




import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Any:
	# Load configuration defined in the metadata file
	with open(__lowerCamelCase       ) as metadata_file:
		a									=				json.load(__lowerCamelCase       )
	a									=				LukeConfig(use_entity_aware_attention=__lowerCamelCase				,     **metadata["""model_config"""]       )

	# Load in the weights from the checkpoint_path
	a									=				torch.load(__lowerCamelCase				,     map_location="""cpu"""       )

	# Load the entity vocab file
	a									=				load_entity_vocab(__lowerCamelCase       )

	a									=				RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]       )

	# Add special tokens to the token vocabulary for downstream tasks
	a									=				AddedToken("""<ent>"""				,     lstrip=__lowerCamelCase				,     rstrip=__lowerCamelCase       )
	a									=				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				,     LukeTokenizer.vocab_files_names["""entity_vocab_file"""]       )				,     """w"""       ) as f:
		json.dump(__lowerCamelCase				,     __lowerCamelCase       )

	a									=				LukeTokenizer.from_pretrained(__lowerCamelCase       )

	# Initialize the embeddings of the special tokens
	a									=				state_dict["""embeddings.word_embeddings.weight"""]
	a									=				word_emb[tokenizer.convert_tokens_to_ids(["""@"""]       )[0]].unsqueeze(0       )
	a									=				word_emb[tokenizer.convert_tokens_to_ids(["""#"""]       )[0]].unsqueeze(0       )
	a									=				torch.cat([word_emb, ent_emb, enta_emb]       )

	# 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"]:
			a									=				f'encoder.layer.{layer_index}.attention.self.'
			a									=				state_dict[prefix + matrix_name]
			a									=				state_dict[prefix + matrix_name]
			a									=				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
	a									=				state_dict["""entity_embeddings.entity_embeddings.weight"""]
	a									=				entity_emb[entity_vocab["""[MASK]"""]]

	a									=				LukeModel(config=__lowerCamelCase       ).eval()

	a							,					a									=				model.load_state_dict(__lowerCamelCase				,     strict=__lowerCamelCase       )
	if not (len(__lowerCamelCase       ) == 1 and missing_keys[0] == "embeddings.position_ids"):
		raise ValueError(f'Missing keys {", ".join(__lowerCamelCase       )}. Expected only missing embeddings.position_ids'       )
	if not (all(key.startswith("""entity_predictions"""       ) or key.startswith("""lm_head"""       ) for key in unexpected_keys       )):
		raise ValueError(
		    """Unexpected keys"""
		    f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"       ) or key.startswith("lm_head"       ))]       )}'       )

	# Check outputs
	a									=				LukeTokenizer.from_pretrained(__lowerCamelCase				,     task="""entity_classification"""       )

	a									=				(
	    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
	    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
	)
	a									=				(39, 42)
	a									=				tokenizer(__lowerCamelCase				,     entity_spans=[span]				,     add_prefix_space=__lowerCamelCase				,     return_tensors="""pt"""       )

	a									=				model(**__lowerCamelCase       )

	# Verify word hidden states
	if model_size == "large":
		a									=				torch.Size((1, 42, 1024)       )
		a									=				torch.tensor(
		    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]       )
	else:  # base
		a									=				torch.Size((1, 42, 768)       )
		a									=				torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]       )

	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":
		a									=				torch.Size((1, 1, 1024)       )
		a									=				torch.tensor([[0.0466, -0.0106, -0.0179]]       )
	else:  # base
		a									=				torch.Size((1, 1, 768)       )
		a									=				torch.tensor([[0.1457, 0.1044, 0.0174]]       )

	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

	# Finally, save our PyTorch model and tokenizer
	print("""Saving PyTorch model to {}""".format(__lowerCamelCase       )       )
	model.save_pretrained(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			str:
	a									=				{}
	with open(__lowerCamelCase				,     """r"""				,     encoding="""utf-8"""       ) as f:
		for index, line in enumerate(__lowerCamelCase       ):
			a							,					a									=				line.rstrip().split("""\t"""       )
			a									=				index

	return entity_vocab


if __name__ == "__main__":
		__UpperCamelCase   :			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   :			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,
		)







import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""       ):
			a									=				"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""       ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""       ) or tensor_key.endswith(""".self.LayerNorm.bias"""       ):
			continue

		a									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, Any]          = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint-repo",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCamelCase   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

