





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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







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

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

	# Load the entity vocab file
	lowerCamelCase__              =	load_original_entity_vocab(lowerCamelCase__							)
	# 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=lowerCamelCase__ ,rstrip=lowerCamelCase__							)
	lowerCamelCase__              =	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__              =	json.load(lowerCamelCase__							)
	lowerCamelCase__              =	"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__              =	MLukeTokenizer.from_pretrained(lowerCamelCase__							)

	# 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=lowerCamelCase__							).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__              =	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__              =	MLukeTokenizer.from_pretrained(lowerCamelCase__ ,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(lowerCamelCase__ ,entity_spans=[span] ,return_tensors='''pt'''							)

	lowerCamelCase__              =	model(**lowerCamelCase__							)

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		lowerCamelCase__              =	torch.Size((1, 33, 768)							)
		lowerCamelCase__              =	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
		lowerCamelCase__              =	torch.Size((1, 1, 768)							)
		lowerCamelCase__              =	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
	lowerCamelCase__              =	MLukeTokenizer.from_pretrained(lowerCamelCase__							)
	lowerCamelCase__              =	"Tokyo is the capital of <mask>."
	lowerCamelCase__              =	(24, 30)
	lowerCamelCase__              =	tokenizer(lowerCamelCase__ ,entity_spans=[span] ,return_tensors='''pt'''							)

	lowerCamelCase__              =	model(**lowerCamelCase__							)

	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(lowerCamelCase__							)

	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(lowerCamelCase__							)							)
	model.save_pretrained(lowerCamelCase__							)





def lowerCAmelCase__(__snake_case							)       ->   List[str]:







	'''simple docstring'''







	lowerCamelCase__              =	["[MASK]", "[PAD]", "[UNK]"]

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

	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__":
					_a						=   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."
					)
					_a						=   parser.parse_args()
					convert_luke_checkpoint(
					    args.checkpoint_path,
					    args.metadata_path,
					    args.entity_vocab_path,
					    args.pytorch_dump_folder_path,
					    args.model_size,
					)










def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	if not grid or not grid[0]:
		raise TypeError('''The grid does not contain the appropriate information'''							)

	for cell_n in range(1 ,len(grid[0]							)							):
		grid[0][cell_n] += grid[0][cell_n - 1]
	lowerCamelCase__              =	grid[0]

	for row_n in range(1 ,len(__snake_case							)							):
		lowerCamelCase__              =	grid[row_n]
		lowerCamelCase__              =	fill_row(__snake_case ,__snake_case							)
		lowerCamelCase__              =	grid[row_n]

	return grid[-1][-1]





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   list:







	'''simple docstring'''







	current_row[0] += row_above[0]
	for cell_n in range(1 ,len(__snake_case							)							):
		current_row[cell_n] += min(current_row[cell_n - 1] ,row_above[cell_n]							)

	return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()



