





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

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

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

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

	# Add special tokens to the token vocabulary for downstream tasks
	UpperCAmelCase_								=     AddedToken("""<ent>"""				,      lstrip=A				,      rstrip=A   )
	UpperCAmelCase_								=     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:
		UpperCAmelCase_								=     json.load(A   )
	UpperCAmelCase_								=     """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   )

	UpperCAmelCase_								=     MLukeTokenizer.from_pretrained(A   )

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

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

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

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

	UpperCAmelCase_						,	UpperCAmelCase_								=     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
	UpperCAmelCase_								=     MLukeTokenizer.from_pretrained(A				,      task="""entity_classification"""   )

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

	UpperCAmelCase_								=     model(**A   )

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		UpperCAmelCase_								=     torch.Size((1, 33, 768)   )
		UpperCAmelCase_								=     torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]   )

	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
		UpperCAmelCase_								=     torch.Size((1, 1, 768)   )
		UpperCAmelCase_								=     torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]   )

	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
	UpperCAmelCase_								=     MLukeTokenizer.from_pretrained(A   )
	UpperCAmelCase_								=     """Tokyo is the capital of <mask>."""
	UpperCAmelCase_								=     (24, 30)
	UpperCAmelCase_								=     tokenizer(A				,      entity_spans=[span]				,      return_tensors="""pt"""   )

	UpperCAmelCase_								=     model(**A   )

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

	UpperCAmelCase_								=     outputs.entity_logits[0][0].argmax().item()
	UpperCAmelCase_								=     [
	    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    A_(			A   ):
	UpperCAmelCase_								=     ["""[MASK]""", """[PAD]""", """[UNK]"""]

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

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


if __name__ == "__main__":
				UpperCamelCase__   : Optional[Any]					  = 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."""
				)
				UpperCamelCase__   : Optional[Any]					  = 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 torch import nn







class   _UpperCamelCase  (       nn.Module  ):







					'''simple docstring'''

					def __init__(						self    :  Dict ,					__lowercase    :  List[str] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     class_size
						UpperCAmelCase_								=     embed_size
						# self.mlp1 = nn.Linear(embed_size, embed_size)
						# self.mlp2 = (nn.Linear(embed_size, class_size))
						UpperCAmelCase_								=     nn.Linear(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.mlp(__lowercase		)
						return logits


