


"""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 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				):


  '''simple docstring'''







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

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

  # Load the entity vocab file
  lowerCAmelCase     :     List[str]								=							load_original_entity_vocab(SCREAMING_SNAKE_CASE				)
  # add an entry for [MASK2]
  lowerCAmelCase     :     Dict								=							max(entity_vocab.values()				) + 1
  config.entity_vocab_size += 1

  lowerCAmelCase     :     List[Any]								=							XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]				)

  # Add special tokens to the token vocabulary for downstream tasks
  lowerCAmelCase     :     Union[str, Any]								=							AddedToken("<ent>"				,      lstrip=SCREAMING_SNAKE_CASE				,      rstrip=SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     List[str]								=							AddedToken("<ent2>"				,      lstrip=SCREAMING_SNAKE_CASE				,      rstrip=SCREAMING_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(SCREAMING_SNAKE_CASE				)
  with open(os.path.join(SCREAMING_SNAKE_CASE				,      "tokenizer_config.json"				)				,      "r"				) as f:
    lowerCAmelCase     :     int								=							json.load(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[int]								=							"MLukeTokenizer"
  with open(os.path.join(SCREAMING_SNAKE_CASE				,      "tokenizer_config.json"				)				,      "w"				) as f:
    json.dump(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

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

  lowerCAmelCase     :     List[Any]								=							MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				)

  # Initialize the embeddings of the special tokens
  lowerCAmelCase     :     Union[str, Any]								=							tokenizer.convert_tokens_to_ids(["@"]				)[0]
  lowerCAmelCase     :     Tuple								=							tokenizer.convert_tokens_to_ids(["#"]				)[0]

  lowerCAmelCase     :     Optional[int]								=							state_dict["embeddings.word_embeddings.weight"]
  lowerCAmelCase     :     str								=							word_emb[ent_init_index].unsqueeze(0				)
  lowerCAmelCase     :     int								=							word_emb[enta_init_index].unsqueeze(0				)
  lowerCAmelCase     :     Optional[Any]								=							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"]:
    lowerCAmelCase     :     Optional[Any]								=							state_dict[bias_name]
    lowerCAmelCase     :     List[str]								=							decoder_bias[ent_init_index].unsqueeze(0				)
    lowerCAmelCase     :     Tuple								=							decoder_bias[enta_init_index].unsqueeze(0				)
    lowerCAmelCase     :     Tuple								=							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"]:
      lowerCAmelCase     :     int								=							f"""encoder.layer.{layer_index}.attention.self."""
      lowerCAmelCase     :     List[str]								=							state_dict[prefix + matrix_name]
      lowerCAmelCase     :     Optional[int]								=							state_dict[prefix + matrix_name]
      lowerCAmelCase     :     Dict								=							state_dict[prefix + matrix_name]

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

  lowerCAmelCase     :     Dict								=							LukeForMaskedLM(config=SCREAMING_SNAKE_CASE				).eval()

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

  lowerCAmelCase						,  lowerCAmelCase     :     Optional[int]								=							model.load_state_dict(SCREAMING_SNAKE_CASE				,      strict=SCREAMING_SNAKE_CASE				)

  if set(SCREAMING_SNAKE_CASE				) != {"luke.embeddings.position_ids"}:
    raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""				)
  if set(SCREAMING_SNAKE_CASE				) != {
      "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
  lowerCAmelCase     :     str								=							MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				,      task="entity_classification"				)

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

  lowerCAmelCase     :     Optional[Any]								=							model(**SCREAMING_SNAKE_CASE				)

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase     :     Tuple								=							torch.Size((1, 3_3, 7_6_8)				)
    lowerCAmelCase     :     Dict								=							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]				,      SCREAMING_SNAKE_CASE				,      atol=1E-4				):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase     :     Tuple								=							torch.Size((1, 1, 7_6_8)				)
    lowerCAmelCase     :     Optional[int]								=							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]				,      SCREAMING_SNAKE_CASE				,      atol=1E-4				):
    raise ValueError

  # Verify masked word/entity prediction
  lowerCAmelCase     :     Tuple								=							MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     List[str]								=							"Tokyo is the capital of <mask>."
  lowerCAmelCase     :     Optional[int]								=							(2_4, 3_0)
  lowerCAmelCase     :     Any								=							tokenizer(SCREAMING_SNAKE_CASE				,      entity_spans=[span]				,      return_tensors="pt"				)

  lowerCAmelCase     :     Any								=							model(**SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     Union[str, Any]								=							encoding["input_ids"][0].tolist()
  lowerCAmelCase     :     str								=							input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"				)				)
  lowerCAmelCase     :     Tuple								=							outputs.logits[0][mask_position_id].argmax(dim=-1				)
  assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     Any								=							outputs.entity_logits[0][0].argmax().item()
  lowerCAmelCase     :     List[str]								=							[
      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(SCREAMING_SNAKE_CASE				)				)
  model.save_pretrained(SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							["[MASK]", "[PAD]", "[UNK]"]

  lowerCAmelCase     :     Tuple								=							[json.loads(SCREAMING_SNAKE_CASE				) for line in open(SCREAMING_SNAKE_CASE				)]

  lowerCAmelCase     :     Any								=							{}
  for entry in data:
    lowerCAmelCase     :     Tuple								=							entry["id"]
    for entity_name, language in entry["entities"]:
      if entity_name in SPECIAL_TOKENS:
        lowerCAmelCase     :     Tuple								=							entity_id
        break
      lowerCAmelCase     :     Tuple								=							f"""{language}:{entity_name}"""
      lowerCAmelCase     :     Tuple								=							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,
     )









