



"""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__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      # Load configuration defined in the metadata file
      with open(lowerCAmelCase__   ) as metadata_file:
            UpperCAmelCase_    				=							json.load(lowerCAmelCase__   )
      UpperCAmelCase_    				=							LukeConfig(use_entity_aware_attention=lowerCAmelCase__			,    **metadata["model_config"]   )

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

      # Load the entity vocab file
      UpperCAmelCase_    				=							load_original_entity_vocab(lowerCAmelCase__   )
      # 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=lowerCAmelCase__			,    rstrip=lowerCAmelCase__   )
      UpperCAmelCase_    				=							AddedToken("<ent2>"			,    lstrip=lowerCAmelCase__			,    rstrip=lowerCAmelCase__   )
      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(lowerCAmelCase__   )
      with open(os.path.join(lowerCAmelCase__			,    "tokenizer_config.json"   )			,    "r"   ) as f:
            UpperCAmelCase_    				=							json.load(lowerCAmelCase__   )
      UpperCAmelCase_    				=							"MLukeTokenizer"
      with open(os.path.join(lowerCAmelCase__			,    "tokenizer_config.json"   )			,    "w"   ) as f:
            json.dump(lowerCAmelCase__			,    lowerCAmelCase__   )

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

      UpperCAmelCase_    				=							MLukeTokenizer.from_pretrained(lowerCAmelCase__   )

      # 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=lowerCAmelCase__   ).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(lowerCAmelCase__			,    strict=lowerCAmelCase__   )

      if set(lowerCAmelCase__   ) != {"luke.embeddings.position_ids"}:
            raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""   )
      if set(lowerCAmelCase__   ) != {
          "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(lowerCAmelCase__			,    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(lowerCAmelCase__			,    entity_spans=[span]			,    return_tensors="pt"   )

      UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

      # 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]			,    lowerCAmelCase__			,    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]			,    lowerCAmelCase__			,    atol=1e-4   ):
            raise ValueError

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

      UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

      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(lowerCAmelCase__   )

      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(lowerCAmelCase__   )   )
      model.save_pretrained(lowerCAmelCase__   )
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							["[MASK]", "[PAD]", "[UNK]"]

      UpperCAmelCase_    				=							[json.loads(lowerCAmelCase__   ) for line in open(lowerCAmelCase__   )]

      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__":
  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"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase      =		{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		["""PerceiverFeatureExtractor"""]
  lowerCamelCase      =		["""PerceiverImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		[
      """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PerceiverForImageClassificationConvProcessing""",
      """PerceiverForImageClassificationFourier""",
      """PerceiverForImageClassificationLearned""",
      """PerceiverForMaskedLM""",
      """PerceiverForMultimodalAutoencoding""",
      """PerceiverForOpticalFlow""",
      """PerceiverForSequenceClassification""",
      """PerceiverLayer""",
      """PerceiverModel""",
      """PerceiverPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
  from .tokenization_perceiver import PerceiverTokenizer

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_perceiver import PerceiverFeatureExtractor
    from .image_processing_perceiver import PerceiverImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_perceiver import (
        PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PerceiverForImageClassificationConvProcessing,
        PerceiverForImageClassificationFourier,
        PerceiverForImageClassificationLearned,
        PerceiverForMaskedLM,
        PerceiverForMultimodalAutoencoding,
        PerceiverForOpticalFlow,
        PerceiverForSequenceClassification,
        PerceiverLayer,
        PerceiverModel,
        PerceiverPreTrainedModel,
    )

else:
  import sys

  lowerCamelCase      =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




