




'''simple docstring'''





import argparse
import copy


def      UpperCamelCase_(	snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      {}

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

							return dict_of_neighbours


def      UpperCamelCase_(	snake_case							:       Tuple							,				snake_case							:       Dict     ):

							'''simple docstring'''






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

							snake_case_						=      []

							snake_case_						=      start_node

							snake_case_						=      0
							while visiting not in first_solution:
														snake_case_						=      1_0_0_0_0
														for k in dict_of_neighbours[visiting]:
																					if int(k[1]     ) < int(__A     ) and k[0] not in first_solution:
																												snake_case_						=      k[1]
																												snake_case_						=      k[0]

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

							first_solution.append(__A     )

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

							snake_case_						=      (
							    distance_of_first_solution
							    + int(dict_of_neighbours[first_solution[-2]][position][1]     )
							    - 1_0_0_0_0
							)
							return first_solution, distance_of_first_solution


def      UpperCamelCase_(	snake_case							:       str							,				snake_case							:       Tuple     ):

							'''simple docstring'''






							snake_case_						=      []

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

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

																					snake_case_						=      0

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

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

							snake_case_						=      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							:       Dict							,				snake_case							:       Dict							,				snake_case							:       Optional[Any]							,				snake_case							:       Union[str, Any]							,				snake_case							:       Optional[Any]     ):

							'''simple docstring'''






							snake_case_						=      1
							snake_case_						=      first_solution
							snake_case_						=      []
							snake_case_						=      distance_of_first_solution
							snake_case_						=      solution

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

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

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

														snake_case_						=      count + 1

							return best_solution_ever, best_cost


def      UpperCamelCase_(	snake_case							:       Tuple=None     ):

							'''simple docstring'''






							snake_case_						=      generate_neighbours(args.File     )

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

							snake_case_					,      snake_case_						=      tabu_search(
							    __A							,				__A							,				__A							,				args.Iterations							,				args.Size							,				)

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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Optional[Any]      		= 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'''





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class __A			:



 '''simple docstring'''







 __lowerCamelCase    :       CommonSchedulerState

 # setable values
 __lowerCamelCase    :       jnp.ndarray
 __lowerCamelCase    :       jnp.ndarray
 __lowerCamelCase    :       Optional[int]          =       None



 @classmethod
 def 					a__  (cls ,   A ,   A ,   A       )    ->     str:


      """simple docstring"""



      return cls(common=A ,   init_noise_sigma=A ,   timesteps=A       )






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       DDPMSchedulerState






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCamelCase    :       jnp.dtype



 @property
 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      return True



 @register_to_config
 def __init__(self ,   A = 1_000 ,   A = 0.0001 ,   A = 0.02 ,   A = "linear" ,   A = None ,   A = "fixed_small" ,   A = True ,   A = "epsilon" ,   A = jnp.floataa ,   )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  dtype



 def 					a__  (self ,   A = None       )    ->     DDPMSchedulerState:


      """simple docstring"""



      if common is None:
           _a      =  CommonSchedulerState.create(self       )

      # standard deviation of the initial noise distribution
      _a      =  jnp.array(1.0 ,   dtype=self.dtype       )

      _a      =  jnp.arange(0 ,   self.config.num_train_timesteps       ).round()[::-1]

      return DDPMSchedulerState.create(
          common=A ,   init_noise_sigma=A ,   timesteps=A ,   )



 def 					a__  (self ,   A ,   A ,   A = None       )    ->     jnp.ndarray:


      """simple docstring"""



      return sample



 def 					a__  (self ,   A ,   A ,   A = ()       )    ->     DDPMSchedulerState:


      """simple docstring"""



      _a      =  self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      _a      =  (jnp.arange(0 ,   A       ) * step_ratio).round()[::-1]

      return state.replace(
          num_inference_steps=A ,   timesteps=A ,   )



 def 					a__  (self ,   A ,   A ,   A=None ,   A=None       )    ->     int:


      """simple docstring"""



      _a      =  state.common.alphas_cumprod[t]
      _a      =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype       )       )

      # 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
      _a      =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           _a      =  jnp.clip(A ,   a_min=1E-20       )
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           _a      =  jnp.log(jnp.clip(A ,   a_min=1E-20       )       )
      elif variance_type == "fixed_large":
           _a      =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           _a      =  jnp.log(state.common.betas[t]       )
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           _a      =  variance
           _a      =  state.common.betas[t]
           _a      =  (predicted_variance + 1) / 2
           _a      =  frac * max_log + (1 - frac) * min_log

      return variance



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = True ,   )    ->     Union[FlaxDDPMSchedulerOutput, Tuple]:


      """simple docstring"""



      _a      =  timestep

      if key is None:
           _a      =  jax.random.PRNGKey(0       )

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           _a  ,    _a      =  jnp.split(A ,   sample.shape[1] ,   axis=1       )
      else:
           _a      =  None

      # 1. compute alphas, betas
      _a      =  state.common.alphas_cumprod[t]
      _a      =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype       )       )
      _a      =  1 - alpha_prod_t
      _a      =  1 - alpha_prod_t_prev

      # 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":
           _a      =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           _a      =  model_output
      elif self.config.prediction_type == "v_prediction":
           _a      =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
               ''' for the FlaxDDPMScheduler.'''       )

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
           _a      =  jnp.clip(A ,   -1 ,   1       )

      # 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
      _a      =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      _a      =  state.common.alphas[t] ** 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
      _a      =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
           _a      =  jax.random.split(A ,   num=1       )
           _a      =  jax.random.normal(A ,   shape=model_output.shape ,   dtype=self.dtype       )
           return (self._get_variance(A ,   A ,   predicted_variance=A       ) ** 0.5) * noise

      _a      =  jnp.where(t > 0 ,   random_variance() ,   jnp.zeros(model_output.shape ,   dtype=self.dtype       )       )

      _a      =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=A ,   state=A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return add_noise_common(state.common ,   A ,   A ,   A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return get_velocity_common(state.common ,   A ,   A ,   A       )



 def __len__(self       )    ->     Tuple:


      """simple docstring"""



      return self.config.num_train_timesteps



