






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   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






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

  # Load in the weights from the checkpoint_path
  SCREAMING_SNAKE_CASE       :      int							=    torch.load(lowercase						,     map_location="cpu"				)

  # Load the entity vocab file
  SCREAMING_SNAKE_CASE       :      Any							=    load_entity_vocab(lowercase				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]				)

  # Add special tokens to the token vocabulary for downstream tasks
  SCREAMING_SNAKE_CASE       :      List[str]							=    AddedToken("<ent>"						,     lstrip=lowercase						,     rstrip=lowercase				)
  SCREAMING_SNAKE_CASE       :      Dict							=    AddedToken("<ent2>"						,     lstrip=lowercase						,     rstrip=lowercase				)
  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(lowercase				)
  with open(os.path.join(lowercase						,     LukeTokenizer.vocab_files_names["entity_vocab_file"]				)						,     "w"				) as f:
    json.dump(lowercase						,     lowercase				)

  SCREAMING_SNAKE_CASE       :      Dict							=    LukeTokenizer.from_pretrained(lowercase				)

  # Initialize the embeddings of the special tokens
  SCREAMING_SNAKE_CASE       :      List[Any]							=    state_dict["embeddings.word_embeddings.weight"]
  SCREAMING_SNAKE_CASE       :      Dict							=    word_emb[tokenizer.convert_tokens_to_ids(["@"]				)[0]].unsqueeze(0				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    word_emb[tokenizer.convert_tokens_to_ids(["#"]				)[0]].unsqueeze(0				)
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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"]:
      SCREAMING_SNAKE_CASE       :      List[Any]							=    F'''encoder.layer.{layer_index}.attention.self.'''
      SCREAMING_SNAKE_CASE       :      Dict							=    state_dict[prefix + matrix_name]
      SCREAMING_SNAKE_CASE       :      str							=    state_dict[prefix + matrix_name]
      SCREAMING_SNAKE_CASE       :      List[str]							=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
  SCREAMING_SNAKE_CASE       :      List[Any]							=    state_dict["entity_embeddings.entity_embeddings.weight"]
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    entity_emb[entity_vocab["[MASK]"]]

  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    LukeModel(config=lowercase				).eval()

  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[Any]							=    model.load_state_dict(lowercase						,     strict=lowercase				)
  if not (len(lowercase				) == 1 and missing_keys[0] == "embeddings.position_ids"):
    raise ValueError(F'''Missing keys {', '.join(lowercase				)}. 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
  SCREAMING_SNAKE_CASE       :      Tuple							=    LukeTokenizer.from_pretrained(lowercase						,     task="entity_classification"				)

  SCREAMING_SNAKE_CASE       :      int							=    (
      "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 ."
  )
  SCREAMING_SNAKE_CASE       :      List[str]							=    (39, 42)
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    tokenizer(lowercase						,     entity_spans=[span]						,     add_prefix_space=lowercase						,     return_tensors="pt"				)

  SCREAMING_SNAKE_CASE       :      str							=    model(**lowercase				)

  # Verify word hidden states
  if model_size == "large":
    SCREAMING_SNAKE_CASE       :      Any							=    torch.Size((1, 42, 1024)				)
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.tensor(
        [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]				)
  else:  # base
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.Size((1, 42, 768)				)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]				)

  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]						,     lowercase						,     atol=1E-4				):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    SCREAMING_SNAKE_CASE       :      List[str]							=    torch.Size((1, 1, 1024)				)
    SCREAMING_SNAKE_CASE       :      str							=    torch.tensor([[0.0466, -0.0106, -0.0179]]				)
  else:  # base
    SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.Size((1, 1, 768)				)
    SCREAMING_SNAKE_CASE       :      int							=    torch.tensor([[0.1457, 0.1044, 0.0174]]				)

  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]						,     lowercase						,     atol=1E-4				):
    raise ValueError

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


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    {}
  with open(lowercase						,     "r"						,     encoding="utf-8"				) as f:
    for index, line in enumerate(lowercase				):
      SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Tuple							=    line.rstrip().split("\t"				)
      SCREAMING_SNAKE_CASE       :      int							=    index

  return entity_vocab


if __name__ == "__main__":
   snake_case    				=		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."""
   )
   snake_case    				=		parser.parse_args()
   convert_luke_checkpoint(
       args.checkpoint_path,
       args.metadata_path,
       args.entity_vocab_path,
       args.pytorch_dump_folder_path,
       args.model_size,
   )














from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    HfArgumentParser(lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    parser.parse_args_into_dataclasses()[0]
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    TensorFlowBenchmark(args=lowercase				)
  try:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    parser.parse_args_into_dataclasses()[0]
  except ValueError as e:
    SCREAMING_SNAKE_CASE       :      int							=    "Arg --no_{0} is no longer used, please use --no-{0} instead."
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    " ".join(str(lowercase				).split(" "				)[:-1]				)
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ""
    SCREAMING_SNAKE_CASE       :      Any							=    eval(str(lowercase				).split(" "				)[-1]				)
    SCREAMING_SNAKE_CASE       :      List[str]							=    []
    for arg in depreciated_args:
      # arg[2:] removes '--'
      if arg[2:] in TensorFlowBenchmark.deprecated_args:
        # arg[5:] removes '--no_'
        full_error_msg += arg_error_msg.format(arg[5:]				)
      else:
        wrong_args.append(lowercase				)
    if len(lowercase				) > 0:
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    full_error_msg + begin_error_msg + str(lowercase				)
    raise ValueError(lowercase				)
  benchmark.run()


if __name__ == "__main__":
   main()






