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)


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()

