






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def 		_UpperCamelCase				(      UpperCamelCase_    :   NDArray[floataa]      ,    UpperCamelCase_    :   NDArray[floataa]      ,    UpperCamelCase_    :   list[int]      ,    UpperCamelCase_    :   int      ,    )					->				list[float]:

						"""simple docstring"""



						lowerCAmelCase__  ,    lowerCAmelCase__        =  coefficient_matrix.shape
						lowerCAmelCase__  ,    lowerCAmelCase__        =  constant_matrix.shape

						if rowsa != colsa:
												lowerCAmelCase__        =  F"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
												raise ValueError(UpperCamelCase_						)

						if colsa != 1:
												lowerCAmelCase__        =  F"Constant matrix must be nx1 but received {rowsa}x{colsa}"
												raise ValueError(UpperCamelCase_						)

						if rowsa != rowsa:
												lowerCAmelCase__        =  (
												    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
												    F"received {rowsa}x{colsa} and {rowsa}x{colsa}"
												)
												raise ValueError(UpperCamelCase_						)

						if len(UpperCamelCase_						) != rowsa:
												lowerCAmelCase__        =  (
												    'Number of initial values must be equal to number of rows in coefficient '
												    F"matrix but received {len(UpperCamelCase_						)} and {rowsa}"
												)
												raise ValueError(UpperCamelCase_						)

						if iterations <= 0:
												raise ValueError('Iterations must be at least 1'						)

						lowerCAmelCase__        =  np.concatenate(
						    (coefficient_matrix, constant_matrix)      ,    axis=1						)

						lowerCAmelCase__  ,    lowerCAmelCase__        =  table.shape

						strictly_diagonally_dominant(UpperCamelCase_						)

						# Iterates the whole matrix for given number of times
						for _ in range(UpperCamelCase_						):
												lowerCAmelCase__        =  []
												for row in range(UpperCamelCase_						):
																		lowerCAmelCase__        =  0
																		for col in range(UpperCamelCase_						):
																								if col == row:
																														lowerCAmelCase__        =  table[row][col]
																								elif col == cols - 1:
																														lowerCAmelCase__        =  table[row][col]
																								else:
																														temp += (-1) * table[row][col] * init_val[col]
																		lowerCAmelCase__        =  (temp + val) / denom
																		new_val.append(UpperCamelCase_						)
												lowerCAmelCase__        =  new_val

						return [float(UpperCamelCase_						) for i in new_val]







def 		_UpperCamelCase				(      UpperCamelCase_    :   NDArray[floataa]						)					->				bool:

						"""simple docstring"""



						lowerCAmelCase__  ,    lowerCAmelCase__        =  table.shape

						lowerCAmelCase__        =  True

						for i in range(0      ,    UpperCamelCase_						):
												lowerCAmelCase__        =  0
												for j in range(0      ,    cols - 1						):
																		if i == j:
																								continue
																		else:
																								total += table[i][j]

												if table[i][i] <= total:
																		raise ValueError('Coefficient matrix is not strictly diagonally dominant'						)

						return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
					import doctest

					doctest.testmod()













from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

__snake_case      :      Any             =       """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""







def 		_UpperCamelCase				(      UpperCamelCase_    :   str = "mumbai"						)					->				Generator[tuple[str, str], None, None]:

						"""simple docstring"""



						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]}')





