



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 (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			# Load configuration defined in the metadata file
			with open(SCREAMING_SNAKE_CASE_  ) as metadata_file:
						lowercase__					  =				json.load(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE_						,  **metadata["model_config"]  )

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

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

			lowercase__					  =				XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]  )

			# Add special tokens to the token vocabulary for downstream tasks
			lowercase__					  =				AddedToken("<ent>"						,  lstrip=SCREAMING_SNAKE_CASE_						,  rstrip=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				AddedToken("<ent2>"						,  lstrip=SCREAMING_SNAKE_CASE_						,  rstrip=SCREAMING_SNAKE_CASE_  )
			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(SCREAMING_SNAKE_CASE_  )
			with open(os.path.join(SCREAMING_SNAKE_CASE_						,  "tokenizer_config.json"  )						,  "r"  ) as f:
						lowercase__					  =				json.load(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				"MLukeTokenizer"
			with open(os.path.join(SCREAMING_SNAKE_CASE_						,  "tokenizer_config.json"  )						,  "w"  ) as f:
						json.dump(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

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

			lowercase__					  =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )

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

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

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

			lowercase__					  =				LukeForMaskedLM(config=SCREAMING_SNAKE_CASE_  ).eval()

			state_dict.pop("entity_predictions.decoder.weight"  )
			state_dict.pop("lm_head.decoder.weight"  )
			state_dict.pop("lm_head.decoder.bias"  )
			lowercase__					  =				OrderedDict()
			for key, value in state_dict.items():
						if not (key.startswith("lm_head"  ) or key.startswith("entity_predictions"  )):
									lowercase__					  =				state_dict[key]
						else:
									lowercase__					  =				state_dict[key]

			lowercase__  ,       lowercase__					  =				model.load_state_dict(SCREAMING_SNAKE_CASE_						,  strict=SCREAMING_SNAKE_CASE_  )

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

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

			lowercase__					  =				model(**SCREAMING_SNAKE_CASE_  )

			# Verify word hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						lowercase__					  =				torch.Size((1, 33, 768)  )
						lowercase__					  =				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]						,  SCREAMING_SNAKE_CASE_						,  atol=1e-4  ):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						lowercase__					  =				torch.Size((1, 1, 768)  )
						lowercase__					  =				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]						,  SCREAMING_SNAKE_CASE_						,  atol=1e-4  ):
						raise ValueError

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

			lowercase__					  =				model(**SCREAMING_SNAKE_CASE_  )

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

			lowercase__					  =				outputs.entity_logits[0][0].argmax().item()
			lowercase__					  =				[
			    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(SCREAMING_SNAKE_CASE_  )  )
			model.save_pretrained(SCREAMING_SNAKE_CASE_  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				["[MASK]", "[PAD]", "[UNK]"]

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

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









from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def __lowerCAmelCase (     ):
			lowercase__					  =				HfArgumentParser(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			lowercase__					  =				TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_  )
			try:
						lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						lowercase__					  =				"Arg --no_{0} is no longer used, please use --no-{0} instead."
						lowercase__					  =				" ".join(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[:-1]  )
						lowercase__					  =				""
						lowercase__					  =				eval(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[-1]  )
						lowercase__					  =				[]
						for arg in depreciated_args:
									# arg[2:] removes '--'
									if arg[2:] in TensorFlowBenchmark.deprecated_args:
												# arg[5:] removes '--no_'
												full_error_msg += arg_error_msg.format(arg[5:]  )
									else:
												wrong_args.append(SCREAMING_SNAKE_CASE_  )
						if len(SCREAMING_SNAKE_CASE_  ) > 0:
									lowercase__					  =				full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_  )
						raise ValueError(SCREAMING_SNAKE_CASE_  )
			benchmark.run()


if __name__ == "__main__":
							main()




