


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					list(A__				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					list(A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					0
			for i in range(len(A__				)				):
						if lista[i] != lista[i]:
									count += 1
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'_'
			if count > 1:
						return False
			else:
						return "".join(A__				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
			while True:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					['$'] * len(A__				)
						SCREAMING_SNAKE_CASE_		:     Tuple					=					[]
						for i in range(len(A__				)				):
									for j in range(i + 1,      len(A__				)				):
												SCREAMING_SNAKE_CASE_		:     str					=					compare_string(binary[i],      binary[j]				)
												if k is False:
															SCREAMING_SNAKE_CASE_		:     Dict					=					'*'
															SCREAMING_SNAKE_CASE_		:     Tuple					=					'*'
															temp.append('X'				)
						for i in range(len(A__				)				):
									if checka[i] == "$":
												pi.append(binary[i]				)
						if len(A__				) == 0:
									return pi
						SCREAMING_SNAKE_CASE_		:     Tuple					=					list(set(A__				)				)






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     str					=					[]
			for minterm in minterms:
						SCREAMING_SNAKE_CASE_		:     int					=					''
						for _ in range(A__				):
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					str(minterm % 2				) + string
									minterm //= 2
						temp.append(A__				)
			return temp






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					list(A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					list(A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					0
			for i in range(len(A__				)				):
						if lista[i] != lista[i]:
									count_n += 1
			return count_n == count






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					[]
			SCREAMING_SNAKE_CASE_		:     str					=					[0] * len(A__				)
			for i in range(len(chart[0]				)				):
						SCREAMING_SNAKE_CASE_		:     Tuple					=					0
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					-1
						for j in range(len(A__				)				):
									if chart[j][i] == 1:
												count += 1
												SCREAMING_SNAKE_CASE_		:     List[Any]					=					j
						if count == 1:
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					1
			for i in range(len(A__				)				):
						if select[i] == 1:
									for j in range(len(chart[0]				)				):
												if chart[i][j] == 1:
															for k in range(len(A__				)				):
																		SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					0
									temp.append(prime_implicants[i]				)
			while True:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					0
						SCREAMING_SNAKE_CASE_		:     Any					=					-1
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					0
						for i in range(len(A__				)				):
									SCREAMING_SNAKE_CASE_		:     Any					=					chart[i].count(1				)
									if count_n > max_n:
												SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					count_n
												SCREAMING_SNAKE_CASE_		:     Any					=					i

						if max_n == 0:
									return temp

						temp.append(prime_implicants[rem]				)

						for i in range(len(chart[0]				)				):
									if chart[rem][i] == 1:
												for j in range(len(A__				)				):
															SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					0






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     int					=					[[0 for x in range(len(A__				)				)] for x in range(len(A__				)				)]
			for i in range(len(A__				)				):
						SCREAMING_SNAKE_CASE_		:     Tuple					=					prime_implicants[i].count('_'				)
						for j in range(len(A__				)				):
									if is_for_table(prime_implicants[i],      binary[j],      A__				):
												SCREAMING_SNAKE_CASE_		:     int					=					1

			return chart






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     int					=					int(input('Enter the no. of variables\n'				)				)
			SCREAMING_SNAKE_CASE_		:     int					=					[
			    float(A__				)
			    for x in input(
			        'Enter the decimal representation of Minterms \'Spaces Separated\'\n'				).split()
			]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					decimal_to_binary(A__,      A__				)

			SCREAMING_SNAKE_CASE_		:     str					=					check(A__				)
			print('Prime Implicants are:'				)
			print(A__				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					prime_implicant_chart(A__,      A__				)

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					selection(A__,      A__				)
			print('Essential Prime Implicants are:'				)
			print(A__				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()













"""simple docstring"""







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


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


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