'''simple docstring'''




def 	_UpperCAmelCase       (				_UpperCamelCase       :      str = "mytext.txt",  _UpperCamelCase       :      bool = False  )      -> None:
    import socket

    A_      =  1_23_12  # Reserve a port for your service.
    A_      =  socket.socket()  # Create a socket object
    A_      =  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_      =  sock.accept()  # Establish connection with client.
        print(F'''Got connection from {addr}'''  )
        A_      =  conn.recv(10_24  )
        print(F'''Server received: {data = }'''  )

        with open(_UpperCamelCase,  '''rb'''  ) as in_file:
            A_      =  in_file.read(10_24  )
            while data:
                conn.send(_UpperCamelCase  )
                print(F'''Sent {data!r}'''  )
                A_      =  in_file.read(10_24  )

        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 typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__snake_case  : Union[str, Any]		       =    logging.get_logger(__name__)

__snake_case  : Union[str, Any]		       =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case  : List[str]		       =    {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__snake_case  : Optional[int]		       =    {
    'openai-gpt': 512,
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   int  					=							VOCAB_FILES_NAMES
       __lowercase   :   int  					=							PRETRAINED_VOCAB_FILES_MAP
       __lowercase   :   str  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase   :   Optional[Any]  					=							['input_ids', 'attention_mask']
       __lowercase   :   List[Any]  					=							OpenAIGPTTokenizer

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE="<unk>"       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @property
       def    __A			( self					)						->	Optional[Any]:
           return True

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	Tuple[str]:
           A_      =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE       ,	name=_SCREAMING_SNAKE_CASE					)
           return tuple(_SCREAMING_SNAKE_CASE					)






