



import socket

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		socket.socket(socket.AF_INET , socket.SOCK_STREAM			)
      lowerCamelCase             =		socket.gethostname()
      lowerCamelCase             =		12312

      sock.connect((host, port)			)
      sock.send(B"Hello server!"			)

      with open("Received_file" , "wb"			) as out_file:
            print("File opened"			)
            print("Receiving data..."			)
            while True:
                  lowerCamelCase             =		sock.recv(1024			)
                  if not data:
                        break
                  out_file.write(UpperCAmelCase__			)

      print("Successfully received the file"			)
      sock.close()
      print("Connection closed"			)


if __name__ == "__main__":
     main()




import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      with open(UpperCAmelCase__			) as metadata_file:
            lowerCamelCase             =		json.load(UpperCAmelCase__			)
      lowerCamelCase             =		LukeConfig(use_entity_aware_attention=UpperCAmelCase__ , **metadata["model_config"]			)

      # Load in the weights from the checkpoint_path
      lowerCamelCase             =		torch.load(UpperCAmelCase__ , map_location="cpu"			)

      # Load the entity vocab file
      lowerCamelCase             =		load_entity_vocab(UpperCAmelCase__			)

      lowerCamelCase             =		RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase             =		AddedToken("<ent>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      lowerCamelCase             =		AddedToken("<ent2>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}			)
      config.vocab_size += 2

      print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""			)
      tokenizer.save_pretrained(UpperCAmelCase__			)
      with open(os.path.join(UpperCAmelCase__ , LukeTokenizer.vocab_files_names["entity_vocab_file"]			) , "w"			) as f:
            json.dump(UpperCAmelCase__ , UpperCAmelCase__			)

      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__			)

      # Initialize the embeddings of the special tokens
      lowerCamelCase             =		state_dict["embeddings.word_embeddings.weight"]
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["@"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["#"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		torch.cat([word_emb, ent_emb, enta_emb]			)

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers			):
            for matrix_name in ["query.weight", "query.bias"]:
                  lowerCamelCase             =		F"""encoder.layer.{layer_index}.attention.self."""
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      lowerCamelCase             =		state_dict["entity_embeddings.entity_embeddings.weight"]
      lowerCamelCase             =		entity_emb[entity_vocab["[MASK]"]]

      lowerCamelCase             =		LukeModel(config=UpperCAmelCase__			).eval()

      lowerCamelCase      ,				lowerCamelCase             =		model.load_state_dict(UpperCAmelCase__ , strict=UpperCAmelCase__			)
      if not (len(UpperCAmelCase__			) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {", ".join(UpperCAmelCase__			)}. Expected only missing embeddings.position_ids"""			)
      if not (all(key.startswith("entity_predictions"			) or key.startswith("lm_head"			) for key in unexpected_keys			)):
            raise ValueError(
                "Unexpected keys"
                F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"			) or key.startswith("lm_head"			))]			)}"""			)

      # Check outputs
      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__ , task="entity_classification"			)

      lowerCamelCase             =		(
          "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
          " new world number one avoid a humiliating second- round exit at Wimbledon ."
      )
      lowerCamelCase             =		(39, 42)
      lowerCamelCase             =		tokenizer(UpperCAmelCase__ , entity_spans=[span] , add_prefix_space=UpperCAmelCase__ , return_tensors="pt"			)

      lowerCamelCase             =		model(**UpperCAmelCase__			)

      # Verify word hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 42, 1024)			)
            lowerCamelCase             =		torch.tensor(
                [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 42, 768)			)
            lowerCamelCase             =		torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]			)

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""			)
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 1, 1024)			)
            lowerCamelCase             =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 1, 768)			)
            lowerCamelCase             =		torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]			)

      if not (outputs.entity_last_hidden_state.shape != expected_shape):
            raise ValueError(
                F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
                F""" {expected_shape}"""			)
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("Saving PyTorch model to {}".format(UpperCAmelCase__			)			)
      model.save_pretrained(UpperCAmelCase__			)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      with open(UpperCAmelCase__ , "r" , encoding="utf-8"			) as f:
            for index, line in enumerate(UpperCAmelCase__			):
                  lowerCamelCase      ,				lowerCamelCase             =		line.rstrip().split("\t"			)
                  lowerCamelCase             =		index

      return entity_vocab


if __name__ == "__main__":
     a_    :  Tuple						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
     parser.add_argument(
         '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
     )
     parser.add_argument(
         '--entity_vocab_path',
         default=None,
         type=str,
         help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
     )
     parser.add_argument(
         '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
     )
     a_    :  Dict						  =		parser.parse_args()
     convert_luke_checkpoint(
         args.checkpoint_path,
         args.metadata_path,
         args.entity_vocab_path,
         args.pytorch_dump_folder_path,
         args.model_size,
     )