



from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase							 =					TypeVar("""T""")

def 			A_      (	__a     :				List[str]		):

				"""simple docstring"""
				return (position - 1) // 2

def 			A_      (	__a     :				Tuple		):

				"""simple docstring"""
				return (2 * position) + 1

def 			A_      (	__a     :				List[Any]		):

				"""simple docstring"""
				return (2 * position) + 2

class  __snake_case      (   Generic[T]):







							'''simple docstring'''






							def __init__(							self		):
											a__				      =		[]
											a__				      =		{}
											a__				      =		0


							def __len__(							self		):
											return self.elements


							def __repr__(							self		):
											return str(self.heap		)


							def _a      (							self		):
											return self.elements == 0


							def _a      (							self  ,  a_  ,  a_		):
											self.heap.append((elem, weight)		)
											a__				      =		self.elements
											self.elements += 1
											self._bubble_up(_UpperCAmelCase		)


							def _a      (							self		):




											if self.elements > 1:
															self._swap_nodes(0  ,  self.elements - 1		)
											a__      ,			a__				      =		self.heap.pop()
											del self.position_map[elem]
											self.elements -= 1
											if self.elements > 0:
															a__      ,			a__				      =		self.heap[0]
															self._bubble_down(_UpperCAmelCase		)
											return elem


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		self.position_map[elem]
											a__				      =		(elem, weight)
											if position > 0:
															a__				      =		get_parent_position(_UpperCAmelCase		)
															a__      ,			a__				      =		self.heap[parent_position]
															if parent_weight > weight:
																			self._bubble_up(_UpperCAmelCase		)
															else:
																			self._bubble_down(_UpperCAmelCase		)
											else:
															self._bubble_down(_UpperCAmelCase		)


							def _a      (							self  ,  a_		):
											a__				      =		self.position_map[elem]
											if curr_pos == 0:
															return None
											a__				      =		get_parent_position(_UpperCAmelCase		)
											a__      ,			a__				      =		self.heap[curr_pos]
											a__      ,			a__				      =		self.heap[parent_position]
											if parent_weight > weight:
															self._swap_nodes(_UpperCAmelCase  ,  _UpperCAmelCase		)
															return self._bubble_up(_UpperCAmelCase		)
											return None


							def _a      (							self  ,  a_		):
											a__				      =		self.position_map[elem]
											a__      ,			a__				      =		self.heap[curr_pos]
											a__				      =		get_child_left_position(_UpperCAmelCase		)
											a__				      =		get_child_right_position(_UpperCAmelCase		)
											if child_left_position < self.elements and child_right_position < self.elements:
															a__      ,			a__				      =		self.heap[child_left_position]
															a__      ,			a__				      =		self.heap[child_right_position]
															if child_right_weight < child_left_weight and child_right_weight < weight:
																			self._swap_nodes(_UpperCAmelCase  ,  _UpperCAmelCase		)
																			return self._bubble_down(_UpperCAmelCase		)
											if child_left_position < self.elements:
															a__      ,			a__				      =		self.heap[child_left_position]
															if child_left_weight < weight:
																			self._swap_nodes(_UpperCAmelCase  ,  _UpperCAmelCase		)
																			return self._bubble_down(_UpperCAmelCase		)
											else:
															return None
											if child_right_position < self.elements:
															a__      ,			a__				      =		self.heap[child_right_position]
															if child_right_weight < weight:
																			self._swap_nodes(_UpperCAmelCase  ,  _UpperCAmelCase		)
																			return self._bubble_down(_UpperCAmelCase		)
											return None







							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		self.heap[nodea_pos][0]
											a__				      =		self.heap[nodea_pos][0]
											a__      ,			a__				      =		(
											    self.heap[nodea_pos],
											    self.heap[nodea_pos],
											)
											a__				      =		nodea_pos
											a__				      =		nodea_pos




class  __snake_case      (   Generic[T]):







							'''simple docstring'''






							def __init__(							self		):
											a__				      =		{}
											a__				      =		0


							def __repr__(							self		):
											return str(self.connections		)


							def __len__(							self		):
											return self.nodes


							def _a      (							self  ,  a_		):




											if node not in self.connections:
															a__				      =		{}
															self.nodes += 1







							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											self.add_node(_UpperCAmelCase		)
											self.add_node(_UpperCAmelCase		)
											a__				      =		weight
											a__				      =		weight




def 			A_      (	__a     :				Optional[int] ,		):

				"""simple docstring"""
				a__				      =		{node: maxsize for node in graph.connections}
				a__				      =		{node: None for node in graph.connections}

				a__				      =		MinPriorityQueue()
				for node, weight in dist.items():
								priority_queue.push(lowerCAmelCase__ ,		lowerCAmelCase__		)

				if priority_queue.is_empty():
								return dist, parent

				# initialization
				a__				      =		priority_queue.extract_min()
				a__				      =		0
				for neighbour in graph.connections[node]:
								if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
												a__				      =		dist[node] + graph.connections[node][neighbour]
												priority_queue.update_key(lowerCAmelCase__ ,		dist[neighbour]		)
												a__				      =		node

    # running prim's algorithm
				while not priority_queue.is_empty():
								a__				      =		priority_queue.extract_min()
								for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																a__				      =		dist[node] + graph.connections[node][neighbour]
																priority_queue.update_key(lowerCAmelCase__ ,		dist[neighbour]		)
																a__				      =		node
				return dist, parent






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			A_      (	__a     :				str = "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(__a ,		headers=__a		).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__":
	UpperCAmelCase							 =					"""headphones"""
	get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")

