

'''simple docstring'''







from abc import ABC, abstractmethod
from argparse import ArgumentParser


class 					A__      (      _snake_case			):



    @staticmethod
    @abstractmethod
    def 					snake_case_			(					UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           raise NotImplementedError()







    @abstractmethod
    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           raise NotImplementedError()



'''simple docstring'''







import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       # Load configuration defined in the metadata file
       with open(UpperCAmelCase__				) as metadata_file:
              A_						=      json.load(UpperCAmelCase__				)
       A_						=      LukeConfig(use_entity_aware_attention=UpperCAmelCase__,       **metadata["""model_config"""]				)

       # Load in the weights from the checkpoint_path
       A_						=      torch.load(UpperCAmelCase__,       map_location="""cpu"""				)["""module"""]

       # Load the entity vocab file
       A_						=      load_original_entity_vocab(UpperCAmelCase__				)
       # add an entry for [MASK2]
       A_						=      max(entity_vocab.values()				) + 1
       config.entity_vocab_size += 1

       A_						=      XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]				)

       # Add special tokens to the token vocabulary for downstream tasks
       A_						=      AddedToken("""<ent>""",       lstrip=UpperCAmelCase__,       rstrip=UpperCAmelCase__				)
       A_						=      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__,       """tokenizer_config.json"""				),       """r"""				) as f:
              A_						=      json.load(UpperCAmelCase__				)
       A_						=      """MLukeTokenizer"""
       with open(os.path.join(UpperCAmelCase__,       """tokenizer_config.json"""				),       """w"""				) as f:
              json.dump(UpperCAmelCase__,       UpperCAmelCase__				)

       with open(os.path.join(UpperCAmelCase__,       MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]				),       """w"""				) as f:
              json.dump(UpperCAmelCase__,       UpperCAmelCase__				)

       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__				)

       # Initialize the embeddings of the special tokens
       A_						=      tokenizer.convert_tokens_to_ids(["""@"""]				)[0]
       A_						=      tokenizer.convert_tokens_to_ids(["""#"""]				)[0]

       A_						=      state_dict["""embeddings.word_embeddings.weight"""]
       A_						=      word_emb[ent_init_index].unsqueeze(0				)
       A_						=      word_emb[enta_init_index].unsqueeze(0				)
       A_						=      torch.cat([word_emb, ent_emb, enta_emb]				)
       # add special tokens for 'entity_predictions.bias'
       for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
              A_						=      state_dict[bias_name]
              A_						=      decoder_bias[ent_init_index].unsqueeze(0				)
              A_						=      decoder_bias[enta_init_index].unsqueeze(0				)
              A_						=      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]				)

       # 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"]:
                     A_						=      F'''encoder.layer.{layer_index}.attention.self.'''
                     A_						=      state_dict[prefix + matrix_name]
                     A_						=      state_dict[prefix + matrix_name]
                     A_						=      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       A_						=      state_dict["""entity_embeddings.entity_embeddings.weight"""]
       A_						=      entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0				)
       A_						=      torch.cat([entity_emb, entity_mask_emb]				)
       # add [MASK2] for 'entity_predictions.bias'
       A_						=      state_dict["""entity_predictions.bias"""]
       A_						=      entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0				)
       A_						=      torch.cat([entity_prediction_bias, entity_mask_bias]				)

       A_						=      LukeForMaskedLM(config=UpperCAmelCase__				).eval()

       state_dict.pop("""entity_predictions.decoder.weight"""				)
       state_dict.pop("""lm_head.decoder.weight"""				)
       state_dict.pop("""lm_head.decoder.bias"""				)
       A_						=      OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""				) or key.startswith("""entity_predictions"""				)):
                     A_						=      state_dict[key]
              else:
                     A_						=      state_dict[key]

       A_					,			A_						=      model.load_state_dict(UpperCAmelCase__,       strict=UpperCAmelCase__				)

       if set(UpperCAmelCase__				) != {"luke.embeddings.position_ids"}:
              raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''				)
       if set(UpperCAmelCase__				) != {
           "lm_head.decoder.weight",
           "lm_head.decoder.bias",
           "entity_predictions.decoder.weight",
       }:
              raise ValueError(F'''Unexpected missing_keys: {missing_keys}'''				)

       model.tie_weights()
       assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
       assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

       # Check outputs
       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__,       task="""entity_classification"""				)

       A_						=      """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
       A_						=      (0, 9)
       A_						=      tokenizer(UpperCAmelCase__,       entity_spans=[span],       return_tensors="""pt"""				)

       A_						=      model(**UpperCAmelCase__				)

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_						=      torch.Size((1, 33, 7_68)				)
              A_						=      torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]				)

       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":
              raise NotImplementedError
       else:  # base
              A_						=      torch.Size((1, 1, 7_68)				)
              A_						=      torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]				)

       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

       # Verify masked word/entity prediction
       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__				)
       A_						=      """Tokyo is the capital of <mask>."""
       A_						=      (24, 30)
       A_						=      tokenizer(UpperCAmelCase__,       entity_spans=[span],       return_tensors="""pt"""				)

       A_						=      model(**UpperCAmelCase__				)

       A_						=      encoding["""input_ids"""][0].tolist()
       A_						=      input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""				)				)
       A_						=      outputs.logits[0][mask_position_id].argmax(dim=-1				)
       assert "Japan" == tokenizer.decode(UpperCAmelCase__				)

       A_						=      outputs.entity_logits[0][0].argmax().item()
       A_						=      [
           entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
       ]
       assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""				)][0] == "en:Japan"

       # Finally, save our PyTorch model and tokenizer
       print("""Saving PyTorch model to {}""".format(UpperCAmelCase__				)				)
       model.save_pretrained(UpperCAmelCase__				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      ["""[MASK]""", """[PAD]""", """[UNK]"""]

       A_						=      [json.loads(UpperCAmelCase__				) for line in open(UpperCAmelCase__				)]

       A_						=      {}
       for entry in data:
              A_						=      entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            A_						=      entity_id
                            break
                     A_						=      F'''{language}:{entity_name}'''
                     A_						=      entity_id
       return new_mapping


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,
      )
