


import argparse
import copy


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		{}

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

						return dict_of_neighbours


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):

						with open(lowercase__      ) as f:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		f.read(1      )
						__SCREAMING_SNAKE_CASE						:     str					=		start_node

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]

						__SCREAMING_SNAKE_CASE						:     str					=		start_node

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		0
						while visiting not in first_solution:
												__SCREAMING_SNAKE_CASE						:     str					=		10000
												for k in dict_of_neighbours[visiting]:
																		if int(k[1]      ) < int(lowercase__      ) and k[0] not in first_solution:
																								__SCREAMING_SNAKE_CASE						:     List[str]					=		k[1]
																								__SCREAMING_SNAKE_CASE						:     Dict					=		k[0]

												first_solution.append(lowercase__      )
												__SCREAMING_SNAKE_CASE						:     int					=		distance_of_first_solution + int(lowercase__      )
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		best_node

						first_solution.append(lowercase__      )

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

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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     int					=		[]

						for n in solution[1:-1]:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		solution.index(lowercase__      )
												for kn in solution[1:-1]:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		solution.index(lowercase__      )
																		if n == kn:
																								continue

																		__SCREAMING_SNAKE_CASE						:     Any					=		copy.deepcopy(lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		kn
																		__SCREAMING_SNAKE_CASE						:     str					=		n

																		__SCREAMING_SNAKE_CASE						:     Dict					=		0

																		for k in _tmp[:-1]:
																								__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		_tmp[_tmp.index(lowercase__      ) + 1]
																								for i in dict_of_neighbours[k]:
																														if i[0] == next_node:
																																				__SCREAMING_SNAKE_CASE						:     Tuple					=		distance + int(i[1]      )
																		_tmp.append(lowercase__      )

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

						__SCREAMING_SNAKE_CASE						:     int					=		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__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		1
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		first_solution
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		distance_of_first_solution
						__SCREAMING_SNAKE_CASE						:     List[str]					=		solution

						while count <= iters:
												__SCREAMING_SNAKE_CASE						:     int					=		find_neighborhood(lowercase__    ,       lowercase__      )
												__SCREAMING_SNAKE_CASE						:     Tuple					=		0
												__SCREAMING_SNAKE_CASE						:     Tuple					=		neighborhood[index_of_best_solution]
												__SCREAMING_SNAKE_CASE						:     Tuple					=		len(lowercase__      ) - 1

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

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

												__SCREAMING_SNAKE_CASE						:     Tuple					=		count + 1

						return best_solution_ever, best_cost


def        _UpperCamelCase							(      lowercase__=None      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		generate_neighbours(args.File      )

						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		generate_first_solution(
						    args.File    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		tabu_search(
						    lowercase__    ,       lowercase__    ,       lowercase__    ,       args.Iterations    ,       args.Size    ,       )

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


if __name__ == "__main__":
						__lowerCAmelCase	: int  				=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'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