"""simple docstring"""







import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
     from transformers.models.rag.configuration_rag import RagConfig
     from transformers.models.rag.tokenization_rag import RagTokenizer


@require_faiss
@require_torch
class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							tempfile.mkdtemp()
         lowerCAmelCase     :     Optional[int]								=							8

         # DPR tok
         lowerCAmelCase     :     Dict								=							[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         lowerCAmelCase     :     List[str]								=							os.path.join(self.tmpdirname		,     "dpr_tokenizer"						)
         os.makedirs(snake_case__		,     exist_ok=snake_case__						)
         lowerCAmelCase     :     Dict								=							os.path.join(snake_case__		,     DPR_VOCAB_FILES_NAMES["vocab_file"]						)
         with open(self.vocab_file		,     "w"		,     encoding="utf-8"						) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)

         # BART tok
         lowerCAmelCase     :     Optional[int]								=							[
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "<unk>",
         ]
         lowerCAmelCase     :     Optional[int]								=							dict(zip(snake_case__		,     range(len(snake_case__						)						)						)						)
         lowerCAmelCase     :     List[Any]								=							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         lowerCAmelCase     :     str								=							{"unk_token": "<unk>"}

         lowerCAmelCase     :     int								=							os.path.join(self.tmpdirname		,     "bart_tokenizer"						)
         os.makedirs(snake_case__		,     exist_ok=snake_case__						)
         lowerCAmelCase     :     int								=							os.path.join(snake_case__		,     BART_VOCAB_FILES_NAMES["vocab_file"]						)
         lowerCAmelCase     :     Dict								=							os.path.join(snake_case__		,     BART_VOCAB_FILES_NAMES["merges_file"]						)
         with open(self.vocab_file		,     "w"		,     encoding="utf-8"						) as fp:
           fp.write(json.dumps(snake_case__						) + "\n"						)
         with open(self.merges_file		,     "w"		,     encoding="utf-8"						) as fp:
           fp.write("\n".join(snake_case__						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,     "dpr_tokenizer"						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,     "bart_tokenizer"						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         shutil.rmtree(self.tmpdirname						)

       @require_tokenizers
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							os.path.join(self.tmpdirname		,     "rag_tokenizer"						)
         lowerCAmelCase     :     List[Any]								=							RagConfig(question_encoder=DPRConfig().to_dict()		,     generator=BartConfig().to_dict()						)
         lowerCAmelCase     :     Optional[Any]								=							RagTokenizer(question_encoder=self.get_dpr_tokenizer()		,     generator=self.get_bart_tokenizer()						)
         rag_config.save_pretrained(snake_case__						)
         rag_tokenizer.save_pretrained(snake_case__						)
         lowerCAmelCase     :     List[str]								=							RagTokenizer.from_pretrained(snake_case__		,     config=snake_case__						)
         self.assertIsInstance(new_rag_tokenizer.question_encoder		,     snake_case__						)
         self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()		,     rag_tokenizer.question_encoder.get_vocab()						)
         self.assertIsInstance(new_rag_tokenizer.generator		,     snake_case__						)
         self.assertEqual(new_rag_tokenizer.generator.get_vocab()		,     rag_tokenizer.generator.get_vocab()						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							RagTokenizer.from_pretrained("facebook/rag-token-nq"						)
         lowerCAmelCase     :     Dict								=							[
             "who got the first nobel prize in physics",
             "when is the next deadpool movie being released",
             "which mode is used for short wave broadcast service",
             "who is the owner of reading football club",
             "when is the next scandal episode coming out",
             "when is the last time the philadelphia won the superbowl",
             "what is the most current adobe flash player version",
             "how many episodes are there in dragon ball z",
             "what is the first step in the evolution of the eye",
             "where is gall bladder situated in human body",
             "what is the main mineral in lithium batteries",
             "who is the president of usa right now",
             "where do the greasers live in the outsiders",
             "panda is a national animal of which country",
             "what is the name of manchester united stadium",
         ]
         lowerCAmelCase     :     Union[str, Any]								=							tokenizer(snake_case__						)
         self.assertIsNotNone(snake_case__						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							RagTokenizer.from_pretrained("facebook/rag-sequence-nq"						)
         lowerCAmelCase     :     List[str]								=							[
             "who got the first nobel prize in physics",
             "when is the next deadpool movie being released",
             "which mode is used for short wave broadcast service",
             "who is the owner of reading football club",
             "when is the next scandal episode coming out",
             "when is the last time the philadelphia won the superbowl",
             "what is the most current adobe flash player version",
             "how many episodes are there in dragon ball z",
             "what is the first step in the evolution of the eye",
             "where is gall bladder situated in human body",
             "what is the main mineral in lithium batteries",
             "who is the president of usa right now",
             "where do the greasers live in the outsiders",
             "panda is a national animal of which country",
             "what is the name of manchester united stadium",
         ]
         lowerCAmelCase     :     str								=							tokenizer(snake_case__						)
         self.assertIsNotNone(snake_case__						)





