





'''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       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Union[str, Any]:
						# Load configuration defined in the metadata file
						with open(__lowerCAmelCase						) as metadata_file:
												_snake_case:   Union[str, Any]  					=		json.load(__lowerCAmelCase						)
						_snake_case:   Optional[Any]  					=		LukeConfig(use_entity_aware_attention=__lowerCAmelCase				,  **metadata['model_config']						)

						# Load in the weights from the checkpoint_path
						_snake_case:   List[Any]  					=		torch.load(__lowerCAmelCase				,  map_location='cpu'						)["""module"""]

						# Load the entity vocab file
						_snake_case:   Dict  					=		load_original_entity_vocab(__lowerCAmelCase						)
						# add an entry for [MASK2]
						_snake_case:   Union[str, Any]  					=		max(entity_vocab.values()						) + 1
						config.entity_vocab_size += 1

						_snake_case:   Dict  					=		XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

						# Add special tokens to the token vocabulary for downstream tasks
						_snake_case:   List[Any]  					=		AddedToken('<ent>'				,  lstrip=__lowerCAmelCase				,  rstrip=__lowerCAmelCase						)
						_snake_case:   List[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:
												_snake_case:   Union[str, Any]  					=		json.load(__lowerCAmelCase						)
						_snake_case:   Tuple  					=		"""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						)

						_snake_case:   int  					=		MLukeTokenizer.from_pretrained(__lowerCAmelCase						)

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

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

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

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

						_snake_case:   int  					=		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
						_snake_case:   Optional[Any]  					=		MLukeTokenizer.from_pretrained(__lowerCAmelCase				,  task='entity_classification'						)

						_snake_case:   Tuple  					=		"""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:   int  					=		tokenizer(__lowerCAmelCase				,  entity_spans=[span]				,  return_tensors='pt'						)

						_snake_case:   Optional[int]  					=		model(**__lowerCAmelCase						)

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

						_snake_case:   Union[str, Any]  					=		model(**__lowerCAmelCase						)

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

						_snake_case:   Tuple  					=		outputs.entity_logits[0][0].argmax().item()
						_snake_case:   List[str]  					=		[
						    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       lowercase_      (		lowercase__						)   ->str:
						_snake_case:   List[Any]  					=		["""[MASK]""", """[PAD]""", """[UNK]"""]

						_snake_case:   List[str]  					=		[json.loads(__lowerCAmelCase						) for line in open(__lowerCAmelCase						)]

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


if __name__ == "__main__":
		A   : str             =			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   : int             =			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'''






import torch





def       lowercase_      (		)   ->Dict:
						if torch.cuda.is_available():
												_snake_case:   Any  					=		torch.cuda.device_count()
						else:
												_snake_case:   Optional[int]  					=		0
						print(F'''Successfully ran on {num_gpus} GPUs'''						)


if __name__ == "__main__":
		main()





