def 							lowerCAmelCase__    (       a__ = "mytext.txt"  ,			a__ = False      )	->None:




  '''simple docstring'''

  import socket

  _UpperCamelCase				=  12_312  # Reserve a port for your service.
  _UpperCamelCase				=  socket.socket()  # Create a socket object
  _UpperCamelCase				=  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:
    _UpperCamelCase	,		_UpperCamelCase				=  sock.accept()  # Establish connection with client.
    print(f'Got connection from {addr}'      )
    _UpperCamelCase				=  conn.recv(1_024      )
    print(f'Server received: {data = }'      )

    with open(a__  ,			"rb"      ) as in_file:
      _UpperCamelCase				=  in_file.read(1_024      )
      while data:
        conn.send(a__      )
        print(f'Sent {data!r}'      )
        _UpperCamelCase				=  in_file.read(1_024      )

    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"""


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_     :      List[str]								=load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
A_							,	A_							,	A_							,	A_     :      List[Any]								=train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

A_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)






