code: import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->List[str]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     TaConfig.from_pretrained(lowerCamelCase__					)
    a__     		=     FlaxTaForConditionalGeneration(config=lowerCamelCase__					)
    a__     		=     checkpoints.load_tax_checkpoint(lowerCamelCase__					)
  
    a__     		=     "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]
  
    # Encoder
    for layer_index in range(config.num_layers					):
      a__     		=     f"layers_{str(lowerCamelCase__					)}"
  
      # Self-Attention
      a__     		=     tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
      a__     		=     tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
      a__     		=     tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
      a__     		=     tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]
  
      # Layer Normalization
      a__     		=     tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]
  
      if split_mlp_wi:
        a__     		=     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
        a__     		=     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
        a__     		=     tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]
  
      a__     		=     tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]
  
      # Layer Normalization
      a__     		=     tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
  
      # Assigning
      a__     		=     tax_attention_key
      a__     		=     tax_attention_out
      a__     		=     tax_attention_query
      a__     		=     tax_attention_value
  
      a__     		=     tax_attention_layer_norm
  
      if split_mlp_wi:
        a__     		=     tax_mlp_wi_a
        a__     		=     tax_mlp_wi_a
      else:
        a__     		=     tax_mlp_wi
  
      a__     		=     tax_mlp_wo
      a__     		=     tax_mlp_layer_norm
  
    # Only for layer 0:
    a__     		=     tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
    a__     		=     tax_encoder_rel_embedding
  
    # Assigning
    a__     		=     tax_model["target"]["encoder"]["encoder_norm"]["scale"]
    a__     		=     tax_encoder_norm
  
    # Decoder
    for layer_index in range(config.num_decoder_layers					):
      a__     		=     f"layers_{str(lowerCamelCase__					)}"
  
      # Self-Attention
      a__     		=     tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
      a__     		=     tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
      a__     		=     tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
      a__     		=     tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]
  
      # Layer Normalization
      a__     		=     tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]
  
      # Encoder-Decoder-Attention
      a__     		=     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
          "kernel"
      ]
      a__     		=     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
          "kernel"
      ]
      a__     		=     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
          "kernel"
      ]
      a__     		=     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
          "kernel"
      ]
  
      # Layer Normalization
      a__     		=     tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]
  
      # MLP
      if split_mlp_wi:
        a__     		=     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
        a__     		=     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
        a__     		=     tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]
  
      a__     		=     tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]
  
      # Layer Normalization
      a__     		=     tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
  
      # Assigning
      a__     		=     tax_attention_key
      a__     		=     tax_attention_out
      a__     		=     tax_attention_query
      a__     		=     tax_attention_value
  
      a__     		=     tax_pre_attention_layer_norm
  
      a__     		=     tax_enc_dec_attention_key
      a__     		=     tax_enc_dec_attention_out
      a__     		=     tax_enc_dec_attention_query
      a__     		=     tax_enc_dec_attention_value
  
      a__     		=     tax_cross_layer_norm
  
      if split_mlp_wi:
        a__     		=     tax_mlp_wi_a
        a__     		=     tax_mlp_wi_a
      else:
        a__     		=     tax_mlp_wi
  
      a__     		=     tax_mlp_wo
  
      a__     		=     txa_mlp_layer_norm
  
    # Decoder Normalization
    a__     		=     tax_model["target"]["decoder"]["decoder_norm"]["scale"]
    a__     		=     txa_decoder_norm
  
    # Only for layer 0:
    a__     		=     tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
    a__     		=     tax_decoder_rel_embedding
  
    # Token Embeddings
    a__     		=     tax_model["target"]["token_embedder"]["embedding"]
    a__     		=     txa_token_embeddings
  
    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
      a__     		=     tax_model["target"]["decoder"]["logits_dense"]["kernel"]
  
    flax_model.save_pretrained(lowerCamelCase__					)
    print("T5X Model was sucessfully converted!"					)
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
       )
       parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
       parser.add_argument(
           """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
       )
       _SCREAMING_SNAKE_CASE   						=    parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  
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()
  
  