





'''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 			_lowercase						:
     _UpperCAmelCase										=					42

     # setable values
     _UpperCAmelCase										=					42
     _UpperCAmelCase										=					42
     _UpperCAmelCase										=					None







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ,   A__    ,   A__    ) ->	Optional[Any]:
      return cls(common=A__    ,   init_noise_sigma=A__    ,   timesteps=A__    )




@dataclass
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					42




class 			_lowercase						(       __a				,		__a					):
     _UpperCAmelCase										=					[e.name for e in FlaxKarrasDiffusionSchedulers]

     _UpperCAmelCase										=					42







     @property
     def     UpperCamelCase			( self    ) ->	List[Any]:
      return True







     @register_to_config
     def __init__( self    ,   A__ = 10_00    ,   A__ = 0.0_0_0_1    ,   A__ = 0.0_2    ,   A__ = "linear"    ,   A__ = None    ,   A__ = "fixed_small"    ,   A__ = True    ,   A__ = "epsilon"    ,   A__ = jnp.floataa    ,   ) ->	str:
      snake_case				=			dtype







     def     UpperCamelCase			( self    ,   A__ = None    ) ->	DDPMSchedulerState:
      if common is None:
       snake_case				=			CommonSchedulerState.create(self    )

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

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

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







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__ = None    ) ->	jnp.ndarray:
      return sample







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__ = ()    ) ->	DDPMSchedulerState:
      snake_case				=			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
      snake_case				=			(jnp.arange(0    ,   A__    ) * step_ratio).round()[::-1]

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







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__=None    ,   A__=None    ) ->	List[str]:
      snake_case				=			state.common.alphas_cumprod[t]
      snake_case				=			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
      snake_case				=			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

      return variance







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__ = None    ,   A__ = True    ,   ) ->	Union[FlaxDDPMSchedulerOutput, Tuple]:
      snake_case				=			timestep

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

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

      # 1. compute alphas, betas
      snake_case				=			state.common.alphas_cumprod[t]
      snake_case				=			jnp.where(t > 0    ,   state.common.alphas_cumprod[t - 1]    ,   jnp.array(1.0    ,   dtype=self.dtype    )    )
      snake_case				=			1 - alpha_prod_t
      snake_case				=			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":
       snake_case				=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
       snake_case				=			model_output
      elif self.config.prediction_type == "v_prediction":
       snake_case				=			(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:
       snake_case				=			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
      snake_case				=			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      snake_case				=			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
      snake_case				=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

      snake_case				=			pred_prev_sample + variance

      if not return_dict:
       return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=A__    ,   state=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	jnp.ndarray:
      return add_noise_common(state.common    ,   A__    ,   A__    ,   A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	jnp.ndarray:
      return get_velocity_common(state.common    ,   A__    ,   A__    ,   A__    )







     def __len__( self    ) ->	Tuple:
      return self.config.num_train_timesteps








'''simple docstring'''







import argparse
import copy

def    __UpperCamelCase	(	a    : Union[str, Any]     ) ->Tuple:
 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	(	a    : Dict					,   a    : Tuple     ) ->int:

 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_0000
  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_0000
 )
 return first_solution, distance_of_first_solution

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : str     ) ->str:
 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 a     : x[index_of_last_item_in_the_list]     )
 return neighborhood_of_solution

def    __UpperCamelCase	(	a    : Any					,   a    : Optional[Any]					,   a    : int					,   a    : Optional[int]					,   a    : Union[str, Any]     ) ->List[Any]:
 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	(	a    : Union[str, Any]=None     ) ->Optional[Any]:
 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__":
   _lowercase       						=			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())

