
'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		with open(SCREAMING_SNAKE_CASE__			) as metadata_file:
				snake_case  :       str     			=						json.load(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Any     			=						LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ ,       **metadata['''model_config''']			)

		# Load in the weights from the checkpoint_path
		snake_case  :       int     			=						torch.load(SCREAMING_SNAKE_CASE__ ,       map_location='''cpu'''			)['''module''']

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

		snake_case  :       Optional[int]     			=						XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']			)

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

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

		snake_case  :       int     			=						MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__			)

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

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

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

		snake_case  :       Union[str, Any]     			=						LukeForMaskedLM(config=SCREAMING_SNAKE_CASE__			).eval()

		state_dict.pop('''entity_predictions.decoder.weight'''			)
		state_dict.pop('''lm_head.decoder.weight'''			)
		state_dict.pop('''lm_head.decoder.bias'''			)
		snake_case  :       Union[str, Any]     			=						OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith('''lm_head'''			) or key.startswith('''entity_predictions'''			)):
						snake_case  :       Optional[int]     			=						state_dict[key]
				else:
						snake_case  :       Tuple     			=						state_dict[key]

		snake_case			,snake_case  :       str     			=						model.load_state_dict(SCREAMING_SNAKE_CASE__ ,       strict=SCREAMING_SNAKE_CASE__			)

		if set(SCREAMING_SNAKE_CASE__			) != {"luke.embeddings.position_ids"}:
				raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'			)
		if set(SCREAMING_SNAKE_CASE__			) != {
		    "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
		snake_case  :       List[Any]     			=						MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ,       task='''entity_classification'''			)

		snake_case  :       Tuple     			=						'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
		snake_case  :       Dict     			=						(0, 9)
		snake_case  :       Optional[int]     			=						tokenizer(SCREAMING_SNAKE_CASE__ ,       entity_spans=[span] ,       return_tensors='''pt'''			)

		snake_case  :       Tuple     			=						model(**SCREAMING_SNAKE_CASE__			)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				snake_case  :       Optional[Any]     			=						torch.Size((1, 33, 768)			)
				snake_case  :       Tuple     			=						torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]			)

		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] ,       SCREAMING_SNAKE_CASE__ ,       atol=1E-4			):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				snake_case  :       int     			=						torch.Size((1, 1, 768)			)
				snake_case  :       int     			=						torch.tensor([[-0.1482, 0.0609, 0.0322]]			)

		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] ,       SCREAMING_SNAKE_CASE__ ,       atol=1E-4			):
				raise ValueError

		# Verify masked word/entity prediction
		snake_case  :       Optional[Any]     			=						MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__			)
		snake_case  :       List[str]     			=						'''Tokyo is the capital of <mask>.'''
		snake_case  :       Union[str, Any]     			=						(24, 30)
		snake_case  :       str     			=						tokenizer(SCREAMING_SNAKE_CASE__ ,       entity_spans=[span] ,       return_tensors='''pt'''			)

		snake_case  :       List[Any]     			=						model(**SCREAMING_SNAKE_CASE__			)

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

		snake_case  :       List[Any]     			=						outputs.entity_logits[0][0].argmax().item()
		snake_case  :       List[Any]     			=						[
		    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(SCREAMING_SNAKE_CASE__			)			)
		model.save_pretrained(SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       int     			=						['''[MASK]''', '''[PAD]''', '''[UNK]''']

		snake_case  :       str     			=						[json.loads(SCREAMING_SNAKE_CASE__			) for line in open(SCREAMING_SNAKE_CASE__			)]

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


if __name__ == "__main__":
						lowercase__						       =      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."
						)
						lowercase__						       =      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'''
from __future__ import annotations

import queue






class 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			int  ,	UpperCamelCase__				:			Optional[int]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						data
				snake_case  :       Tuple     			=						None
				snake_case  :       int     			=						None







def    _UpperCamelCase						(       )  ->							TreeNode:


