




"""simple docstring"""


import argparse
import copy


def  UpperCAmelCase__	(snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Dict								=  {}

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

  return dict_of_neighbours


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]		,       snake_case__							: Union[str, Any]   ):
  """simple docstring"""

  with open(snake_case__   ) as f:
    _snake_case							:		Tuple								=  f.read(1   )
  _snake_case							:		Optional[int]								=  start_node

  _snake_case							:		Any								=  []

  _snake_case							:		List[Any]								=  start_node

  _snake_case							:		Any								=  0
  while visiting not in first_solution:
    _snake_case							:		List[Any]								=  1_00_00
    for k in dict_of_neighbours[visiting]:
      if int(k[1]   ) < int(snake_case__   ) and k[0] not in first_solution:
        _snake_case							:		Optional[int]								=  k[1]
        _snake_case							:		Dict								=  k[0]

    first_solution.append(snake_case__   )
    _snake_case							:		Any								=  distance_of_first_solution + int(snake_case__   )
    _snake_case							:		Union[str, Any]								=  best_node

  first_solution.append(snake_case__   )

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

  _snake_case							:		List[Any]								=  (
      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__	(snake_case__							: Dict		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  []

  for n in solution[1:-1]:
    _snake_case							:		int								=  solution.index(snake_case__   )
    for kn in solution[1:-1]:
      _snake_case							:		Any								=  solution.index(snake_case__   )
      if n == kn:
        continue

      _snake_case							:		Dict								=  copy.deepcopy(snake_case__   )
      _snake_case							:		List[str]								=  kn
      _snake_case							:		Optional[Any]								=  n

      _snake_case							:		List[str]								=  0

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

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

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

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


def  UpperCAmelCase__	(snake_case__							: Tuple		,       snake_case__							: Optional[int]		,       snake_case__							: Optional[int]		,       snake_case__							: str		,       snake_case__							: int   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  1
  _snake_case							:		Optional[int]								=  first_solution
  _snake_case							:		int								=  []
  _snake_case							:		List[Any]								=  distance_of_first_solution
  _snake_case							:		Tuple								=  solution

  while count <= iters:
    _snake_case							:		Union[str, Any]								=  find_neighborhood(snake_case__		,       snake_case__   )
    _snake_case							:		Tuple								=  0
    _snake_case							:		Optional[Any]								=  neighborhood[index_of_best_solution]
    _snake_case							:		Any								=  len(snake_case__   ) - 1

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

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

    _snake_case							:		Dict								=  count + 1

  return best_solution_ever, best_cost


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]=None   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  generate_neighbours(args.File   )

  _snake_case		, _snake_case							:		Tuple								=  generate_first_solution(
      args.File		,       snake_case__   )

  _snake_case		, _snake_case							:		str								=  tabu_search(
      snake_case__		,       snake_case__		,       snake_case__		,       args.Iterations		,       args.Size		,       )

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


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








"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


A_      =    logging.get_logger(__name__)  # pylint: disable=invalid-name






class  lowercase(					__a			):







 '''simple docstring'''





 def __init__(    self:		Optional[int],				a_:		str,				a_:		Optional[Any]		):




   '''simple docstring'''



   super().__init__()
   self.register_modules(unet=a_,				scheduler=a_		)


 @torch.no_grad()
 def __call__(    self:		Any,				a_:		int = 1,				a_:		int = 100,				a_:		Optional[Union[torch.Generator, List[torch.Generator]]] = None,				a_:		Optional[float] = None,				a_:		bool = True,				):




   '''simple docstring'''



   if audio_length_in_s is None:
     _snake_case							:		Dict								=  self.unet.config.sample_size / self.unet.config.sample_rate

   _snake_case							:		Optional[int]								=  audio_length_in_s * self.unet.config.sample_rate

   _snake_case							:		int								=  2 ** len(self.unet.up_blocks		)
   if sample_size < 3 * down_scale_factor:
     raise ValueError(
         f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
         f" {3 * down_scale_factor / self.unet.config.sample_rate}."		)

   _snake_case							:		Union[str, Any]								=  int(a_		)
   if sample_size % down_scale_factor != 0:
     _snake_case							:		Optional[Any]								=  (
         (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
     ) * down_scale_factor
     logger.info(
         f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
         f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
         """ process."""		)
   _snake_case							:		str								=  int(a_		)

   _snake_case							:		int								=  next(iter(self.unet.parameters()		)		).dtype
   _snake_case							:		Optional[Any]								=  (batch_size, self.unet.config.in_channels, sample_size)
   if isinstance(a_,				a_		) and len(a_		) != batch_size:
     raise ValueError(
         f"You have passed a list of generators of length {len(a_		)}, but requested an effective batch"
         f" size of {batch_size}. Make sure the batch size matches the length of the generators."		)

   _snake_case							:		Optional[Any]								=  randn_tensor(a_,				generator=a_,				device=self.device,				dtype=a_		)

   # set step values
   self.scheduler.set_timesteps(a_,				device=audio.device		)
   _snake_case							:		Optional[int]								=  self.scheduler.timesteps.to(a_		)

   for t in self.progress_bar(self.scheduler.timesteps		):
     # 1. predict noise model_output
     _snake_case							:		str								=  self.unet(a_,				a_		).sample

     # 2. compute previous image: x_t -> t_t-1
     _snake_case							:		Optional[Any]								=  self.scheduler.step(a_,				a_,				a_		).prev_sample

   _snake_case							:		Tuple								=  audio.clamp(-1,				1		).float().cpu().numpy()

   _snake_case							:		Dict								=  audio[:, :, :original_sample_size]

   if not return_dict:
     return (audio,)

   return AudioPipelineOutput(audios=a_		)


