




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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict	,							lowercase	:       List[str]	,							lowercase	:       int	,							lowercase	:       Union[str, Any]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      with open(lowercase     ) as metadata_file:
            lowerCamelCase_		     =    json.load(lowercase     )
      lowerCamelCase_		     =    LukeConfig(use_entity_aware_attention=lowercase	,							**metadata['model_config']     )

      # Load in the weights from the checkpoint_path
      lowerCamelCase_		     =    torch.load(lowercase	,							map_location='cpu'     )['module']

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

      lowerCamelCase_		     =    XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

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

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

      lowerCamelCase_		     =    MLukeTokenizer.from_pretrained(lowercase     )

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

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

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

      lowerCamelCase_		     =    LukeForMaskedLM(config=lowercase     ).eval()

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

      lowerCamelCase_     ,				lowerCamelCase_		     =    model.load_state_dict(lowercase	,							strict=lowercase     )

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

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

      lowerCamelCase_		     =    model(**lowercase     )

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            lowerCamelCase_		     =    torch.Size((1, 33, 7_68)     )
            lowerCamelCase_		     =    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]	,							lowercase	,							atol=1e-4     ):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            lowerCamelCase_		     =    torch.Size((1, 1, 7_68)     )
            lowerCamelCase_		     =    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]	,							lowercase	,							atol=1e-4     ):
            raise ValueError

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

      lowerCamelCase_		     =    model(**lowercase     )

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

      lowerCamelCase_		     =    outputs.entity_logits[0][0].argmax().item()
      lowerCamelCase_		     =    [
          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(lowercase     )     )
      model.save_pretrained(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int     ):


      '''simple docstring'''



      lowerCamelCase_		     =    ['[MASK]', '[PAD]', '[UNK]']

      lowerCamelCase_		     =    [json.loads(lowercase     ) for line in open(lowercase     )]

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


if __name__ == "__main__":
 lowerCamelCase				:		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."
 )
 lowerCamelCase				:		Optional[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,
 )









from typing import TYPE_CHECKING

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


lowerCamelCase				:		Union[str, Any]     =   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Optional[Any]     =   ["ConvBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Dict     =   [
     "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConvBertForMaskedLM",
     "ConvBertForMultipleChoice",
     "ConvBertForQuestionAnswering",
     "ConvBertForSequenceClassification",
     "ConvBertForTokenClassification",
     "ConvBertLayer",
     "ConvBertModel",
     "ConvBertPreTrainedModel",
     "load_tf_weights_in_convbert",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   [
     "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFConvBertForMaskedLM",
     "TFConvBertForMultipleChoice",
     "TFConvBertForQuestionAnswering",
     "TFConvBertForSequenceClassification",
     "TFConvBertForTokenClassification",
     "TFConvBertLayer",
     "TFConvBertModel",
     "TFConvBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

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



