

'''simple docstring'''






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 			lowercase_	(_lowerCamelCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			4_2
  SCREAMING_SNAKE_CASE    :  Tuple							=			None


def 		_A     (    A__     ,				A__=0.9_9_9     ,				A__="cosine"     ,				):






 """simple docstring"""



 if alpha_transform_type == "cosine":

  def alpha_bar_fn(A__      ):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2      ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(A__      ):
   return math.exp(t * -1_2.0      )

 else:
  raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"      )

 __lowercase							  = []
 for i in range(A__      ):
  __lowercase							  = i / num_diffusion_timesteps
  __lowercase							  = (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(A__      ) / alpha_bar_fn(A__      )     ,				A__      )      )
 return torch.tensor(A__     ,				dtype=torch.floataa      )






class 			lowercase_	(_lowerCamelCase				,       _lowerCamelCase      ):




  """simple docstring"""




  @register_to_config
  def __init__(      self   :     Tuple					,lowercase__   :     int = 1_0_0_0					,lowercase__   :     str = "fixed_small_log"					,lowercase__   :     bool = True					,lowercase__   :     Optional[float] = 1.0					,lowercase__   :     str = "epsilon"					,lowercase__   :     str = "squaredcos_cap_v2"					,):
   if beta_schedule != "squaredcos_cap_v2":
    raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

   __lowercase							  = betas_for_alpha_bar(_UpperCamelCase			)

   __lowercase							  = 1.0 - self.betas
   __lowercase							  = torch.cumprod(self.alphas					,dim=0			)
   __lowercase							  = torch.tensor(1.0			)

   # standard deviation of the initial noise distribution
   __lowercase							  = 1.0

   # setable values
   __lowercase							  = None
   __lowercase							  = torch.from_numpy(np.arange(0					,_UpperCamelCase			)[::-1].copy()			)

   __lowercase							  = variance_type






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     torch.FloatTensor					,lowercase__   :     Optional[int] = None			):
   return sample






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     int					,lowercase__   :     Union[str, torch.device] = None			):
   __lowercase							  = num_inference_steps
   __lowercase							  = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
   __lowercase							  = (np.arange(0					,_UpperCamelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
   __lowercase							  = torch.from_numpy(_UpperCamelCase			).to(_UpperCamelCase			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     str					,lowercase__   :     Tuple=None					,lowercase__   :     Any=None					,lowercase__   :     Any=None			):
   if prev_timestep is None:
    __lowercase							  = t - 1

   __lowercase							  = self.alphas_cumprod[t]
   __lowercase							  = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
   __lowercase							  = 1 - alpha_prod_t
   __lowercase							  = 1 - alpha_prod_t_prev

   if prev_timestep == t - 1:
    __lowercase							  = self.betas[t]
   else:
    __lowercase							  = 1 - alpha_prod_t / alpha_prod_t_prev

   # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
   # and sample from it to get previous sample
   # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
   __lowercase							  = beta_prod_t_prev / beta_prod_t * beta

   if variance_type is None:
    __lowercase							  = self.config.variance_type

   # hacks - were probably added for training stability
   if variance_type == "fixed_small_log":
    __lowercase							  = torch.log(torch.clamp(_UpperCamelCase					,min=1e-2_0			)			)
    __lowercase							  = torch.exp(0.5 * variance			)
   elif variance_type == "learned_range":
    # NOTE difference with DDPM scheduler
    __lowercase							  = variance.log()
    __lowercase							  = beta.log()

    __lowercase							  = (predicted_variance + 1) / 2
    __lowercase							  = frac * max_log + (1 - frac) * min_log

   return variance






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     torch.FloatTensor					,lowercase__   :     int					,lowercase__   :     torch.FloatTensor					,lowercase__   :     Optional[int] = None					,lowercase__   :     Any=None					,lowercase__   :     bool = True					,):
   __lowercase							  = timestep

   if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
    __lowercase  ,	__lowercase							  = torch.split(_UpperCamelCase					,sample.shape[1]					,dim=1			)
   else:
    __lowercase							  = None

