code: from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  _SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a    ):
      UpperCAmelCase_			:       Dict						 =							'mt5'
      UpperCAmelCase_			:       Dict						 =							['past_key_values']
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   int=250112      ,      lowerCAmelCase  :   List[str]=512      ,      lowerCAmelCase  :   Any=64      ,      lowerCAmelCase  :   int=1024      ,      lowerCAmelCase  :   List[Any]=8      ,      lowerCAmelCase  :   Union[str, Any]=None      ,      lowerCAmelCase  :   Optional[int]=6      ,      lowerCAmelCase  :   List[Any]=32      ,      lowerCAmelCase  :   Optional[Any]=128      ,      lowerCAmelCase  :   Optional[int]=0.1      ,      lowerCAmelCase  :   Optional[Any]=1e-6      ,      lowerCAmelCase  :   int=1.0      ,      lowerCAmelCase  :   Any="gated-gelu"      ,      lowerCAmelCase  :   Optional[int]=True      ,      lowerCAmelCase  :   Union[str, Any]=True      ,      lowerCAmelCase  :   Tuple="T5Tokenizer"      ,      lowerCAmelCase  :   Dict=False      ,      lowerCAmelCase  :   List[str]=0      ,      lowerCAmelCase  :   Optional[int]=1      ,      lowerCAmelCase  :   int=0      ,      **lowerCAmelCase  :   int      ,      )	-> List[str]:
        super().__init__(
            is_encoder_decoder=lowerCAmelCase      ,      tokenizer_class=lowerCAmelCase      ,      tie_word_embeddings=lowerCAmelCase      ,      pad_token_id=lowerCAmelCase      ,      eos_token_id=lowerCAmelCase      ,      decoder_start_token_id=lowerCAmelCase      ,      **lowerCAmelCase      ,      )
        a__     		= vocab_size
        a__     		= d_model
        a__     		= d_kv
        a__     		= d_ff
        a__     		= num_layers
        a__     		= (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        a__     		= num_heads
        a__     		= relative_attention_num_buckets
        a__     		= relative_attention_max_distance
        a__     		= dropout_rate
        a__     		= layer_norm_epsilon
        a__     		= initializer_factor
        a__     		= feed_forward_proj
        a__     		= use_cache
  
        a__     		= self.feed_forward_proj.split("-"   )
        a__     		= act_info[-1]
        a__     		= act_info[0] == "gated"
  
        if len(lowerCAmelCase   ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase   ) > 2:
          raise ValueError(
              f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
              "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
              "'gated-gelu' or 'relu'"   )
  
        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
          a__     		= "gelu_new"
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   Tuple   )	-> Optional[Any]:
        return self.d_model
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> List[str]:
        return self.num_heads
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   Dict   )	-> List[str]:
        return self.num_layers
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a    ):
  
  
  
  
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Mapping[str, Mapping[int, str]]:
        a__     		= {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
          a__     		= "past_encoder_sequence + sequence"
          a__     		= {0: "batch"}
          a__     		= {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
          a__     		= {0: "batch", 1: "decoder_sequence"}
          a__     		= {0: "batch", 1: "decoder_sequence"}
  
        if self.use_past:
          self.fill_with_past_key_values_(lowerCAmelCase      ,      direction="inputs"   )
  
        return common_inputs
  
  
  
  
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> int:
        return 13
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   Tuple   )	-> float:
        return 5e-4
  
  
style_context: def 				__lowercase	(						lowerCamelCase__ = "mytext.txt" ,				lowerCamelCase__ = False					)				->None:
  
    '''simple docstring'''
  
  
  
  
    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(lowerCamelCase__ ,				"rb"					) as in_file:
        a__     		=     in_file.read(10_24					)
        while data:
          conn.send(lowerCamelCase__					)
          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()
  
  