


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 			_UpperCamelCase				( lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__ ):
						# Load configuration defined in the metadata file
						with open(__A ) as metadata_file:
												__SCREAMING_SNAKE_CASE		:	Any						     =						json.load(__A )
						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						LukeConfig(use_entity_aware_attention=__A       ,			**metadata['''model_config'''] )

						# Load in the weights from the checkpoint_path
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						torch.load(__A       ,			map_location='''cpu''' )

						# Load the entity vocab file
						__SCREAMING_SNAKE_CASE		:	List[Any]						     =						load_entity_vocab(__A )

						__SCREAMING_SNAKE_CASE		:	int						     =						RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )

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

						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						LukeTokenizer.from_pretrained(__A )

						# Initialize the embeddings of the special tokens
						__SCREAMING_SNAKE_CASE		:	int						     =						state_dict['''embeddings.word_embeddings.weight''']
						__SCREAMING_SNAKE_CASE		:	Dict						     =						word_emb[tokenizer.convert_tokens_to_ids(['''@'''] )[0]].unsqueeze(0 )
						__SCREAMING_SNAKE_CASE		:	Tuple						     =						word_emb[tokenizer.convert_tokens_to_ids(['''#'''] )[0]].unsqueeze(0 )
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						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"]:
																		__SCREAMING_SNAKE_CASE		:	List[str]						     =						F'''encoder.layer.{layer_index}.attention.self.'''
																		__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						state_dict[prefix + matrix_name]
																		__SCREAMING_SNAKE_CASE		:	List[Any]						     =						state_dict[prefix + matrix_name]
																		__SCREAMING_SNAKE_CASE		:	List[str]						     =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						__SCREAMING_SNAKE_CASE		:	Tuple						     =						state_dict['''entity_embeddings.entity_embeddings.weight''']
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						entity_emb[entity_vocab['''[MASK]''']]

						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						LukeModel(config=__A ).eval()

						__SCREAMING_SNAKE_CASE		:	Tuple						     =						model.load_state_dict(__A       ,			strict=__A )
						if not (len(__A ) == 1 and missing_keys[0] == "embeddings.position_ids"):
												raise ValueError(F'''Missing keys {', '.join(__A )}. 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
						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						LukeTokenizer.from_pretrained(__A       ,			task='''entity_classification''' )

						__SCREAMING_SNAKE_CASE		:	Any						     =						(
						    '''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 .'''
						)
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						(39, 42)
						__SCREAMING_SNAKE_CASE		:	Dict						     =						tokenizer(__A       ,			entity_spans=[span]       ,			add_prefix_space=__A       ,			return_tensors='''pt''' )

						__SCREAMING_SNAKE_CASE		:	Dict						     =						model(**__A )

						# Verify word hidden states
						if model_size == "large":
												__SCREAMING_SNAKE_CASE		:	int						     =						torch.Size((1, 42, 1024) )
												__SCREAMING_SNAKE_CASE		:	Dict						     =						torch.tensor(
												    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
						else:  # base
												__SCREAMING_SNAKE_CASE		:	List[Any]						     =						torch.Size((1, 42, 768) )
												__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						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]       ,			__A       ,			atol=1e-4 ):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												__SCREAMING_SNAKE_CASE		:	str						     =						torch.Size((1, 1, 1024) )
												__SCREAMING_SNAKE_CASE		:	Tuple						     =						torch.tensor([[0.0466, -0.0106, -0.0179]] )
						else:  # base
												__SCREAMING_SNAKE_CASE		:	str						     =						torch.Size((1, 1, 768) )
												__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						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]       ,			__A       ,			atol=1e-4 ):
												raise ValueError

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


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	int						     =						{}
						with open(__A       ,			'''r'''       ,			encoding='''utf-8''' ) as f:
												for index, line in enumerate(__A ):
																		__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						line.rstrip().split('''\t''' )
																		__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						index

						return entity_vocab


if __name__ == "__main__":
						__lowerCAmelCase      :					Dict    							=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.'
						)
						__lowerCAmelCase      :					List[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,
						)








'''simple docstring'''







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCAmelCase									=   object()

# For specifying empty leaf dict `{}`
__lowerCAmelCase									=   object()





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Tuple   ):
				a_		:	List[Any]									=   tuple((re.compile(x + '''$'''   ) for x in qs)   )
				for i in range(len(__A   ) - len(__A   ) + 1   ):
								a_		:	Union[str, Any]									=   [x.match(__A   ) for x, y in zip(__A    ,		ks[i:]   )]
								if matches and all(__A   ):
												return True
				return False





def 		_UpperCAmelCase (				__A				: List[str]   ):
				def replace(__A				: int    ,		__A				: Union[str, Any]   ):
								for rule, replacement in rules:
												if _match(__A    ,		__A   ):
																return replacement
								return val

				return replace





def 		_UpperCAmelCase (				):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('''mp'''    ,		__A   )),
				    (("transformer", "wte", "embedding"), P('''mp'''    ,		__A   )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__A    ,		'''mp'''   )),
				    (("attention", "out_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__A    ,		'''mp'''   )),
				    (("mlp", "c_fc", "bias"), P('''mp'''   )),
				    (("mlp", "c_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				a_		:	Tuple									=   _get_partition_rules()
				a_		:	Tuple									=   _replacement_rules(__A   )
				a_		:	Optional[Any]									=   {k: _unmatched for k in flatten_dict(__A   )}
				a_		:	Optional[Any]									=   {k: replace(__A    ,		__A   ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__A   )   )




