





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_	(    __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__   )				->			str:




				"""simple docstring"""






				with open(_UpperCamelCase   ) as metadata_file:
								snake_case   :					Dict    						=				json.load(_UpperCamelCase   )
				snake_case   :					Optional[int]    						=				LukeConfig(use_entity_aware_attention=_UpperCamelCase		,     **metadata['''model_config''']   )

				# Load in the weights from the checkpoint_path
				snake_case   :					Tuple    						=				torch.load(_UpperCamelCase		,     map_location='''cpu'''   )['''module''']

				# Load the entity vocab file
				snake_case   :					int    						=				load_original_entity_vocab(_UpperCamelCase   )
				# add an entry for [MASK2]
				snake_case   :					List[Any]    						=				max(entity_vocab.values()   ) + 1
				config.entity_vocab_size += 1

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

				# Add special tokens to the token vocabulary for downstream tasks
				snake_case   :					Optional[Any]    						=				AddedToken('''<ent>'''		,     lstrip=_UpperCamelCase		,     rstrip=_UpperCamelCase   )
				snake_case   :					Optional[int]    						=				AddedToken('''<ent2>'''		,     lstrip=_UpperCamelCase		,     rstrip=_UpperCamelCase   )
				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(_UpperCamelCase   )
				with open(os.path.join(_UpperCamelCase		,     '''tokenizer_config.json'''   )		,     '''r'''   ) as f:
								snake_case   :					str    						=				json.load(_UpperCamelCase   )
				snake_case   :					Union[str, Any]    						=				'''MLukeTokenizer'''
				with open(os.path.join(_UpperCamelCase		,     '''tokenizer_config.json'''   )		,     '''w'''   ) as f:
								json.dump(_UpperCamelCase		,     _UpperCamelCase   )

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

				snake_case   :					str    						=				MLukeTokenizer.from_pretrained(_UpperCamelCase   )

				# Initialize the embeddings of the special tokens
				snake_case   :					int    						=				tokenizer.convert_tokens_to_ids(['''@''']   )[0]
				snake_case   :					Optional[int]    						=				tokenizer.convert_tokens_to_ids(['''#''']   )[0]

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

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

				snake_case   :					Tuple    						=				LukeForMaskedLM(config=_UpperCamelCase   ).eval()

				state_dict.pop('''entity_predictions.decoder.weight'''   )
				state_dict.pop('''lm_head.decoder.weight'''   )
				state_dict.pop('''lm_head.decoder.bias'''   )
				snake_case   :					List[Any]    						=				OrderedDict()
				for key, value in state_dict.items():
								if not (key.startswith('''lm_head'''   ) or key.startswith('''entity_predictions'''   )):
												snake_case   :					Optional[int]    						=				state_dict[key]
								else:
												snake_case   :					Dict    						=				state_dict[key]

				snake_case						,						snake_case   :					Tuple    						=				model.load_state_dict(_UpperCamelCase		,     strict=_UpperCamelCase   )

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

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

				snake_case   :					Tuple    						=				model(**_UpperCamelCase   )

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								snake_case   :					Tuple    						=				torch.Size((1, 33, 768)   )
								snake_case   :					Any    						=				torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]   )

				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]		,     _UpperCamelCase		,     atol=1e-4   ):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								snake_case   :					Optional[Any]    						=				torch.Size((1, 1, 768)   )
								snake_case   :					List[str]    						=				torch.tensor([[-0.1482, 0.0609, 0.0322]]   )

				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]		,     _UpperCamelCase		,     atol=1e-4   ):
								raise ValueError

				# Verify masked word/entity prediction
				snake_case   :					str    						=				MLukeTokenizer.from_pretrained(_UpperCamelCase   )
				snake_case   :					List[str]    						=				'''Tokyo is the capital of <mask>.'''
				snake_case   :					List[Any]    						=				(24, 30)
				snake_case   :					Union[str, Any]    						=				tokenizer(_UpperCamelCase		,     entity_spans=[span]		,     return_tensors='''pt'''   )

				snake_case   :					Tuple    						=				model(**_UpperCamelCase   )

				snake_case   :					Tuple    						=				encoding['''input_ids'''][0].tolist()
				snake_case   :					Optional[int]    						=				input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''   )   )
				snake_case   :					Optional[Any]    						=				outputs.logits[0][mask_position_id].argmax(dim=-1   )
				assert "Japan" == tokenizer.decode(_UpperCamelCase   )

				snake_case   :					Dict    						=				outputs.entity_logits[0][0].argmax().item()
				snake_case   :					List[Any]    						=				[
				    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(_UpperCamelCase   )   )
				model.save_pretrained(_UpperCamelCase   )
def 							a_	(    __magic_name__   )				->			List[str]:




				"""simple docstring"""






				snake_case   :					str    						=				['''[MASK]''', '''[PAD]''', '''[UNK]''']

				snake_case   :					str    						=				[json.loads(_UpperCamelCase   ) for line in open(_UpperCamelCase   )]

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


if __name__ == "__main__":
			_a     :    Optional[int]         =						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     :    Dict         =						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 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = hex_num.strip()
			if not hex_num:
						raise ValueError('''No value was passed to the function'''	)

			__lowercase			       = hex_num[0] == '''-'''
			if is_negative:
						__lowercase			       = hex_num[1:]

			try:
						__lowercase			       = int(_UpperCamelCase							,      16	)
			except ValueError:
						raise ValueError('''Invalid value was passed to the function'''	)

			__lowercase			       = ''''''
			while int_num > 0:
						__lowercase			       = str(int_num % 2	) + bin_str
						int_num >>= 1

			return int(('''-''' + bin_str) if is_negative else bin_str	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


