

import argparse
import copy

def 		UpperCamelCase   (lowercase_:					Any   )	-> Union[str, Any]:
	A__				:       int	  =						{}

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

	return dict_of_neighbours

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					List[str]   )	-> Tuple:

	with open(lowercase_   ) as f:
		A__				:       List[Any]	  =						f.read(1   )
	A__				:       Union[str, Any]	  =						start_node

	A__				:       List[Any]	  =						[]

	A__				:       str	  =						start_node

	A__				:       List[str]	  =						0
	while visiting not in first_solution:
		A__				:       Dict	  =						10000
		for k in dict_of_neighbours[visiting]:
			if int(k[1]   ) < int(lowercase_   ) and k[0] not in first_solution:
				A__				:       Tuple	  =						k[1]
				A__				:       Optional[Any]	  =						k[0]

		first_solution.append(lowercase_   )
		A__				:       Optional[int]	  =						distance_of_first_solution + int(lowercase_   )
		A__				:       str	  =						best_node

	first_solution.append(lowercase_   )

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

	A__				:       int	  =						(
	    distance_of_first_solution
	    + int(dict_of_neighbours[first_solution[-2]][position][1]   )
	    - 10000
	)
	return first_solution, distance_of_first_solution

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					str   )	-> Any:
	A__				:       int	  =						[]

	for n in solution[1:-1]:
		A__				:       Union[str, Any]	  =						solution.index(lowercase_   )
		for kn in solution[1:-1]:
			A__				:       List[Any]	  =						solution.index(lowercase_   )
			if n == kn:
				continue

			A__				:       Union[str, Any]	  =						copy.deepcopy(lowercase_   )
			A__				:       int	  =						kn
			A__				:       Optional[int]	  =						n

			A__				:       Dict	  =						0

			for k in _tmp[:-1]:
				A__				:       Dict	  =						_tmp[_tmp.index(lowercase_   ) + 1]
				for i in dict_of_neighbours[k]:
					if i[0] == next_node:
						A__				:       List[Any]	  =						distance + int(i[1]   )
			_tmp.append(lowercase_   )

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

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

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

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Tuple       , lowercase_:					Dict       , lowercase_:					Dict       , lowercase_:					Tuple   )	-> Union[str, Any]:
	A__				:       Union[str, Any]	  =						1
	A__				:       List[str]	  =						first_solution
	A__				:       Dict	  =						[]
	A__				:       Union[str, Any]	  =						distance_of_first_solution
	A__				:       Any	  =						solution

	while count <= iters:
		A__				:       Optional[int]	  =						find_neighborhood(lowercase_       , lowercase_   )
		A__				:       Union[str, Any]	  =						0
		A__				:       str	  =						neighborhood[index_of_best_solution]
		A__				:       int	  =						len(lowercase_   ) - 1

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

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

		A__				:       Any	  =						count + 1

	return best_solution_ever, best_cost

def 		UpperCamelCase   (lowercase_:					Union[str, Any]=None   )	-> List[str]:
	A__				:       Union[str, Any]	  =						generate_neighbours(args.File   )

	A__  ,  A__				:       int	  =						generate_first_solution(
	    args.File       , lowercase_   )

	A__  ,  A__				:       str	  =						tabu_search(
	    lowercase_       , lowercase_       , lowercase_       , args.Iterations       , args.Size       , )

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


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






import tensorflow as tf

from ...tf_utils import shape_list



class        _a		(tf.keras.layers.Layer							):


					'''simple docstring'''






					def __init__(					self       ,			A__       ,			A__       ,			A__       ,			A__       ,			A__=1       ,			A__=False       ,			**A__     ):
						super().__init__(**A__     )

						A__				:       Tuple	  =						vocab_size
						A__				:       List[str]	  =						d_embed
						A__				:       int	  =						d_proj

						A__				:       Tuple	  =						cutoffs + [vocab_size]
						A__				:       Any	  =						[0] + self.cutoffs
						A__				:       str	  =						div_val

						A__				:       int	  =						self.cutoffs[0]
						A__				:       List[str]	  =						len(self.cutoffs     ) - 1
						A__				:       Optional[int]	  =						self.shortlist_size + self.n_clusters
						A__				:       List[Any]	  =						keep_order

						A__				:       Optional[int]	  =						[]
						A__				:       Optional[Any]	  =						[]






					def     __A     (					self       ,			A__     ):
						if self.n_clusters > 0:
							A__				:       List[str]	  =						self.add_weight(
							    shape=(self.n_clusters, self.d_embed)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name="""cluster_weight"""     )
							A__				:       Union[str, Any]	  =						self.add_weight(
							    shape=(self.n_clusters,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name="""cluster_bias"""     )

						if self.div_val == 1:
							for i in range(len(self.cutoffs     )     ):
								if self.d_proj != self.d_embed:
									A__				:       List[str]	  =						self.add_weight(
									    shape=(self.d_embed, self.d_proj)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_projs_._{i}"""       ,			)
									self.out_projs.append(A__     )
								else:
									self.out_projs.append(A__     )
								A__				:       int	  =						self.add_weight(
								    shape=(self.vocab_size, self.d_embed)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._weight"""       ,			)
								A__				:       str	  =						self.add_weight(
								    shape=(self.vocab_size,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._bias"""       ,			)
								self.out_layers.append((weight, bias)     )
						else:
							for i in range(len(self.cutoffs     )     ):
								A__  ,  A__				:       Optional[int]	  =						self.cutoff_ends[i], self.cutoff_ends[i + 1]
								A__				:       Optional[int]	  =						self.d_embed // (self.div_val**i)

