

'''simple docstring'''
from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_snake_case     =  logging.get_logger(__name__)


class    a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE    :     Dict	       =							['input_features', 'attention_mask']


   def __init__( self			,				_UpperCamelCase=80			,				_UpperCamelCase=16000			,				_UpperCamelCase=0.0			,				_UpperCamelCase=10			,				_UpperCamelCase=25			,				_UpperCamelCase="hamming_window"			,				_UpperCamelCase=32768.0			,				_UpperCamelCase=0.9_7			,				_UpperCamelCase=1.0			,				_UpperCamelCase=True			,				_UpperCamelCase=True			,				_UpperCamelCase=False			,				**_UpperCamelCase			,				):





      """simple docstring"""





      super().__init__(feature_size=__a			,				sampling_rate=__a			,				padding_value=__a			,				**__a  )

      _lowercase			:      int			     =   feature_size
      _lowercase			:      Optional[Any]			     =   sampling_rate
      _lowercase			:      Tuple			     =   padding_value
      _lowercase			:      int			     =   hop_length
      _lowercase			:      str			     =   win_length
      _lowercase			:      str			     =   frame_signal_scale
      _lowercase			:      Union[str, Any]			     =   preemphasis_coeff
      _lowercase			:      Optional[Any]			     =   mel_floor
      _lowercase			:      str			     =   normalize_means
      _lowercase			:      Union[str, Any]			     =   normalize_vars
      _lowercase			:      List[str]			     =   win_function
      _lowercase			:      str			     =   return_attention_mask

      _lowercase			:      str			     =   win_length * sampling_rate // 1000
      _lowercase			:      Union[str, Any]			     =   hop_length * sampling_rate // 1000

      _lowercase			:      Tuple			     =   optimal_fft_length(self.sample_size  )
      _lowercase			:      Tuple			     =   (self.n_fft // 2) + 1


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      if self.win_function == "hamming_window":
         _lowercase			:      str			     =   window_function(window_length=self.sample_size			,				name=self.win_function			,				periodic=__a  )
      else:
         _lowercase			:      Union[str, Any]			     =   window_function(window_length=self.sample_size			,				name=self.win_function  )

      _lowercase			:      List[Any]			     =   mel_filter_bank(
          num_frequency_bins=self.n_freqs			,				num_mel_filters=self.feature_size			,				min_frequency=0.0			,				max_frequency=self.sampling_rate / 2.0			,				sampling_rate=self.sampling_rate			,				)

      _lowercase			:      Optional[Any]			     =   spectrogram(
          one_waveform * self.frame_signal_scale			,				window=__a			,				frame_length=self.sample_size			,				hop_length=self.sample_stride			,				fft_length=self.n_fft			,				center=__a			,				preemphasis=self.preemphasis_coeff			,				mel_filters=__a			,				mel_floor=self.mel_floor			,				log_mel="log"			,				)
      return msfc_features.T


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      if self.normalize_means:
         _lowercase			:      Dict			     =   x[:input_length].mean(axis=0  )
         _lowercase			:      Optional[Any]			     =   np.subtract(__a			,				__a  )
      if self.normalize_vars:
         _lowercase			:      List[str]			     =   x[:input_length].std(axis=0  )
         _lowercase			:      Optional[int]			     =   np.divide(__a			,				__a  )

      if input_length < x.shape[0]:
         _lowercase			:      Union[str, Any]			     =   padding_value

      # make sure array is in float32
      _lowercase			:      Optional[int]			     =   x.astype(np.floataa  )

      return x


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase = None  ):





      """simple docstring"""





      _lowercase			:      int			     =   attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
      return [self._normalize_one(__a			,				__a			,				self.padding_value  ) for x, n in zip(__a			,				__a  )]






   def __call__( self			,				_UpperCamelCase			,				_UpperCamelCase = False			,				_UpperCamelCase = None			,				_UpperCamelCase = False			,				_UpperCamelCase = None			,				_UpperCamelCase = None			,				_UpperCamelCase = None			,				_UpperCamelCase = None			,				**_UpperCamelCase			,				):





      """simple docstring"""





      if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
            raise ValueError(
                f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                f''' {self.sampling_rate} and not {sampling_rate}.'''  )
      else:
         logger.warning(
             "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
             "Failing to do so can result in silent errors that might be hard to debug."  )

      _lowercase			:      List[Any]			     =   isinstance(__a			,				np.ndarray  ) and len(raw_speech.shape  ) > 1
      if is_batched_numpy and len(raw_speech.shape  ) > 2:
         raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''  )
      _lowercase			:      int			     =   is_batched_numpy or (
          isinstance(__a			,				(list, tuple)  ) and (isinstance(raw_speech[0]			,				(np.ndarray, tuple, list)  ))
      )

      if is_batched:
         _lowercase			:      Any			     =   [np.asarray(__a			,				dtype=np.floataa  ) for speech in raw_speech]
      elif not is_batched and not isinstance(__a			,				np.ndarray  ):
         _lowercase			:      Tuple			     =   np.asarray(__a			,				dtype=np.floataa  )
      elif isinstance(__a			,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
         _lowercase			:      Union[str, Any]			     =   raw_speech.astype(np.floataa  )

      # always return batch
      if not is_batched:
         _lowercase			:      Optional[Any]			     =   [raw_speech]

      # extract fbank features
      _lowercase			:      List[str]			     =   [self._extract_mfsc_features(__a  ) for one_waveform in raw_speech]

      # convert into correct format for padding
      _lowercase			:      int			     =   BatchFeature({"input_features": features}  )

      _lowercase			:      str			     =   self.pad(
          __a			,				padding=__a			,				max_length=__a			,				truncation=__a			,				pad_to_multiple_of=__a			,				return_attention_mask=__a			,				**__a			,				)
      # make sure list is in array format
      _lowercase			:      Optional[Any]			     =   padded_inputs.get("input_features"  )
      if isinstance(input_features[0]			,				__a  ):
         _lowercase			:      int			     =   [np.asarray(__a			,				dtype=np.floataa  ) for feature in input_features]

      _lowercase			:      str			     =   padded_inputs.get("attention_mask"  )
      if attention_mask is not None:
         _lowercase			:      Optional[Any]			     =   [np.asarray(__a			,				dtype=np.intaa  ) for array in attention_mask]

      if self.normalize_means or self.normalize_vars:
         _lowercase			:      int			     =   (
             np.array(__a			,				dtype=np.intaa  )
             if self._get_padding_strategies(__a			,				max_length=__a  ) is not PaddingStrategy.DO_NOT_PAD
             and padding
             else None
         )
         _lowercase			:      List[Any]			     =   self.normalize(
             padded_inputs["input_features"]			,				attention_mask=__a  )

      if return_tensors is not None:
         _lowercase			:      Tuple			     =   padded_inputs.convert_to_tensors(__a  )

      return padded_inputs











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())
