

"""simple docstring"""






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__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    with open(_lowerCamelCase   ) as metadata_file:
        _lowerCAmelCase       :   Union[str, Any]  =    json.load(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    LukeConfig(use_entity_aware_attention=_lowerCamelCase ,   **metadata['model_config']   )

    # Load in the weights from the checkpoint_path
    _lowerCAmelCase       :   List[str]  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )

    # Load the entity vocab file
    _lowerCAmelCase       :   Dict  =    load_entity_vocab(_lowerCamelCase   )

    _lowerCAmelCase       :   Tuple  =    RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']   )

    # Add special tokens to the token vocabulary for downstream tasks
    _lowerCAmelCase       :   Optional[Any]  =    AddedToken('<ent>' ,   lstrip=_lowerCamelCase ,   rstrip=_lowerCamelCase   )
    _lowerCAmelCase       :   Optional[int]  =    AddedToken('<ent2>' ,   lstrip=_lowerCamelCase ,   rstrip=_lowerCamelCase   )
    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(_lowerCamelCase   )
    with open(os.path.join(_lowerCamelCase ,   LukeTokenizer.vocab_files_names['entity_vocab_file']   ) ,   'w'   ) as f:
        json.dump(_lowerCamelCase ,   _lowerCamelCase   )

    _lowerCAmelCase       :   Optional[Any]  =    LukeTokenizer.from_pretrained(_lowerCamelCase   )

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

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

    _lowerCAmelCase       :   Union[str, Any]  =    LukeModel(config=_lowerCamelCase   ).eval()

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

    _lowerCAmelCase       :   Tuple  =    (
        '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       :   Optional[Any]  =    (39, 42)
    _lowerCAmelCase       :   Union[str, Any]  =    tokenizer(_lowerCamelCase ,   entity_spans=[span] ,   add_prefix_space=_lowerCamelCase ,   return_tensors='pt'   )

    _lowerCAmelCase       :   int  =    model(**_lowerCamelCase   )

    # Verify word hidden states
    if model_size == "large":
        _lowerCAmelCase       :   Optional[Any]  =    torch.Size((1, 42, 1024)   )
        _lowerCAmelCase       :   Tuple  =    torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]   )
    else:  # base
        _lowerCAmelCase       :   str  =    torch.Size((1, 42, 768)   )
        _lowerCAmelCase       :   Optional[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] ,   _lowerCamelCase ,   atol=1e-4   ):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        _lowerCAmelCase       :   Optional[Any]  =    torch.Size((1, 1, 1024)   )
        _lowerCAmelCase       :   List[Any]  =    torch.tensor([[0.0466, -0.0106, -0.0179]]   )
    else:  # base
        _lowerCAmelCase       :   Tuple  =    torch.Size((1, 1, 768)   )
        _lowerCAmelCase       :   Optional[Any]  =    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] ,   _lowerCamelCase ,   atol=1e-4   ):
        raise ValueError

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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   str  =    {}
    with open(_lowerCamelCase ,   'r' ,   encoding='utf-8'   ) as f:
        for index, line in enumerate(_lowerCamelCase   ):
            _lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    line.rstrip().split('\t'   )
            _lowerCAmelCase       :   Any  =    index

    return entity_vocab


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








"""simple docstring"""






from collections.abc import Callable







class 	__UpperCamelCase    :

  def __init__(		self    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   list  =    []
      # Stores indexes of each item for supporting updates and deletion.
      _lowerCAmelCase       :   dict  =    {}
      # Stores current size of heap.
      _lowerCAmelCase       :   Union[str, Any]  =    0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      _lowerCAmelCase       :   Union[str, Any]  =    key or (lambda _A      : x)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return int((i - 1) / 2      ) if i > 0 else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    int(2 * i + 1      )
      return left if 0 < left < self.size else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    int(2 * i + 2      )
      return right if 0 < right < self.size else None

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.arr[j], self.arr[i]

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      return self.arr[i][1] < self.arr[j][1]

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self._left(_A      )
      _lowerCAmelCase       :   str  =    self._right(_A      )
      _lowerCAmelCase       :   Tuple  =    i

      if left is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   int  =    left
      if right is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   Optional[int]  =    right

      return valid_parent

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self._parent(_A      )
      while parent is not None and not self._cmp(_A    ,_A      ):
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    parent, self._parent(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self._get_valid_parent(_A      )
      while valid_parent != index:
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    valid_parent, self._get_valid_parent(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   int  =    self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    [item, self.key(_A      )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_A      )
      self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   List[str]  =    self.pos_map[item]
      del self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    self.arr[self.size - 1]
      _lowerCAmelCase       :   Optional[Any]  =    index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
          self._heapify_up(_A      )
          self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    len(self.arr      )
      if arr_len == self.size:
          self.arr.append([item, self.key(_A      )]      )
      else:
          _lowerCAmelCase       :   Any  =    [item, self.key(_A      )]
      _lowerCAmelCase       :   str  =    self.size
      self.size += 1
      self._heapify_up(self.size - 1      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.arr[0] if self.size else None







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.get_top()
      if top_item_tuple:
          self.delete_item(top_item_tuple[0]      )
      return top_item_tuple





def 		lowerCamelCase__		(					):

    '''simple docstring'''





if __name__ == "__main__":
 import doctest

 doctest.testmod()