								A__				:       Optional[Any]	  =						self.add_weight(
								    shape=(d_emb_i, self.d_proj)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_projs_._{i}"""     )
								self.out_projs.append(A__     )
								A__				:       List[str]	  =						self.add_weight(
								    shape=(r_idx - l_idx, d_emb_i)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._weight"""       ,			)
								A__				:       Optional[int]	  =						self.add_weight(
								    shape=(r_idx - l_idx,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._bias"""       ,			)
								self.out_layers.append((weight, bias)     )
						super().build(A__     )






					@staticmethod
					def     __A     (					A__       ,			A__       ,			A__       ,			A__=None     ):
						A__				:       Tuple	  =						x
						if proj is not None:
							A__				:       Tuple	  =						tf.einsum("""ibd,ed->ibe"""       ,			A__       ,			A__     )
						return tf.einsum("""ibd,nd->ibn"""       ,			A__       ,			A__     ) + b






					@staticmethod
					def     __A     (					A__       ,			A__     ):
						A__				:       Optional[Any]	  =						shape_list(A__     )
						A__				:       Optional[Any]	  =						tf.range(lp_size[0]       ,			dtype=target.dtype     )
						A__				:       List[str]	  =						tf.stack([r, target]       ,			1     )
						return tf.gather_nd(A__       ,			A__     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__=True       ,			A__=False     ):
						A__				:       str	  =						0
						if self.n_clusters == 0:
							A__				:       List[str]	  =						self._logit(A__       ,			self.out_layers[0][0]       ,			self.out_layers[0][1]       ,			self.out_projs[0]     )
							if target is not None:
								A__				:       Union[str, Any]	  =						tf.nn.sparse_softmax_cross_entropy_with_logits(labels=A__       ,			logits=A__     )
							A__				:       int	  =						tf.nn.log_softmax(A__       ,			axis=-1     )
						else:
							A__				:       Any	  =						shape_list(A__     )
							A__				:       int	  =						[]
							A__				:       Union[str, Any]	  =						tf.zeros(hidden_sizes[:2]     )
							for i in range(len(self.cutoffs     )     ):
								A__  ,  A__				:       Union[str, Any]	  =						self.cutoff_ends[i], self.cutoff_ends[i + 1]
								if target is not None:
									A__				:       Any	  =						(target >= l_idx) & (target < r_idx)
									A__				:       Optional[Any]	  =						tf.where(A__     )
									A__				:       int	  =						tf.boolean_mask(A__       ,			A__     ) - l_idx

								if self.div_val == 1:
									A__				:       Any	  =						self.out_layers[0][0][l_idx:r_idx]
									A__				:       List[Any]	  =						self.out_layers[0][1][l_idx:r_idx]
								else:
									A__				:       Optional[int]	  =						self.out_layers[i][0]
									A__				:       Tuple	  =						self.out_layers[i][1]

								if i == 0:
									A__				:       Union[str, Any]	  =						tf.concat([cur_W, self.cluster_weight]       ,			0     )
									A__				:       Optional[int]	  =						tf.concat([cur_b, self.cluster_bias]       ,			0     )

									A__				:       Any	  =						self._logit(A__       ,			A__       ,			A__       ,			self.out_projs[0]     )
									A__				:       Optional[Any]	  =						tf.nn.log_softmax(A__     )
									out.append(head_logprob[..., : self.cutoffs[0]]     )
									if target is not None:
										A__				:       Optional[Any]	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[str]	  =						self._gather_logprob(A__       ,			A__     )
								else:
									A__				:       str	  =						self._logit(A__       ,			A__       ,			A__       ,			self.out_projs[i]     )
									A__				:       Dict	  =						tf.nn.log_softmax(A__     )
									A__				:       List[str]	  =						self.cutoffs[0] + i - 1  # No probability for the head cluster
									A__				:       Optional[Any]	  =						head_logprob[..., cluster_prob_idx, None] + tail_logprob
									out.append(A__     )
									if target is not None:
										A__				:       Any	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[Any]	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[Any]	  =						self._gather_logprob(A__       ,			A__     )
										cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
								if target is not None:
									loss += tf.scatter_nd(A__       ,			-cur_logprob       ,			shape_list(A__     )     )
							A__				:       List[str]	  =						tf.concat(A__       ,			axis=-1     )

						if target is not None:
							if return_mean:
								A__				:       List[Any]	  =						tf.reduce_mean(A__     )
							# Add the training-time loss value to the layer using `self.add_loss()`.
							self.add_loss(A__     )

							# Log the loss as a metric (we could log arbitrary metrics,
							# including different metrics for training and inference.
							self.add_metric(A__       ,			name=self.name       ,			aggregation="""mean""" if return_mean else """"""     )

						return out



