






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_):
							# Load configuration defined in the metadata file
							with open(a_) as metadata_file:
														snake_case_				=							json.load(a_)
							snake_case_				=							LukeConfig(use_entity_aware_attention=a_							,     **metadata['model_config'])

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

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

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

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

							snake_case_				=							MLukeTokenizer.from_pretrained(a_)

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

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

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

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

							snake_case_			,		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
							snake_case_				=							MLukeTokenizer.from_pretrained(a_							,     task='entity_classification')

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

							snake_case_				=							model(**a_)

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

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

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

							snake_case_				=							model(**a_)

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

							snake_case_				=							outputs.entity_logits[0][0].argmax().item()
							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_):
							snake_case_				=							['[MASK]', '[PAD]', '[UNK]']

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

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


if __name__ == "__main__":
	lowercase  							=					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."
	)
	lowercase  							=					parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)













class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a					)       ->    None:
								snake_case_				=							set_counts
								snake_case_				=							max(a					)
								snake_case_				=							len(a					)
								snake_case_				=							[1] * num_sets
								snake_case_				=							list(range(a					)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a					)       ->    bool:
								snake_case_				=							self.get_parent(a					)
								snake_case_				=							self.get_parent(a					)

								if src_parent == dst_parent:
															return False

								if self.ranks[dst_parent] >= self.ranks[src_parent]:
															self.set_counts[dst_parent] += self.set_counts[src_parent]
															snake_case_				=							0
															snake_case_				=							dst_parent
															if self.ranks[dst_parent] == self.ranks[src_parent]:
																						self.ranks[dst_parent] += 1
															snake_case_				=							self.set_counts[dst_parent]
								else:
															self.set_counts[src_parent] += self.set_counts[dst_parent]
															snake_case_				=							0
															snake_case_				=							src_parent
															snake_case_				=							self.set_counts[src_parent]

								snake_case_				=							max(self.max_set       ,			a					)
								return True






	def 							_UpperCamelCase			(       self       ,			a					)       ->    int:
								if self.parents[disj_set] == disj_set:
															return disj_set
								snake_case_				=							self.get_parent(self.parents[disj_set]					)
								return self.parents[disj_set]





