


'''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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):






		with open(UpperCamelCase__      ) as metadata_file:
				_UpperCAmelCase						:				str	    =					json.load(UpperCamelCase__      )
		_UpperCAmelCase						:				int	    =					LukeConfig(use_entity_aware_attention=UpperCamelCase__					,       **metadata["model_config"]      )

		# Load in the weights from the checkpoint_path
		_UpperCAmelCase						:				Optional[int]	    =					torch.load(UpperCamelCase__					,       map_location="cpu"      )["module"]

		# Load the entity vocab file
		_UpperCAmelCase						:				Optional[int]	    =					load_original_entity_vocab(UpperCamelCase__      )
		# add an entry for [MASK2]
		_UpperCAmelCase						:				Optional[Any]	    =					max(entity_vocab.values()      ) + 1
		config.entity_vocab_size += 1

		_UpperCAmelCase						:				Tuple	    =					XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

		# Add special tokens to the token vocabulary for downstream tasks
		_UpperCAmelCase						:				str	    =					AddedToken("<ent>"					,       lstrip=UpperCamelCase__					,       rstrip=UpperCamelCase__      )
		_UpperCAmelCase						:				Tuple	    =					AddedToken("<ent2>"					,       lstrip=UpperCamelCase__					,       rstrip=UpperCamelCase__      )
		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(UpperCamelCase__      )
		with open(os.path.join(UpperCamelCase__					,       "tokenizer_config.json"      )					,       "r"      ) as f:
				_UpperCAmelCase						:				List[Any]	    =					json.load(UpperCamelCase__      )
		_UpperCAmelCase						:				Optional[Any]	    =					"MLukeTokenizer"
		with open(os.path.join(UpperCamelCase__					,       "tokenizer_config.json"      )					,       "w"      ) as f:
				json.dump(UpperCamelCase__					,       UpperCamelCase__      )

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

		_UpperCAmelCase						:				str	    =					MLukeTokenizer.from_pretrained(UpperCamelCase__      )

		# Initialize the embeddings of the special tokens
		_UpperCAmelCase						:				List[Any]	    =					tokenizer.convert_tokens_to_ids(["@"]      )[0]
		_UpperCAmelCase						:				int	    =					tokenizer.convert_tokens_to_ids(["#"]      )[0]

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

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

		_UpperCAmelCase						:				Union[str, Any]	    =					LukeForMaskedLM(config=UpperCamelCase__      ).eval()

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

		_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					model.load_state_dict(UpperCamelCase__					,       strict=UpperCamelCase__      )

		if set(UpperCamelCase__      ) != {"luke.embeddings.position_ids"}:
				raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""      )
		if set(UpperCamelCase__      ) != {
		    "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						:				Any	    =					MLukeTokenizer.from_pretrained(UpperCamelCase__					,       task="entity_classification"      )

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

		_UpperCAmelCase						:				List[Any]	    =					model(**UpperCamelCase__      )

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

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

		# Verify masked word/entity prediction
		_UpperCAmelCase						:				Tuple	    =					MLukeTokenizer.from_pretrained(UpperCamelCase__      )
		_UpperCAmelCase						:				Tuple	    =					"Tokyo is the capital of <mask>."
		_UpperCAmelCase						:				Union[str, Any]	    =					(24, 30)
		_UpperCAmelCase						:				Union[str, Any]	    =					tokenizer(UpperCamelCase__					,       entity_spans=[span]					,       return_tensors="pt"      )

		_UpperCAmelCase						:				Any	    =					model(**UpperCamelCase__      )

		_UpperCAmelCase						:				Optional[int]	    =					encoding["input_ids"][0].tolist()
		_UpperCAmelCase						:				List[str]	    =					input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"      )      )
		_UpperCAmelCase						:				Dict	    =					outputs.logits[0][mask_position_id].argmax(dim=-1      )
		assert "Japan" == tokenizer.decode(UpperCamelCase__      )

		_UpperCAmelCase						:				Any	    =					outputs.entity_logits[0][0].argmax().item()
		_UpperCAmelCase						:				Dict	    =					[
		    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(UpperCamelCase__      )      )
		model.save_pretrained(UpperCamelCase__      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					["[MASK]", "[PAD]", "[UNK]"]

		_UpperCAmelCase						:				Tuple	    =					[json.loads(UpperCamelCase__      ) for line in open(UpperCamelCase__      )]

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


if __name__ == "__main__":
						lowerCamelCase__					=   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__					=   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 __future__ import annotations

import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    np.shape(UpperCamelCase__      )
							if rows != columns:
														snake_case_							=    (
														    '\'table\' has to be of square shaped array but got a '
														    F'''{rows}x{columns} array:\n{table}'''
														)
														raise ValueError(UpperCamelCase__      )

							snake_case_							=    np.zeros((rows, columns)      )
							snake_case_							=    np.zeros((rows, columns)      )
							for i in range(UpperCamelCase__      ):
														for j in range(UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					if upper[j][j] == 0:
																												raise ArithmeticError('No LU decomposition exists'      )
																					snake_case_							=    (table[i][j] - total) / upper[j][j]
														snake_case_							=    1
														for j in range(UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					snake_case_							=    table[i][j] - total
							return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()




