






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    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






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

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

				# Load the entity vocab file
				lowercase__     				=    load_original_entity_vocab(lowerCamelCase_       )
				# 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=lowerCamelCase_	,     rstrip=lowerCamelCase_       )
				lowercase__     				=    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_	,     '''tokenizer_config.json'''       )	,     '''r'''       ) as f:
								lowercase__     				=    json.load(lowerCamelCase_       )
				lowercase__     				=    '''MLukeTokenizer'''
				with open(os.path.join(lowerCamelCase_	,     '''tokenizer_config.json'''       )	,     '''w'''       ) as f:
								json.dump(lowerCamelCase_	,     lowerCamelCase_       )

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

				lowercase__     				=    MLukeTokenizer.from_pretrained(lowerCamelCase_       )

				# 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=lowerCamelCase_       ).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(lowerCamelCase_	,     strict=lowerCamelCase_       )

				if set(lowerCamelCase_       ) != {"luke.embeddings.position_ids"}:
								raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""       )
				if set(lowerCamelCase_       ) != {
				    "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(lowerCamelCase_	,     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(lowerCamelCase_	,     entity_spans=[span]	,     return_tensors='''pt'''       )

				lowercase__     				=    model(**lowerCamelCase_       )

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								lowercase__     				=    torch.Size((1, 33, 768)       )
								lowercase__     				=    torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]       )

				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":
								raise NotImplementedError
				else:  # base
								lowercase__     				=    torch.Size((1, 1, 768)       )
								lowercase__     				=    torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]       )

				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

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

				lowercase__     				=    model(**lowerCamelCase_       )

				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(lowerCamelCase_       )

				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(lowerCamelCase_       )       )
				model.save_pretrained(lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    ['''[MASK]''', '''[PAD]''', '''[UNK]''']

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

				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__":
							A__			:    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.'
							)
							A__			:    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,
							)












from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= None
				lowercase__       							= None






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    Node(1       )
				lowercase__     				=    Node(2       )
				lowercase__     				=    Node(3       )
				lowercase__     				=    Node(4       )
				lowercase__     				=    Node(5       )
				return tree






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return [root.data, *preorder(root.left       ), *preorder(root.right       )] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return postorder(root.left       ) + postorder(root.right       ) + [root.data] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return [*inorder(root.left       ), root.data, *inorder(root.right       )] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return (max(height(root.left       )	,     height(root.right       )       ) + 1) if root else 0






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				if root is None:
								return output

				lowercase__     				=    deque([root]       )

				while process_queue:
								lowercase__     				=    process_queue.popleft()
								output.append(node.data       )

								if node.left:
												process_queue.append(node.left       )
								if node.right:
												process_queue.append(node.right       )
				return output






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				def populate_output(lowerCamelCase_	,     lowerCamelCase_       ) -> None:
								if not root:
												return
								if level == 1:
												output.append(root.data       )
								elif level > 1:
												populate_output(root.left	,     level - 1       )
												populate_output(root.right	,     level - 1       )

				populate_output(lowerCamelCase_	,     lowerCamelCase_       )
				return output






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				def populate_output(lowerCamelCase_	,     lowerCamelCase_       ) -> None:
								if root is None:
												return
								if level == 1:
												output.append(root.data       )
								elif level > 1:
												populate_output(root.right	,     level - 1       )
												populate_output(root.left	,     level - 1       )

				populate_output(lowerCamelCase_	,     lowerCamelCase_       )
				return output






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if root is None:
								return []

				lowercase__     				=    []

				lowercase__     				=    0
				lowercase__     				=    height(lowerCamelCase_       )

				for h in range(1	,     height_tree + 1       ):
								if not flag:
												output.append(get_nodes_from_left_to_right(lowerCamelCase_	,     lowerCamelCase_       )       )
												lowercase__     				=    1
								else:
												output.append(get_nodes_from_right_to_left(lowerCamelCase_	,     lowerCamelCase_       )       )
												lowercase__     				=    0

				return output






def 						a    (		):  # Main function for testing.






				'''simple docstring'''
				lowercase__     				=    make_tree()
				print(F"""In-order Traversal: {inorder(lowerCamelCase_       )}"""       )
				print(F"""Pre-order Traversal: {preorder(lowerCamelCase_       )}"""       )
				print(F"""Post-order Traversal: {postorder(lowerCamelCase_       )}"""	,     '''\n'''       )

				print(F"""Height of Tree: {height(lowerCamelCase_       )}"""	,     '''\n'''       )

				print('''Complete Level Order Traversal: '''       )
				print(level_order(lowerCamelCase_       )	,     '''\n'''       )

				print('''Level-wise order Traversal: '''       )

				for level in range(1	,     height(lowerCamelCase_       ) + 1       ):
								print(F"""Level {level}:"""	,     get_nodes_from_left_to_right(lowerCamelCase_	,     level=lowerCamelCase_       )       )

				print('''\nZigZag order Traversal: '''       )
				print(zigzag(lowerCamelCase_       )       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




