

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        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




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

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

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

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

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

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

     SCREAMING_SNAKE_CASE__					=					MLukeTokenizer.from_pretrained(_A       )

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

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

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

     SCREAMING_SNAKE_CASE__					=					LukeForMaskedLM(config=_A       ).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__					=					OrderedDict()
     for key, value in state_dict.items():
          if not (key.startswith('''lm_head'''       ) or key.startswith('''entity_predictions'''       )):
               SCREAMING_SNAKE_CASE__					=					state_dict[key]
          else:
               SCREAMING_SNAKE_CASE__					=					state_dict[key]

     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					model.load_state_dict(_A   ,	strict=_A       )

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

     SCREAMING_SNAKE_CASE__					=					'''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__					=					(0, 9)
     SCREAMING_SNAKE_CASE__					=					tokenizer(_A   ,	entity_spans=[span]   ,	return_tensors='''pt'''       )

     SCREAMING_SNAKE_CASE__					=					model(**_A       )

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

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

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

     SCREAMING_SNAKE_CASE__					=					model(**_A       )

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

     SCREAMING_SNAKE_CASE__					=					outputs.entity_logits[0][0].argmax().item()
     SCREAMING_SNAKE_CASE__					=					[
         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(_A       )       )
     model.save_pretrained(_A       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE__					=					[json.loads(_A       ) for line in open(_A       )]

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						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.'''
       )
       _SCREAMING_SNAKE_CASE  :						List[str]         =    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,
)


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


