






"""simple docstring"""




def     lowerCAmelCase__  (  UpperCamelCase__ = "mytext.txt"  ,     UpperCamelCase__ = False       ):



  '''simple docstring'''
  import socket

  _a						: Optional[int]    =			1_2_3_1_2  # Reserve a port for your service.
  _a						: List[str]    =			socket.socket()  # Create a socket object
  _a						: Dict    =			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:
    _a   ,						_a						: Optional[Any]    =			sock.accept()  # Establish connection with client.
    print(F"""Got connection from {addr}"""       )
    _a						: int    =			conn.recv(1_0_2_4       )
    print(F"""Server received: {data = }"""       )

    with open(UpperCamelCase__  ,     """rb"""       ) as in_file:
      _a						: List[Any]    =			in_file.read(1_0_2_4       )
      while data:
        conn.send(UpperCamelCase__       )
        print(F"""Sent {data!r}"""       )
        _a						: Union[str, Any]    =			in_file.read(1_0_2_4       )

    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"""




# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

