





"""simple docstring"""



from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







def 						__lowercase	(  snake_case_    :    str = "laptop"		)  ->DataFrame:



    '''simple docstring'''







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












"""simple docstring"""



import numpy as np


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : np.ndarray			,					snake_case       : np.ndarray			,					snake_case       : float = 1E-1_2			,					snake_case       : int = 100			,					)->		tuple[float, np.ndarray]:






     '''simple docstring'''



     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[1]
     # Ensure proper dimensionality.
     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(snake_case				) == np.iscomplexobj(snake_case				)
     UpperCAmelCase__						:    str							=							np.iscomplexobj(snake_case				)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(snake_case			,					input_matrix.conj().T				)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     UpperCAmelCase__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    List[Any]							=							0
     UpperCAmelCase__						:    Any							=							0
     UpperCAmelCase__						:    List[str]							=							1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          UpperCAmelCase__						:    Optional[Any]							=							np.dot(snake_case			,					snake_case				)
          # Normalize the resulting output vector.
          UpperCAmelCase__						:    Tuple							=							w / np.linalg.norm(snake_case				)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          UpperCAmelCase__						:    Any							=							vector.conj().T if is_complex else vector.T
          UpperCAmelCase__						:    List[str]							=							np.dot(snake_case			,					np.dot(snake_case			,					snake_case				)				)

          # Check convergence.
          UpperCAmelCase__						:    Optional[Any]							=							np.abs(lambda_ - lambda_previous				) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               UpperCAmelCase__						:    Tuple							=							True

          UpperCAmelCase__						:    Tuple							=							lambda_

     if is_complex:
          UpperCAmelCase__						:    str							=							np.real(lambda_				)

     return lambda_, vector


def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
     UpperCAmelCase__						:    Dict							=							np.array([41, 4, 20]				)
     UpperCAmelCase__						:    Union[str, Any]							=							real_input_matrix.astype(np.complexaaa				)
     UpperCAmelCase__						:    Optional[int]							=							np.triu(1J * complex_input_matrix			,					1				)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     UpperCAmelCase__						:    int							=							np.array([41, 4, 20]				).astype(np.complexaaa				)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               UpperCAmelCase__						:    List[str]							=							real_input_matrix
               UpperCAmelCase__						:    Any							=							real_vector
          elif problem_type == "complex":
               UpperCAmelCase__						:    str							=							complex_input_matrix
               UpperCAmelCase__						:    List[Any]							=							complex_vector

          # Our implementation.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							power_iteration(snake_case			,					snake_case				)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							np.linalg.eigh(snake_case				)
          # Last eigenvalue is the maximum one.
          UpperCAmelCase__						:    str							=							eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCAmelCase__						:    Optional[int]							=							eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max				) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(snake_case				) - np.abs(snake_case				)				) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()


