






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def      __A					(     _lowercase = "laptop" ):

					'''simple docstring'''







					_A								= f"""https://www.amazon.in/laptop/s?k={product}"""
					_A								= {
					    '''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (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(''','''					,			'''''' ) )
																				    )
																				    * 1_00 )
															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__":
				__A      =				'headphones'
				get_amazon_product_data(product).to_csv(f'Amazon Product Data for {product}.csv')











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					global f  # a global dp table for knapsack
					if f[i][j] < 0:
										if j < wt[i - 1]:
															_A								= mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )
										else:
															_A								= max(
															    mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			_lowercase )					,			mf_knapsack(i - 1					,			_lowercase					,			_lowercase					,			j - wt[i - 1] ) + val[i - 1]					,			)
										_A								= val
					return f[i][j]




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= [[0] * (w + 1) for _ in range(n + 1 )]

					for i in range(1					,			n + 1 ):
										for w_ in range(1					,			w + 1 ):
															if wt[i - 1] <= w_:
																				_A								= max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]					,			dp[i - 1][w_] )
															else:
																				_A								= dp[i - 1][w_]

					return dp[n][w_], dp




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					if not (isinstance(_lowercase					,			(list, tuple) ) and isinstance(_lowercase					,			(list, tuple) )):
										raise ValueError(
										    '''Both the weights and values vectors must be either lists or tuples''' )

					_A								= len(_lowercase )
					if num_items != len(_lowercase ):
										_A								= (
										    '''The number of weights must be the same as the number of values.\n'''
										    f"""But got {num_items} weights and {len(_lowercase )} values"""
										)
										raise ValueError(_lowercase )
					for i in range(_lowercase ):
										if not isinstance(wt[i]					,			_lowercase ):
															_A								= (
															    '''All weights must be integers but got weight of '''
															    f"""type {type(wt[i] )} at index {i}"""
															)
															raise TypeError(_lowercase )

					_A							,_A								= knapsack(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					_A								= set()
					_construct_solution(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

					return optimal_val, example_optional_set




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					if i > 0 and j > 0:
										if dp[i - 1][j] == dp[i][j]:
															_construct_solution(_lowercase					,			_lowercase					,			i - 1					,			_lowercase					,			_lowercase )
										else:
															optimal_set.add(_lowercase )
															_construct_solution(_lowercase					,			_lowercase					,			i - 1					,			j - wt[i - 1]					,			_lowercase )


if __name__ == "__main__":
				__A      =				[3, 2, 4, 4]
				__A      =				[4, 3, 2, 3]
				__A      =				4
				__A      =				6
				__A      =				[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
				__A ,  __A      =				knapsack(w, wt, val, n)
				print(optimal_solution)
				print(mf_knapsack(n, wt, val, w))  # switched the n and w

				# testing the dynamic programming problem with example
				# the optimal subset for the above example are items 3 and 4
				__A ,  __A      =				knapsack_with_example_solution(w, wt, val)
				assert optimal_solution == 8
				assert optimal_subset == {3, 4}
				print('optimal_value = ', optimal_solution)
				print('An optimal subset corresponding to the optimal value', optimal_subset)



