





"""simple docstring"""
import argparse
import copy






def 					A__				(			UpperCamelCase       ):
							A		=  {}

							with open(UpperCamelCase       ) 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__				(			UpperCamelCase					,  UpperCamelCase       ):

							with open(UpperCamelCase       ) as f:
														A		=  f.read(1       )
							A		=  start_node

							A		=  []

							A		=  start_node

							A		=  0
							while visiting not in first_solution:
														A		=  10_000
														for k in dict_of_neighbours[visiting]:
																					if int(k[1]       ) < int(UpperCamelCase       ) and k[0] not in first_solution:
																												A		=  k[1]
																												A		=  k[0]

														first_solution.append(UpperCamelCase       )
														A		=  distance_of_first_solution + int(UpperCamelCase       )
														A		=  best_node

							first_solution.append(UpperCamelCase       )

							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]       )
							    - 10_000
							)
							return first_solution, distance_of_first_solution






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  []

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

																					A		=  copy.deepcopy(UpperCamelCase       )
																					A		=  kn
																					A		=  n

																					A		=  0

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

																					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 UpperCamelCase       : x[index_of_last_item_in_the_list]       )
							return neighborhood_of_solution






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  1
							A		=  first_solution
							A		=  []
							A		=  distance_of_first_solution
							A		=  solution

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

														A		=  False
														while not found:
																					A		=  0
																					while i < len(UpperCamelCase       ):
																												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(UpperCamelCase       ) >= size:
																					tabu_list.pop(0       )

														A		=  count + 1

							return best_solution_ever, best_cost






def 					A__				(			UpperCamelCase=None       ):
							A		=  generate_neighbours(args.File       )

							A,	A		=  generate_first_solution(
							    args.File					,  UpperCamelCase       )

							A,	A		=  tabu_search(
							    UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  args.Iterations					,  args.Size					,  )

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


if __name__ == "__main__":
		_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"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					A__				(			UpperCamelCase = "laptop"       ):
							A		=  F"https://www.amazon.in/laptop/s?k={product}"
							A		=  {
							    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
							    "Accept-Language": "en-US, en;q=0.5",
							}
							A		=  BeautifulSoup(requests.get(UpperCamelCase					,  headers=UpperCamelCase       ).text       )
							# Initialize a Pandas dataframe with the column titles
							A		=  DataFrame(
							    columns=[
							        "Product Title",
							        "Product Link",
							        "Current Price of the product",
							        "Product Rating",
							        "MRP of the product",
							        "Discount",
							    ]       )
							# Loop through each entry and store them in the dataframe
							for item, _ in zip_longest(
							    soup.find_all(
							        "div"					,  attrs={"class": "s-result-item", "data-component-type": "s-search-result"}					,  )					,  soup.find_all("div"					,  attrs={"class": "a-row a-size-base a-color-base"}       )					,  ):
														try:
																					A		=  item.ha.text
																					A		=  "https://www.amazon.in/" + item.ha.a["href"]
																					A		=  item.find("span"					,  attrs={"class": "a-offscreen"}       ).text
																					try:
																												A		=  item.find("span"					,  attrs={"class": "a-icon-alt"}       ).text
																					except AttributeError:
																												A		=  "Not available"
																					try:
																												A		=  (
																												    "₹"
																												    + item.find(
																												        "span"					,  attrs={"class": "a-price a-text-price"}       ).text.split("₹"       )[1]
																												)
																					except AttributeError:
																												A		=  ""
																					try:
																												A		=  float(
																												    (
																												        (
																												            float(product_mrp.strip("₹"       ).replace(","					,  ""       )       )
																												            - float(product_price.strip("₹"       ).replace(","					,  ""       )       )
																												        )
																												        / float(product_mrp.strip("₹"       ).replace(","					,  ""       )       )
																												    )
																												    * 100       )
																					except ValueError:
																												A		=  float("nan"       )
														except AttributeError:
																					pass
														A		=  [
														    product_title,
														    product_link,
														    product_price,
														    product_rating,
														    product_mrp,
														    discount,
														]
							A		=  " "
							A		=  " "
							data_frame.index += 1
							return data_frame


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= 'headphones'
		get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")


