






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

lowerCamelCase      :       List[Any]    =						'''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''





def    __lowerCAmelCase   (    __snake_case = "mumbai"							):
     __lowerCAmelCase             =       BeautifulSoup(requests.get(url + location							).content       ,			"html.parser"							)
     # This attribute finds out all the specifics listed in a job
     for job in soup.find_all("div"       ,			attrs={"data-tn-component": "organicJob"}							):
          __lowerCAmelCase             =       job.find("a"       ,			attrs={"data-tn-element": "jobTitle"}							).text.strip()
          __lowerCAmelCase             =       job.find("span"       ,			{"class": "company"}							).text.strip()
          yield job_title, company_name


if __name__ == "__main__":
 for i, job in enumerate(fetch_jobs('''Bangalore'''), 1):
  print(F'''Job {i:>2} is {job[0]} at {job[1]}''')














import numpy as np





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case = 1E-12       ,			__snake_case = 100       ,			):
     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							)
     __lowerCAmelCase             =       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.

     __lowerCAmelCase             =       False
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       1E12

     while not convergence:
          # Multiple matrix by the vector.
          __lowerCAmelCase             =       np.dot(__snake_case       ,			__snake_case							)
          # Normalize the resulting output vector.
          __lowerCAmelCase             =       w / np.linalg.norm(__snake_case							)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __lowerCAmelCase             =       vector.conj().T if is_complex else vector.T
          __lowerCAmelCase             =       np.dot(__snake_case       ,			np.dot(__snake_case       ,			__snake_case							)							)

          # Check convergence.
          __lowerCAmelCase             =       np.abs(lambda_ - lambda_previous							) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __lowerCAmelCase             =       True

          __lowerCAmelCase             =       lambda_

     if is_complex:
          __lowerCAmelCase             =       np.real(lambda_							)

     return lambda_, vector





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
     __lowerCAmelCase             =       np.array([41, 4, 20]							)
     __lowerCAmelCase             =       real_input_matrix.astype(np.complexaaa							)
     __lowerCAmelCase             =       np.triu(1J * complex_input_matrix       ,			1							)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __lowerCAmelCase             =       np.array([41, 4, 20]							).astype(np.complexaaa							)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __lowerCAmelCase             =       real_input_matrix
               __lowerCAmelCase             =       real_vector
          elif problem_type == "complex":
               __lowerCAmelCase             =       complex_input_matrix
               __lowerCAmelCase             =       complex_vector

          # Our implementation.
          __lowerCAmelCase      ,			__lowerCAmelCase             =       power_iteration(__snake_case       ,			__snake_case							)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __lowerCAmelCase      ,			__lowerCAmelCase             =       np.linalg.eigh(__snake_case							)
          # Last eigenvalue is the maximum one.
          __lowerCAmelCase             =       eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __lowerCAmelCase             =       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()






