






import unittest

import numpy as np




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase = None					,			):

					'''simple docstring'''







					_A								= np.shape(__SCREAMING_SNAKE_CASE )
					_A								= np.shape(__SCREAMING_SNAKE_CASE )
					_A								= np.shape(__SCREAMING_SNAKE_CASE )

					if shape_a[0] != shape_b[0]:
										_A								= (
										    "Expected the same number of rows for A and B. "
										    f"""Instead found A of size {shape_a} and B of size {shape_b}"""
										)
										raise ValueError(__SCREAMING_SNAKE_CASE )

					if shape_b[1] != shape_c[1]:
										_A								= (
										    "Expected the same number of columns for B and C. "
										    f"""Instead found B of size {shape_b} and C of size {shape_c}"""
										)
										raise ValueError(__SCREAMING_SNAKE_CASE )

					_A								= pseudo_inv
					if a_inv is None:
										try:
															_A								= np.linalg.inv(__SCREAMING_SNAKE_CASE )
										except np.linalg.LinAlgError:
															raise ValueError(
															    '''Input matrix A is not invertible. Cannot compute Schur complement.''' )

					return mat_c - mat_b.T @ a_inv @ mat_b





class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   Tuple   )    ->				List[Any]:
												_A								= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
												_A								= np.array([[0, 3], [3, 0], [2, 3]]   )
												_A								= np.array([[2, 1], [6, 3]]   )

												_A								= schur_complement(lowercase_		,     lowercase_		,     lowercase_   )

												_A								= np.block([[a, b], [b.T, c]]   )

												_A								= np.linalg.det(lowercase_   )
												_A								= np.linalg.det(lowercase_   )
												_A								= np.linalg.det(lowercase_   )

												self.assertAlmostEqual(lowercase_		,     det_a * det_s   )

							def       __A (  self:   List[str]   )    ->				Optional[int]:
												_A								= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
												_A								= np.array([[0, 3], [3, 0], [2, 3]]   )
												_A								= np.array([[2, 1], [6, 3]]   )

												with self.assertRaises(lowercase_   ):
																	schur_complement(lowercase_		,     lowercase_		,     lowercase_   )



							def       __A (  self:   Union[str, Any]   )    ->				Dict:
												_A								= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
												_A								= np.array([[0, 3], [3, 0], [2, 3]]   )
												_A								= np.array([[2, 1, 3], [6, 3, 5]]   )

												with self.assertRaises(lowercase_   ):
																	schur_complement(lowercase_		,     lowercase_		,     lowercase_   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()











from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def      __A					(     _lowercase = "" ):

					'''simple docstring'''







					_A								= url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					_A								= BeautifulSoup(requests.get(_lowercase ).text					,			'''html.parser''' )
					_A								= soup.find_all('''td'''					,			attrs='''titleColumn''' )
					_A								= soup.find_all('''td'''					,			class_='''ratingColumn imdbRating''' )
					return {
					    title.a.text: float(rating.strong.text )
					    for title, rating in zip(_lowercase					,			_lowercase )
					}




def      __A					(     _lowercase = "IMDb_Top_250_Movies.csv" ):

					'''simple docstring'''







					_A								= get_imdb_top_aaa_movies()
					with open(_lowercase					,			'''w'''					,			newline='''''' ) as out_file:
										_A								= csv.writer(_lowercase )
										writer.writerow(['''Movie title''', '''IMDb rating'''] )
										for title, rating in movies.items():
															writer.writerow([title, rating] )


if __name__ == "__main__":
				write_movies()



