


'''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       (     __A      ,			__A      ,			__A      ,			__A      ,			__A    )							->		List[str]:


      '''simple docstring'''



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

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

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

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

      # Add special tokens to the token vocabulary for downstream tasks
      UpperCamelCase__										=				AddedToken("<ent>"      ,			lstrip=__A      ,			rstrip=__A    )
      UpperCamelCase__										=				AddedToken("<ent2>"      ,			lstrip=__A      ,			rstrip=__A    )
      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(__A    )
      with open(os.path.join(__A      ,			"tokenizer_config.json"    )      ,			"r"    ) as f:
            UpperCamelCase__										=				json.load(__A    )
      UpperCamelCase__										=				"MLukeTokenizer"
      with open(os.path.join(__A      ,			"tokenizer_config.json"    )      ,			"w"    ) as f:
            json.dump(__A      ,			__A    )

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

      UpperCamelCase__										=				MLukeTokenizer.from_pretrained(__A    )

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

      UpperCamelCase__										=				state_dict["embeddings.word_embeddings.weight"]
      UpperCamelCase__										=				word_emb[ent_init_index].unsqueeze(0    )
      UpperCamelCase__										=				word_emb[enta_init_index].unsqueeze(0    )
      UpperCamelCase__										=				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"]:
            UpperCamelCase__										=				state_dict[bias_name]
            UpperCamelCase__										=				decoder_bias[ent_init_index].unsqueeze(0    )
            UpperCamelCase__										=				decoder_bias[enta_init_index].unsqueeze(0    )
            UpperCamelCase__										=				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"]:
                  UpperCamelCase__										=				F'''encoder.layer.{layer_index}.attention.self.'''
                  UpperCamelCase__										=				state_dict[prefix + matrix_name]
                  UpperCamelCase__										=				state_dict[prefix + matrix_name]
                  UpperCamelCase__										=				state_dict[prefix + matrix_name]

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

      UpperCamelCase__										=				LukeForMaskedLM(config=__A    ).eval()

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

      UpperCamelCase__					,	UpperCamelCase__										=				model.load_state_dict(__A      ,			strict=__A    )

      if set(__A    ) != {"luke.embeddings.position_ids"}:
            raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''    )
      if set(__A    ) != {
          "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
      UpperCamelCase__										=				MLukeTokenizer.from_pretrained(__A      ,			task="entity_classification"    )

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

      UpperCamelCase__										=				model(**__A    )

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            UpperCamelCase__										=				torch.Size((1, 33, 768)    )
            UpperCamelCase__										=				torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]    )

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

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            UpperCamelCase__										=				torch.Size((1, 1, 768)    )
            UpperCamelCase__										=				torch.tensor([[-0.1482, 0.0609, 0.0322]]    )

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

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

      UpperCamelCase__										=				model(**__A    )

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

      UpperCamelCase__										=				outputs.entity_logits[0][0].argmax().item()
      UpperCamelCase__										=				[
          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(__A    )    )
      model.save_pretrained(__A    )







def _UpperCamelCase       (     __A    )							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__										=				["[MASK]", "[PAD]", "[UNK]"]

      UpperCamelCase__										=				[json.loads(__A    ) for line in open(__A    )]

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


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__  :					Any        =       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'''







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	bool:
	return number & 1 == 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()

