






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					= {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMAEForPreTraining',
      'ViTMAELayer',
      'ViTMAEModel',
      'ViTMAEPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TFViTMAEForPreTraining',
      'TFViTMAEModel',
      'TFViTMAEPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_mae import (
        VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMAEForPreTraining,
        ViTMAELayer,
        ViTMAEModel,
        ViTMAEPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''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(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Any,	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      with open(UpperCamelCase__							) as metadata_file:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=LukeConfig(use_entity_aware_attention=UpperCamelCase__,	**metadata['''model_config''']							)

      # Load in the weights from the checkpoint_path
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.load(UpperCamelCase__,	map_location='''cpu'''							)['''module''']

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

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

      # Add special tokens to the token vocabulary for downstream tasks
      SCREAMING_SNAKE_CASE__     :				List[Any]									=AddedToken('''<ent>''',	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Any									=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:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				int									='''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__							)

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)

      # Initialize the embeddings of the special tokens
      SCREAMING_SNAKE_CASE__     :				str									=tokenizer.convert_tokens_to_ids(['''@''']							)[0]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer.convert_tokens_to_ids(['''#''']							)[0]

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

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

      SCREAMING_SNAKE_CASE__     :				int									=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'''							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith('''lm_head'''							) or key.startswith('''entity_predictions'''							)):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=state_dict[key]
            else:
                  SCREAMING_SNAKE_CASE__     :				Any									=state_dict[key]

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=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
      SCREAMING_SNAKE_CASE__     :				Any									=MLukeTokenizer.from_pretrained(UpperCamelCase__,	task='''entity_classification'''							)

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

      SCREAMING_SNAKE_CASE__     :				List[Any]									=model(**UpperCamelCase__							)

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__     :				str									=torch.Size((1, 3_3, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]							)

      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
            SCREAMING_SNAKE_CASE__     :				Any									=torch.Size((1, 1, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]							)

      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
      SCREAMING_SNAKE_CASE__     :				str									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''Tokyo is the capital of <mask>.'''
      SCREAMING_SNAKE_CASE__     :				Dict									=(2_4, 3_0)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer(UpperCamelCase__,	entity_spans=[span],	return_tensors='''pt'''							)

      SCREAMING_SNAKE_CASE__     :				List[str]									=model(**UpperCamelCase__							)

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

      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=outputs.entity_logits[0][0].argmax().item()
      SCREAMING_SNAKE_CASE__     :				Dict									=[
          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  _a(  UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[Any]									=['''[MASK]''', '''[PAD]''', '''[UNK]''']

      SCREAMING_SNAKE_CASE__     :				List[str]									=[json.loads(UpperCamelCase__							) for line in open(UpperCamelCase__							)]

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


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,
  )