



from random import randint, random






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    bool = False				,   __lowerCamelCase	:    bool = False				,   __lowerCamelCase	:    int = 5				,   )  ->						list:
					__lowerCAmelCase	   =[[-1] * number_of_cells]  # Create a highway without any car
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =max(__lowerCamelCase				,   0	)
					while i < number_of_cells:
										__lowerCAmelCase	   =(
										    randint(0				,   __lowerCamelCase	) if random_speed else initial_speed
										)  # Place the cars
										i += (
										    randint(1				,   max_speed * 2	) if random_frequency else frequency
										)  # Arbitrary number, may need tuning
					return highway






def 					__lowerCAmelCase		(  __lowerCamelCase	:    list				,   __lowerCamelCase	:    int	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =highway_now[car_index + 1 :]
					for cell in range(len(__lowerCamelCase	)	):  # May need a better name for this
										if cells[cell] != -1:  # If the cell is not empty then
															return distance  # we have the distance we wanted
										distance += 1
					# Here if the car is near the end of the highway
					return distance + get_distance(__lowerCamelCase				,   -1	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    list				,   __lowerCamelCase	:    float				,   __lowerCamelCase	:    int	)  ->						list:
					__lowerCAmelCase	   =len(__lowerCamelCase	)
					# Beforce calculations, the highway is empty
					__lowerCAmelCase	   =[-1] * number_of_cells

					for car_index in range(__lowerCamelCase	):
										if highway_now[car_index] != -1:
															# Add 1 to the current speed of the car and cap the speed
															__lowerCAmelCase	   =min(highway_now[car_index] + 1				,   __lowerCamelCase	)
															# Number of empty cell before the next car
															__lowerCAmelCase	   =get_distance(__lowerCamelCase				,   __lowerCamelCase	) - 1
															# We can't have the car causing an accident
															__lowerCAmelCase	   =min(next_highway[car_index]				,   __lowerCamelCase	)
															if random() < probability:
																				# Randomly, a driver will slow down
																				__lowerCAmelCase	   =max(next_highway[car_index] - 1				,   0	)
					return next_highway






def 					__lowerCAmelCase		(  __lowerCamelCase	:    list				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    float				,   __lowerCamelCase	:    int	)  ->						list:
					__lowerCAmelCase	   =len(highway[0]	)

					for i in range(__lowerCamelCase	):
										__lowerCAmelCase	   =update(highway[i]				,   __lowerCamelCase				,   __lowerCamelCase	)
										__lowerCAmelCase	   =[-1] * number_of_cells

										for car_index in range(__lowerCamelCase	):
															__lowerCAmelCase	   =next_speeds_calculated[car_index]
															if speed != -1:
																				# Change the position based on the speed (with % to create the loop)
																				__lowerCAmelCase	   =(car_index + speed) % number_of_cells
																				# Commit the change of position
																				__lowerCAmelCase	   =speed
										highway.append(__lowerCamelCase	)

					return highway


if __name__ == "__main__":
				import doctest

				doctest.testmod()









from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str = "laptop"	)  ->						DataFrame:
					__lowerCAmelCase	   =f"""https://www.amazon.in/laptop/s?k={product}"""
					__lowerCAmelCase	   ={
					    """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""",
					}
					__lowerCAmelCase	   =BeautifulSoup(requests.get(__lowerCamelCase				,   headers=__lowerCamelCase	).text	)
					# Initialize a Pandas dataframe with the column titles
					__lowerCAmelCase	   =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:
															__lowerCAmelCase	   =item.ha.text
															__lowerCAmelCase	   ="""https://www.amazon.in/""" + item.ha.a["""href"""]
															__lowerCAmelCase	   =item.find("""span"""				,   attrs={"""class""": """a-offscreen"""}	).text
															try:
																				__lowerCAmelCase	   =item.find("""span"""				,   attrs={"""class""": """a-icon-alt"""}	).text
															except AttributeError:
																				__lowerCAmelCase	   ="""Not available"""
															try:
																				__lowerCAmelCase	   =(
																				    """₹"""
																				    + item.find(
																				        """span"""				,   attrs={"""class""": """a-price a-text-price"""}	).text.split("""₹"""	)[1]
																				)
															except AttributeError:
																				__lowerCAmelCase	   =""""""
															try:
																				__lowerCAmelCase	   =float(
																				    (
																				        (
																				            float(product_mrp.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				            - float(product_price.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				        )
																				        / float(product_mrp.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				    )
																				    * 100	)
															except ValueError:
																				__lowerCAmelCase	   =float("""nan"""	)
										except AttributeError:
															pass
										__lowerCAmelCase	   =[
										    product_title,
										    product_link,
										    product_price,
										    product_rating,
										    product_mrp,
										    discount,
										]
					__lowerCAmelCase	   =""" """
					__lowerCAmelCase	   =""" """
					data_frame.index += 1
					return data_frame


if __name__ == "__main__":
				lowercase_	       =			'''headphones'''
				get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")