   # 1. compute alphas, betas
   if prev_timestep is None:
    __lowercase							  = t - 1

   __lowercase							  = self.alphas_cumprod[t]
   __lowercase							  = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
   __lowercase							  = 1 - alpha_prod_t
   __lowercase							  = 1 - alpha_prod_t_prev

   if prev_timestep == t - 1:
    __lowercase							  = self.betas[t]
    __lowercase							  = self.alphas[t]
   else:
    __lowercase							  = 1 - alpha_prod_t / alpha_prod_t_prev
    __lowercase							  = 1 - beta

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if self.config.prediction_type == "epsilon":
    __lowercase							  = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif self.config.prediction_type == "sample":
    __lowercase							  = model_output
   else:
    raise ValueError(
        F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
        ''' for the UnCLIPScheduler.'''			)

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
    __lowercase							  = torch.clamp(
        _UpperCamelCase					,-self.config.clip_sample_range					,self.config.clip_sample_range			)

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   __lowercase							  = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
   __lowercase							  = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   __lowercase							  = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   __lowercase							  = 0
   if t > 0:
    __lowercase							  = randn_tensor(
        model_output.shape					,dtype=model_output.dtype					,generator=_UpperCamelCase					,device=model_output.device			)

    __lowercase							  = self._get_variance(
        _UpperCamelCase					,predicted_variance=_UpperCamelCase					,prev_timestep=_UpperCamelCase					,)

    if self.variance_type == "fixed_small_log":
     __lowercase							  = variance
    elif self.variance_type == "learned_range":
     __lowercase							  = (0.5 * variance).exp()
    else:
     raise ValueError(
         F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
         ''' for the UnCLIPScheduler.'''			)

    __lowercase							  = variance * variance_noise

   __lowercase							  = pred_prev_sample + variance

   if not return_dict:
    return (pred_prev_sample,)

   return UnCLIPSchedulerOutput(prev_sample=_UpperCamelCase					,pred_original_sample=_UpperCamelCase			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     torch.FloatTensor					,lowercase__   :     torch.FloatTensor					,lowercase__   :     torch.IntTensor					,):
   # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
   __lowercase							  = self.alphas_cumprod.to(device=original_samples.device					,dtype=original_samples.dtype			)
   __lowercase							  = timesteps.to(original_samples.device			)

   __lowercase							  = alphas_cumprod[timesteps] ** 0.5
   __lowercase							  = sqrt_alpha_prod.flatten()
   while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
    __lowercase							  = sqrt_alpha_prod.unsqueeze(-1			)

   __lowercase							  = (1 - alphas_cumprod[timesteps]) ** 0.5
   __lowercase							  = sqrt_one_minus_alpha_prod.flatten()
   while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
    __lowercase							  = sqrt_one_minus_alpha_prod.unsqueeze(-1			)

   __lowercase							  = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples








import argparse
import copy



def _a				(	lowerCamelCase:   List[Any] )	->						List[str]:







      '''simple docstring'''






      __A          =				{}

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

      return dict_of_neighbours



def _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Optional[Any] )	->						Dict:







      '''simple docstring'''






      with open(lowerCamelCase ) as f:
            __A          =				f.read(1 )
      __A          =				start_node

      __A          =				[]

      __A          =				start_node

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

            first_solution.append(lowerCamelCase )
            __A          =				distance_of_first_solution + int(lowerCamelCase )
            __A          =				best_node

      first_solution.append(lowerCamelCase )

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

      __A          =				(
          distance_of_first_solution
          + int(dict_of_neighbours[first_solution[-2]][position][1] )
          - 1_00_00
      )
      return first_solution, distance_of_first_solution



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Any )	->						Any:







      '''simple docstring'''






      __A          =				[]

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

                  __A          =				copy.deepcopy(lowerCamelCase )
                  __A          =				kn
                  __A          =				n

                  __A          =				0

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

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

      __A          =				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:   Optional[int]					,				lowerCamelCase:   Dict					,				lowerCamelCase:   Any					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Union[str, Any] )	->						Any:







      '''simple docstring'''






      __A          =				1
      __A          =				first_solution
      __A          =				[]
      __A          =				distance_of_first_solution
      __A          =				solution

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

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

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

            __A          =				count + 1

      return best_solution_ever, best_cost



def _a				(	lowerCamelCase:   List[str]=None )	->						str:







      '''simple docstring'''






      __A          =				generate_neighbours(args.File )

      __A   ,						__A          =				generate_first_solution(
          args.File					,				lowerCamelCase )

      __A   ,						__A          =				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__					:							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())