		'''simple docstring'''


		print('''\n********Press N to stop entering at any point of time********\n'''			)
		snake_case  :       List[Any]     			=						input('''Enter the value of the root node: '''			).strip().lower()
		snake_case  :       queue.Queue     			=						queue.Queue()
		snake_case  :       List[Any]     			=						TreeNode(int(SCREAMING_SNAKE_CASE__			)			)
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       List[str]     			=						q.get()
				snake_case  :       List[str]     			=						F'Enter the left node of {node_found.data}: '
				snake_case  :       List[Any]     			=						input(SCREAMING_SNAKE_CASE__			).strip().lower() or '''n'''
				if check == "n":
						return tree_node
				snake_case  :       Any     			=						TreeNode(int(SCREAMING_SNAKE_CASE__			)			)
				snake_case  :       List[str]     			=						left_node
				q.put(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Union[str, Any]     			=						F'Enter the right node of {node_found.data}: '
				snake_case  :       Optional[Any]     			=						input(SCREAMING_SNAKE_CASE__			).strip().lower() or '''n'''
				if check == "n":
						return tree_node
				snake_case  :       Any     			=						TreeNode(int(SCREAMING_SNAKE_CASE__			)			)
				snake_case  :       Union[str, Any]     			=						right_node
				q.put(SCREAMING_SNAKE_CASE__			)
		raise




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		print(node.data ,       end=''','''			)
		pre_order(node.left			)
		pre_order(node.right			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		in_order(node.left			)
		print(node.data ,       end=''','''			)
		in_order(node.right			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		post_order(node.left			)
		post_order(node.right			)
		print(node.data ,       end=''','''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       queue.Queue     			=						queue.Queue()
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       Any     			=						q.get()
				print(node_dequeued.data ,       end=''','''			)
				if node_dequeued.left:
						q.put(node_dequeued.left			)
				if node_dequeued.right:
						q.put(node_dequeued.right			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       queue.Queue     			=						queue.Queue()
		q.put(SCREAMING_SNAKE_CASE__			)
		while not q.empty():
				snake_case  :       str     			=						[]
				while not q.empty():
						snake_case  :       int     			=						q.get()
						print(node_dequeued.data ,       end=''','''			)
						if node_dequeued.left:
								list_.append(node_dequeued.left			)
						if node_dequeued.right:
								list_.append(node_dequeued.right			)
				print()
				for node in list_:
						q.put(SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       list[TreeNode]     			=						[]
		snake_case  :       Union[str, Any]     			=						node
		while n or stack:
				while n:  # start from root node, find its left child
						print(n.data ,       end=''','''			)
						stack.append(SCREAMING_SNAKE_CASE__			)
						snake_case  :       Dict     			=						n.left
				# end of while means current node doesn't have left child
				snake_case  :       List[Any]     			=						stack.pop()
				# start to traverse its right child
				snake_case  :       Any     			=						n.right




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case  :       list[TreeNode]     			=						[]
		snake_case  :       Dict     			=						node
		while n or stack:
				while n:
						stack.append(SCREAMING_SNAKE_CASE__			)
						snake_case  :       List[str]     			=						n.left
				snake_case  :       Union[str, Any]     			=						stack.pop()
				print(n.data ,       end=''','''			)
				snake_case  :       int     			=						n.right




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		if not isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			) or not node:
				return
		snake_case			,snake_case  :       Dict     			=						[], []
		snake_case  :       Optional[int]     			=						node
		stacka.append(SCREAMING_SNAKE_CASE__			)
		while stacka:  # to find the reversed order of post order, store it in stack2
				snake_case  :       Union[str, Any]     			=						stacka.pop()
				if n.left:
						stacka.append(n.left			)
				if n.right:
						stacka.append(n.right			)
				stacka.append(SCREAMING_SNAKE_CASE__			)
		while stacka:  # pop up from stack2 will be the post order
				print(stacka.pop().data ,       end=''','''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "" ,       SCREAMING_SNAKE_CASE__=50 ,       SCREAMING_SNAKE_CASE__="*"			)  ->							str:


		'''simple docstring'''


		if not s:
				return "\n" + width * char
		snake_case			,snake_case  :       Any     			=						divmod(width - len(SCREAMING_SNAKE_CASE__			) - 2 ,       2			)
		return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(prompt("Binary Tree Traversals"))

						lowercase__						       =      build_tree()
						print(prompt("Pre Order Traversal"))
						pre_order(node)
						print(prompt() + "\n")

						print(prompt("In Order Traversal"))
						in_order(node)
						print(prompt() + "\n")

						print(prompt("Post Order Traversal"))
						post_order(node)
						print(prompt() + "\n")

						print(prompt("Level Order Traversal"))
						level_order(node)
						print(prompt() + "\n")

						print(prompt("Actual Level Order Traversal"))
						level_order_actual(node)
						print("*" * 5_0 + "\n")

						print(prompt("Pre Order Traversal - Iteration Version"))
						pre_order_iter(node)
						print(prompt() + "\n")

						print(prompt("In Order Traversal - Iteration Version"))
						in_order_iter(node)
						print(prompt() + "\n")

						print(prompt("Post Order Traversal - Iteration Version"))
						post_order_iter(node)
						print(prompt())


