


'''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 			__UpperCamelCase    (    _A    :				Tuple					,   _A    :				Optional[int]					,   _A    :				List[str]					,   _A    :				int					,   _A    :				Optional[Any]				)    ->      Optional[Any]:




	"""simple docstring"""





	with open(lowerCamelCase__				) as metadata_file:
		lowerCAmelCase					:    List[Any]      							= json.load(lowerCamelCase__				)
	lowerCAmelCase					:    Dict      							= LukeConfig(use_entity_aware_attention=lowerCamelCase__					,   **metadata['model_config']				)

	# Load in the weights from the checkpoint_path
	lowerCAmelCase					:    Optional[Any]      							= torch.load(lowerCamelCase__					,   map_location='cpu'				)["module"]

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

	lowerCAmelCase					:    List[str]      							= XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']				)

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

	lowerCAmelCase					:    Tuple      							= MLukeTokenizer.from_pretrained(lowerCamelCase__				)

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

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

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

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

	lowerCAmelCase					:    Optional[Any]      							= 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
	lowerCAmelCase					:    List[Any]      							= MLukeTokenizer.from_pretrained(lowerCamelCase__					,   task='entity_classification'				)

	lowerCAmelCase					:    Optional[int]      							= "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
	lowerCAmelCase					:    Dict      							= (0, 9)
	lowerCAmelCase					:    List[str]      							= tokenizer(lowerCamelCase__					,   entity_spans=[span]					,   return_tensors='pt'				)

	lowerCAmelCase					:    int      							= model(**lowerCamelCase__				)

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		lowerCAmelCase					:    int      							= torch.Size((1, 33, 7_68)				)
		lowerCAmelCase					:    str      							= torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]				)

	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
		lowerCAmelCase					:    Tuple      							= torch.Size((1, 1, 7_68)				)
		lowerCAmelCase					:    Tuple      							= torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]				)

	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
	lowerCAmelCase					:    Optional[int]      							= MLukeTokenizer.from_pretrained(lowerCamelCase__				)
	lowerCAmelCase					:    int      							= "Tokyo is the capital of <mask>."
	lowerCAmelCase					:    List[str]      							= (24, 30)
	lowerCAmelCase					:    Any      							= tokenizer(lowerCamelCase__					,   entity_spans=[span]					,   return_tensors='pt'				)

	lowerCAmelCase					:    Tuple      							= model(**lowerCamelCase__				)

	lowerCAmelCase					:    List[Any]      							= encoding["input_ids"][0].tolist()
	lowerCAmelCase					:    Union[str, Any]      							= input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'				)				)
	lowerCAmelCase					:    Optional[Any]      							= outputs.logits[0][mask_position_id].argmax(dim=-1				)
	assert "Japan" == tokenizer.decode(lowerCamelCase__				)

	lowerCAmelCase					:    Dict      							= outputs.entity_logits[0][0].argmax().item()
	lowerCAmelCase					:    Optional[int]      							= [
	    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 			__UpperCamelCase    (    _A    :				List[Any]				)    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= ["[MASK]", "[PAD]", "[UNK]"]

	lowerCAmelCase					:    Dict      							= [json.loads(lowerCamelCase__				) for line in open(lowerCamelCase__				)]

	lowerCAmelCase					:    str      							= {}
	for entry in data:
		lowerCAmelCase					:    Any      							= entry["id"]
		for entity_name, language in entry["entities"]:
			if entity_name in SPECIAL_TOKENS:
				lowerCAmelCase					:    int      							= entity_id
				break
			lowerCAmelCase					:    Union[str, Any]      							= F"{language}:{entity_name}"
			lowerCAmelCase					:    List[str]      							= entity_id
	return new_mapping


if __name__ == "__main__":
	_lowerCAmelCase  :		Any		     =	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  :		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,
	)




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				int				)    ->      bool:




	"""simple docstring"""





	return number & 1 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
