




"""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       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      Union[str, Any]	,     snake_case__							:      Tuple	,     snake_case__							:      List[str]	,     snake_case__							:      Any					):
  """simple docstring"""

  with open(snake_case__					) as metadata_file:
    _snake_case							:	List[Any]			=						json.load(snake_case__					)
  _snake_case							:	Any			=						LukeConfig(use_entity_aware_attention=snake_case__	,     **metadata["""model_config"""]					)

  # Load in the weights from the checkpoint_path
  _snake_case							:	Optional[int]			=						torch.load(snake_case__	,     map_location="""cpu"""					)

  # Load the entity vocab file
  _snake_case							:	int			=						load_entity_vocab(snake_case__					)

  _snake_case							:	Union[str, Any]			=						RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]					)

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

  _snake_case							:	List[str]			=						LukeTokenizer.from_pretrained(snake_case__					)

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

  _snake_case							:	Union[str, Any]			=						LukeModel(config=snake_case__					).eval()

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

  _snake_case							:	Union[str, Any]			=						(
      """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 ."""
  )
  _snake_case							:	Optional[int]			=						(39, 42)
  _snake_case							:	Tuple			=						tokenizer(snake_case__	,     entity_spans=[span]	,     add_prefix_space=snake_case__	,     return_tensors="""pt"""					)

  _snake_case							:	Optional[Any]			=						model(**snake_case__					)

  # Verify word hidden states
  if model_size == "large":
    _snake_case							:	Optional[Any]			=						torch.Size((1, 42, 10_24)					)
    _snake_case							:	Any			=						torch.tensor(
        [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]					)
  else:  # base
    _snake_case							:	List[str]			=						torch.Size((1, 42, 7_68)					)
    _snake_case							:	Optional[int]			=						torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]					)

  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]	,     snake_case__	,     atol=1e-4					):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    _snake_case							:	str			=						torch.Size((1, 1, 10_24)					)
    _snake_case							:	List[str]			=						torch.tensor([[0.04_66, -0.01_06, -0.01_79]]					)
  else:  # base
    _snake_case							:	Optional[int]			=						torch.Size((1, 1, 7_68)					)
    _snake_case							:	List[Any]			=						torch.tensor([[0.14_57, 0.10_44, 0.01_74]]					)

  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]	,     snake_case__	,     atol=1e-4					):
    raise ValueError

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


def       UpperCAmelCase__ (snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						{}
  with open(snake_case__	,     """r"""	,     encoding="""utf-8"""					) as f:
    for index, line in enumerate(snake_case__					):
      _snake_case	,      _snake_case							:	int			=						line.rstrip().split("""\t"""					)
      _snake_case							:	str			=						index

  return entity_vocab


if __name__ == "__main__":
     A_      			=       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_      			=       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 abc import ABC, abstractmethod
from argparse import ArgumentParser






class       lowercase(  __a				):







 '''simple docstring'''





 @staticmethod
 @abstractmethod
 def 				UpperCamelCase_     (       a_:	ArgumentParser	):




   '''simple docstring'''



   raise NotImplementedError()


 @abstractmethod
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   raise NotImplementedError()


