


import argparse
import copy






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				{}

  with open(SCREAMING_SNAKE_CASE_	) as f:
    for line in f:
      if line.split()[0] not in dict_of_neighbours:
        lowerCamelCase :					Optional[int]    =				[]
        _list.append([line.split()[1], line.split()[2]]	)
        lowerCamelCase :					str    =				_list
      else:
        dict_of_neighbours[line.split()[0]].append(
            [line.split()[1], line.split()[2]]	)
      if line.split()[1] not in dict_of_neighbours:
        lowerCamelCase :					Tuple    =				[]
        _list.append([line.split()[0], line.split()[2]]	)
        lowerCamelCase :					Union[str, Any]    =				_list
      else:
        dict_of_neighbours[line.split()[1]].append(
            [line.split()[0], line.split()[2]]	)

  return dict_of_neighbours






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  with open(SCREAMING_SNAKE_CASE_	) as f:
    lowerCamelCase :					List[str]    =				f.read(1	)
  lowerCamelCase :					Dict    =				start_node

  lowerCamelCase :					Optional[int]    =				[]

  lowerCamelCase :					int    =				start_node

  lowerCamelCase :					Tuple    =				0
  while visiting not in first_solution:
    lowerCamelCase :					Union[str, Any]    =				10000
    for k in dict_of_neighbours[visiting]:
      if int(k[1]	) < int(SCREAMING_SNAKE_CASE_	) and k[0] not in first_solution:
        lowerCamelCase :					str    =				k[1]
        lowerCamelCase :					str    =				k[0]

    first_solution.append(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					Optional[int]    =				distance_of_first_solution + int(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					List[Any]    =				best_node

  first_solution.append(SCREAMING_SNAKE_CASE_	)

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

  lowerCamelCase :					Optional[Any]    =				(
      distance_of_first_solution
      + int(dict_of_neighbours[first_solution[-2]][position][1]	)
      - 10000
  )
  return first_solution, distance_of_first_solution






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				[]

  for n in solution[1:-1]:
    lowerCamelCase :					Any    =				solution.index(SCREAMING_SNAKE_CASE_	)
    for kn in solution[1:-1]:
      lowerCamelCase :					List[str]    =				solution.index(SCREAMING_SNAKE_CASE_	)
      if n == kn:
        continue

      lowerCamelCase :					Tuple    =				copy.deepcopy(SCREAMING_SNAKE_CASE_	)
      lowerCamelCase :					Union[str, Any]    =				kn
      lowerCamelCase :					List[str]    =				n

      lowerCamelCase :					List[str]    =				0

      for k in _tmp[:-1]:
        lowerCamelCase :					Union[str, Any]    =				_tmp[_tmp.index(SCREAMING_SNAKE_CASE_	) + 1]
        for i in dict_of_neighbours[k]:
          if i[0] == next_node:
            lowerCamelCase :					Any    =				distance + int(i[1]	)
      _tmp.append(SCREAMING_SNAKE_CASE_	)

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

  lowerCamelCase :					Union[str, Any]    =				len(neighborhood_of_solution[0]	) - 1

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					str    =				1
  lowerCamelCase :					List[str]    =				first_solution
  lowerCamelCase :					Optional[int]    =				[]
  lowerCamelCase :					List[Any]    =				distance_of_first_solution
  lowerCamelCase :					Optional[Any]    =				solution

  while count <= iters:
    lowerCamelCase :					List[Any]    =				find_neighborhood(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					Dict    =				0
    lowerCamelCase :					Optional[int]    =				neighborhood[index_of_best_solution]
    lowerCamelCase :					Optional[int]    =				len(SCREAMING_SNAKE_CASE_	) - 1

    lowerCamelCase :					Optional[int]    =				False
    while not found:
      lowerCamelCase :					int    =				0
      while i < len(SCREAMING_SNAKE_CASE_	):
        if best_solution[i] != solution[i]:
          lowerCamelCase :					int    =				best_solution[i]
          lowerCamelCase :					int    =				solution[i]
          break
        lowerCamelCase :					List[str]    =				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]	)
        lowerCamelCase :					Optional[int]    =				True
        lowerCamelCase :					List[str]    =				best_solution[:-1]
        lowerCamelCase :					Any    =				neighborhood[index_of_best_solution][best_cost_index]
        if cost < best_cost:
          lowerCamelCase :					Dict    =				cost
          lowerCamelCase :					Any    =				solution
      else:
        lowerCamelCase :					int    =				index_of_best_solution + 1
        lowerCamelCase :					Union[str, Any]    =				neighborhood[index_of_best_solution]

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

    lowerCamelCase :					Tuple    =				count + 1

  return best_solution_ever, best_cost






def 		lowercase_(  SCREAMING_SNAKE_CASE_=None	):


  '''simple docstring'''

  lowerCamelCase :					Union[str, Any]    =				generate_neighbours(args.File	)

  lowerCamelCase    ,	lowerCamelCase :					int    =				generate_first_solution(
      args.File      ,						SCREAMING_SNAKE_CASE_	)

  lowerCamelCase    ,	lowerCamelCase :					Any    =				tabu_search(
      SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						args.Iterations      ,						args.Size      ,						)

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


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







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"mobilenet_v2"






   def __init__(		self						,				__A=3						,				__A=224						,				__A=1.0						,				__A=8						,				__A=8						,				__A=6						,				__A=32						,				__A=True						,				__A=True						,				__A="relu6"						,				__A=True						,				__A=0.8						,				__A=0.02						,				__A=0.001						,				__A=255						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )

     if depth_multiplier <= 0:
       raise ValueError("depth_multiplier must be greater than zero." )

     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					Any    =				image_size
     lowerCamelCase :					Union[str, Any]    =				depth_multiplier
     lowerCamelCase :					Tuple    =				depth_divisible_by
     lowerCamelCase :					Dict    =				min_depth
     lowerCamelCase :					Dict    =				expand_ratio
     lowerCamelCase :					Optional[Any]    =				output_stride
     lowerCamelCase :					int    =				first_layer_is_expansion
     lowerCamelCase :					Union[str, Any]    =				finegrained_output
     lowerCamelCase :					Optional[Any]    =				hidden_act
     lowerCamelCase :					Optional[Any]    =				tf_padding
     lowerCamelCase :					Optional[Any]    =				classifier_dropout_prob
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					str    =				layer_norm_eps
     lowerCamelCase :					Optional[Any]    =				semantic_loss_ignore_index



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return OrderedDict([("pixel_values", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     if self.task == "image-classification":
       return OrderedDict([("logits", {0: "batch"})] )
     else:
       return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 1e-4



