





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  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   # Load configuration defined in the metadata file
   with open(SCREAMING_SNAKE_CASE_   ) as metadata_file:
      a__	:						Any      		=  json.load(SCREAMING_SNAKE_CASE_   )
   a__	:						Optional[Any]      		=  LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE_							, **metadata["model_config"]   )

   # Load in the weights from the checkpoint_path
   a__	:						Union[str, Any]      		=  torch.load(SCREAMING_SNAKE_CASE_							, map_location="cpu"   )["module"]

   # Load the entity vocab file
   a__	:						List[Any]      		=  load_original_entity_vocab(SCREAMING_SNAKE_CASE_   )
   # add an entry for [MASK2]
   a__	:						Any      		=  max(entity_vocab.values()   ) + 1
   config.entity_vocab_size += 1

   a__	:						Tuple      		=  XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]   )

   # Add special tokens to the token vocabulary for downstream tasks
   a__	:						Dict      		=  AddedToken("<ent>"							, lstrip=SCREAMING_SNAKE_CASE_							, rstrip=SCREAMING_SNAKE_CASE_   )
   a__	:						List[Any]      		=  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:
      a__	:						Any      		=  json.load(SCREAMING_SNAKE_CASE_   )
   a__	:						Dict      		=  "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_   )

   a__	:						List[Any]      		=  MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_   )

   # Initialize the embeddings of the special tokens
   a__	:						Dict      		=  tokenizer.convert_tokens_to_ids(["@"]   )[0]
   a__	:						List[Any]      		=  tokenizer.convert_tokens_to_ids(["#"]   )[0]

   a__	:						Union[str, Any]      		=  state_dict["embeddings.word_embeddings.weight"]
   a__	:						int      		=  word_emb[ent_init_index].unsqueeze(0   )
   a__	:						Optional[Any]      		=  word_emb[enta_init_index].unsqueeze(0   )
   a__	:						Optional[int]      		=  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__	:						int      		=  state_dict[bias_name]
      a__	:						List[Any]      		=  decoder_bias[ent_init_index].unsqueeze(0   )
      a__	:						int      		=  decoder_bias[enta_init_index].unsqueeze(0   )
      a__	:						List[Any]      		=  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__	:						Optional[int]      		=  F"""encoder.layer.{layer_index}.attention.self."""
         a__	:						List[Any]      		=  state_dict[prefix + matrix_name]
         a__	:						List[str]      		=  state_dict[prefix + matrix_name]
         a__	:						Union[str, Any]      		=  state_dict[prefix + matrix_name]

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

   a__	:						List[str]      		=  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"   )
   a__	:						Any      		=  OrderedDict()
   for key, value in state_dict.items():
      if not (key.startswith("lm_head"   ) or key.startswith("entity_predictions"   )):
         a__	:						Dict      		=  state_dict[key]
      else:
         a__	:						List[Any]      		=  state_dict[key]

   a__		,     a__	:						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
   a__	:						str      		=  MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_							, task="entity_classification"   )

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

   a__	:						Dict      		=  model(**SCREAMING_SNAKE_CASE_   )

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      a__	:						Tuple      		=  torch.Size((1, 33, 768)   )
      a__	:						str      		=  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]							, SCREAMING_SNAKE_CASE_							, atol=1E-4   ):
      raise ValueError

   # Verify entity hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      a__	:						List[Any]      		=  torch.Size((1, 1, 768)   )
      a__	:						Any      		=  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]							, SCREAMING_SNAKE_CASE_							, atol=1E-4   ):
      raise ValueError

   # Verify masked word/entity prediction
   a__	:						Union[str, Any]      		=  MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_   )
   a__	:						Any      		=  "Tokyo is the capital of <mask>."
   a__	:						Tuple      		=  (24, 30)
   a__	:						Union[str, Any]      		=  tokenizer(SCREAMING_SNAKE_CASE_							, entity_spans=[span]							, return_tensors="pt"   )

   a__	:						Tuple      		=  model(**SCREAMING_SNAKE_CASE_   )

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

   a__	:						Any      		=  outputs.entity_logits[0][0].argmax().item()
   a__	:						Any      		=  [
       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  (      lowerCamelCase   ):
   a__	:						str      		=  ["[MASK]", "[PAD]", "[UNK]"]

   a__	:						str      		=  [json.loads(SCREAMING_SNAKE_CASE_   ) for line in open(SCREAMING_SNAKE_CASE_   )]

   a__	:						Optional[int]      		=  {}
   for entry in data:
      a__	:						Tuple      		=  entry["id"]
      for entity_name, language in entry["entities"]:
         if entity_name in SPECIAL_TOKENS:
            a__	:						Union[str, Any]      		=  entity_id
            break
         a__	:						Union[str, Any]      		=  F"""{language}:{entity_name}"""
         a__	:						List[Any]      		=  entity_id
   return new_mapping


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						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."""
  )
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       :			str			      =						{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						["""DistilBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						[
      """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DistilBertForMaskedLM""",
      """DistilBertForMultipleChoice""",
      """DistilBertForQuestionAnswering""",
      """DistilBertForSequenceClassification""",
      """DistilBertForTokenClassification""",
      """DistilBertModel""",
      """DistilBertPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						[
      """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDistilBertForMaskedLM""",
      """TFDistilBertForMultipleChoice""",
      """TFDistilBertForQuestionAnswering""",
      """TFDistilBertForSequenceClassification""",
      """TFDistilBertForTokenClassification""",
      """TFDistilBertMainLayer""",
      """TFDistilBertModel""",
      """TFDistilBertPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Any			      =						[
      """FlaxDistilBertForMaskedLM""",
      """FlaxDistilBertForMultipleChoice""",
      """FlaxDistilBertForQuestionAnswering""",
      """FlaxDistilBertForSequenceClassification""",
      """FlaxDistilBertForTokenClassification""",
      """FlaxDistilBertModel""",
      """FlaxDistilBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

