
'''simple docstring'''
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ = "mytext.txt"   ,					SCREAMING_SNAKE_CASE__ = False )     ->       Optional[int]:


		'''simple docstring'''


		import socket

		snake_case     :					Tuple     =	1_2312  # Reserve a port for your service.
		snake_case     :					str     =	socket.socket()  # Create a socket object
		snake_case     :					Optional[Any]     =	socket.gethostname()  # Get local machine name
		sock.bind((host, port) )  # Bind to the port
		sock.listen(5 )  # Now wait for client connection.

		print('''Server listening....''' )

		while True:
				snake_case ,snake_case     :					Tuple     =	sock.accept()  # Establish connection with client.
				print(F'Got connection from {addr}' )
				snake_case     :					List[str]     =	conn.recv(1024 )
				print(F'Server received: {data = }' )

				with open(lowercase__   ,					'''rb''' ) as in_file:
						snake_case     :					List[str]     =	in_file.read(1024 )
						while data:
								conn.send(lowercase__ )
								print(F'Sent {data!r}' )
								snake_case     :					List[str]     =	in_file.read(1024 )

				print('''Done sending''' )
				conn.close()
				if testing:  # Allow the test to complete
						break

		sock.shutdown(1 )
		sock.close()


if __name__ == "__main__":
						send_file()




'''simple docstring'''
import numpy as np




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:


		'''simple docstring'''


		snake_case ,snake_case     :					str     =	a.shape
		snake_case     :					Union[str, Any]     =	min(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
		snake_case     :					List[Any]     =	np.eye(SCREAMING_SNAKE_CASE__ )
		snake_case     :					Union[str, Any]     =	a.copy()

		for k in range(t - 1 ):
				# select a column of modified matrix A':
				snake_case     :					Union[str, Any]     =	r[k:, [k]]
				# construct first basis vector
				snake_case     :					str     =	np.zeros_like(SCREAMING_SNAKE_CASE__ )
				snake_case     :					List[str]     =	1.0
				# determine scaling factor
				snake_case     :					int     =	np.linalg.norm(SCREAMING_SNAKE_CASE__ )
				# construct vector v for Householder reflection
				snake_case     :					Optional[int]     =	x + np.sign(x[0] ) * alpha * ea
				v /= np.linalg.norm(SCREAMING_SNAKE_CASE__ )

				# construct the Householder matrix
				snake_case     :					Tuple     =	np.eye(m - k ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				snake_case     :					int     =	np.block([[np.eye(SCREAMING_SNAKE_CASE__ ), np.zeros((k, m - k) )], [np.zeros((m - k, k) ), q_k]] )

				snake_case     :					int     =	q @ q_k.T
				snake_case     :					Dict     =	q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()


