


"""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     :					List[str],     _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Optional[int],     _lowerCAmelCase     :					int       ):






     """simple docstring"""

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

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

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

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

     # Add special tokens to the token vocabulary for downstream tasks
     _a    	=				AddedToken('''<ent>''',     lstrip=_lowerCAmelCase,     rstrip=_lowerCAmelCase       )
     _a    	=				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:
          _a    	=				json.load(_lowerCAmelCase       )
     _a    	=				'''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       )

     _a    	=				MLukeTokenizer.from_pretrained(_lowerCAmelCase       )

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

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

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

     _a    	=				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'''       )
     _a    	=				OrderedDict()
     for key, value in state_dict.items():
          if not (key.startswith('''lm_head'''       ) or key.startswith('''entity_predictions'''       )):
               _a    	=				state_dict[key]
          else:
               _a    	=				state_dict[key]

     _a	,      _a    	=				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
     _a    	=				MLukeTokenizer.from_pretrained(_lowerCAmelCase,     task='''entity_classification'''       )

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

     _a    	=				model(**_lowerCAmelCase       )

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

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

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

     _a    	=				model(**_lowerCAmelCase       )

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

     _a    	=				outputs.entity_logits[0][0].argmax().item()
     _a    	=				[
         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     :					List[Any]       ):






     """simple docstring"""

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

     _a    	=				[json.loads(_lowerCAmelCase       ) for line in open(_lowerCAmelCase       )]

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


if __name__ == "__main__":
   __snake_case		=       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.'''
   )
   __snake_case		=       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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case		=       {
    '''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig'''],
    '''tokenization_lxmert''': ['''LxmertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''LxmertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''LxmertEncoder''',
       '''LxmertForPreTraining''',
       '''LxmertForQuestionAnswering''',
       '''LxmertModel''',
       '''LxmertPreTrainedModel''',
       '''LxmertVisualFeatureEncoder''',
       '''LxmertXLayer''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFLxmertForPreTraining''',
       '''TFLxmertMainLayer''',
       '''TFLxmertModel''',
       '''TFLxmertPreTrainedModel''',
       '''TFLxmertVisualFeatureEncoder''',
   ]


if TYPE_CHECKING:
   from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
   from .tokenization_lxmert import LxmertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_lxmert_fast import LxmertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lxmert import (
          LxmertEncoder,
          LxmertForPreTraining,
          LxmertForQuestionAnswering,
          LxmertModel,
          LxmertPreTrainedModel,
          LxmertVisualFeatureEncoder,
          LxmertXLayer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_lxmert import (
          TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLxmertForPreTraining,
          TFLxmertMainLayer,
          TFLxmertModel,
          TFLxmertPreTrainedModel,
          TFLxmertVisualFeatureEncoder,
      )

else:
   import sys

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