


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase_   :   List[str]									=  {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_   :   str									=  ["""Speech2TextTokenizer"""]

try:
       if not is_speech_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_   :   Optional[Any]									=  ["""Speech2TextFeatureExtractor"""]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_   :   List[Any]									=  [
           """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFSpeech2TextForConditionalGeneration""",
           """TFSpeech2TextModel""",
           """TFSpeech2TextPreTrainedModel""",
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_   :   str									=  [
           """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """Speech2TextForConditionalGeneration""",
           """Speech2TextModel""",
           """Speech2TextPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
       from .processing_speech_to_text import SpeechaTextProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_speech_to_text import SpeechaTextTokenizer

       try:
              if not is_speech_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_speech_to_text import (
                  TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFSpeechaTextForConditionalGeneration,
                  TFSpeechaTextModel,
                  TFSpeechaTextPreTrainedModel,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_speech_to_text import (
                  SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SpeechaTextForConditionalGeneration,
                  SpeechaTextModel,
                  SpeechaTextPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_   :   Optional[Any]									=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




import argparse
import copy






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    {}

							with open(_lowerCamelCase  ) as f:
														for line in f:
																					if line.split()[0] not in dict_of_neighbours:
																												_lowerCAmelCase     :							Tuple    =    []
																												_list.append([line.split()[1], line.split()[2]]  )
																												_lowerCAmelCase     :							Any    =    _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     :							str    =    []
																												_list.append([line.split()[0], line.split()[2]]  )
																												_lowerCAmelCase     :							Any    =    _list
																					else:
																												dict_of_neighbours[line.split()[1]].append(
																												    [line.split()[0], line.split()[2]]  )

							return dict_of_neighbours






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							with open(_lowerCamelCase  ) as f:
														_lowerCAmelCase     :							str    =    f.read(1  )
							_lowerCAmelCase     :							str    =    start_node

							_lowerCAmelCase     :							List[str]    =    []

							_lowerCAmelCase     :							Any    =    start_node

							_lowerCAmelCase     :							str    =    0
							while visiting not in first_solution:
														_lowerCAmelCase     :							Dict    =    10_000
														for k in dict_of_neighbours[visiting]:
																					if int(k[1]  ) < int(_lowerCamelCase  ) and k[0] not in first_solution:
																												_lowerCAmelCase     :							List[str]    =    k[1]
																												_lowerCAmelCase     :							List[Any]    =    k[0]

														first_solution.append(_lowerCamelCase  )
														_lowerCAmelCase     :							Optional[int]    =    distance_of_first_solution + int(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    best_node

							first_solution.append(_lowerCamelCase  )

							_lowerCAmelCase     :							Union[str, Any]    =    0
							for k in dict_of_neighbours[first_solution[-2]]:
														if k[0] == start_node:
																					break
														position += 1

							_lowerCAmelCase     :							Tuple    =    (
							    distance_of_first_solution
							    + int(dict_of_neighbours[first_solution[-2]][position][1]  )
							    - 10_000
							)
							return first_solution, distance_of_first_solution






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    []

							for n in solution[1:-1]:
														_lowerCAmelCase     :							Dict    =    solution.index(_lowerCamelCase  )
														for kn in solution[1:-1]:
																					_lowerCAmelCase     :							Dict    =    solution.index(_lowerCamelCase  )
																					if n == kn:
																												continue

																					_lowerCAmelCase     :							Optional[int]    =    copy.deepcopy(_lowerCamelCase  )
																					_lowerCAmelCase     :							int    =    kn
																					_lowerCAmelCase     :							Dict    =    n

																					_lowerCAmelCase     :							Optional[int]    =    0

																					for k in _tmp[:-1]:
																												_lowerCAmelCase     :							str    =    _tmp[_tmp.index(_lowerCamelCase  ) + 1]
																												for i in dict_of_neighbours[k]:
																																			if i[0] == next_node:
																																										_lowerCAmelCase     :							Optional[Any]    =    distance + int(i[1]  )
																					_tmp.append(_lowerCamelCase  )

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

							_lowerCAmelCase     :							List[Any]    =    len(neighborhood_of_solution[0]  ) - 1

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    1
							_lowerCAmelCase     :							int    =    first_solution
							_lowerCAmelCase     :							Tuple    =    []
							_lowerCAmelCase     :							Tuple    =    distance_of_first_solution
							_lowerCAmelCase     :							Optional[int]    =    solution

							while count <= iters:
														_lowerCAmelCase     :							int    =    find_neighborhood(_lowerCamelCase				, _lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    0
														_lowerCAmelCase     :							Dict    =    neighborhood[index_of_best_solution]
														_lowerCAmelCase     :							int    =    len(_lowerCamelCase  ) - 1

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

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

														_lowerCAmelCase     :							int    =    count + 1

							return best_solution_ever, best_cost






def 				A				(					_lowerCamelCase=None  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    generate_neighbours(args.File  )

							_lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    generate_first_solution(
							    args.File				, _lowerCamelCase  )

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    tabu_search(
							    _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, 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())
