lowerCamelCase						:		Dict = None     ,       __lowerCamelCase						:		int = None     ,       __lowerCamelCase						:		List[Any] = None     ,       __lowerCamelCase						:		Tuple = None     ,       __lowerCamelCase						:		Tuple = None     ,       __lowerCamelCase						:		str = None     ,       __lowerCamelCase						:		Union[str, Any] = None     ,       __lowerCamelCase						:		Any = ChannelDimension.FIRST     ,       **__lowerCamelCase						:		str     ,       )		->							PIL.Image.Image:
       SCREAMING_SNAKE_CASE__   =   do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__   =   resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__   =   do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__   =   do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__   =   image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__   =   image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__   =   size if size is not None else self.size
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__   =   get_size_dict(__lowerCamelCase     ,       param_name='''crop_size''' )

       SCREAMING_SNAKE_CASE__   =   make_list_of_images(__lowerCamelCase )

       if not valid_images(__lowerCamelCase ):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

       if do_resize and size is None or resample is None:
            raise ValueError('''Size and resample must be specified if do_resize is True.''' )

       if do_center_crop and crop_size is None:
            raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

       if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

       if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__   =   [to_numpy_array(__lowerCamelCase ) for image in images]

       if do_resize:
            SCREAMING_SNAKE_CASE__   =   [self.resize(image=__lowerCamelCase     ,       size=__lowerCamelCase     ,       resample=__lowerCamelCase ) for image in images]

       if do_center_crop:
            SCREAMING_SNAKE_CASE__   =   [self.center_crop(image=__lowerCamelCase     ,       size=__lowerCamelCase ) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__   =   [self.rescale(image=__lowerCamelCase     ,       scale=__lowerCamelCase ) for image in images]

       if do_normalize:
            SCREAMING_SNAKE_CASE__   =   [self.normalize(image=__lowerCamelCase     ,       mean=__lowerCamelCase     ,       std=__lowerCamelCase ) for image in images]

       SCREAMING_SNAKE_CASE__   =   [to_channel_dimension_format(__lowerCamelCase     ,       __lowerCamelCase ) for image in images]

       SCREAMING_SNAKE_CASE__   =   {'''pixel_values''': images}
       return BatchFeature(data=__lowerCamelCase     ,       tensor_type=__lowerCamelCase )





import argparse
import copy
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   {}

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

     return dict_of_neighbours
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     with open(_A    ) as f:
          SCREAMING_SNAKE_CASE__   =   f.read(1    )
     SCREAMING_SNAKE_CASE__   =   start_node

     SCREAMING_SNAKE_CASE__   =   []

     SCREAMING_SNAKE_CASE__   =   start_node

     SCREAMING_SNAKE_CASE__   =   0
     while visiting not in first_solution:
          SCREAMING_SNAKE_CASE__   =   1_00_00
          for k in dict_of_neighbours[visiting]:
               if int(k[1]    ) < int(_A    ) and k[0] not in first_solution:
                    SCREAMING_SNAKE_CASE__   =   k[1]
                    SCREAMING_SNAKE_CASE__   =   k[0]

          first_solution.append(_A    )
          SCREAMING_SNAKE_CASE__   =   distance_of_first_solution + int(_A    )
          SCREAMING_SNAKE_CASE__   =   best_node

     first_solution.append(_A    )

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

     SCREAMING_SNAKE_CASE__   =   (
         distance_of_first_solution
         + int(dict_of_neighbours[first_solution[-2]][position][1]    )
         - 1_00_00
     )
     return first_solution, distance_of_first_solution
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   []

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

               SCREAMING_SNAKE_CASE__   =   copy.deepcopy(_A    )
               SCREAMING_SNAKE_CASE__   =   kn
               SCREAMING_SNAKE_CASE__   =   n

               SCREAMING_SNAKE_CASE__   =   0

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

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

     SCREAMING_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				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   1
     SCREAMING_SNAKE_CASE__   =   first_solution
     SCREAMING_SNAKE_CASE__   =   []
     SCREAMING_SNAKE_CASE__   =   distance_of_first_solution
     SCREAMING_SNAKE_CASE__   =   solution

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

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

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

          SCREAMING_SNAKE_CASE__   =   count + 1

     return best_solution_ever, best_cost
def     UpperCAmelCase_       (						_A=None    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   generate_neighbours(args.File    )

     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   generate_first_solution(
         args.File				,		_A    )

     SCREAMING_SNAKE_CASE__,SCREAMING_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__":
       _SCREAMING_SNAKE_CASE					:  Tuple             =      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())


