
from collections.abc import Callable




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   List[str]      ,  lowercase     :   Callable | None = None     ):


				'''simple docstring'''


				_snake_case          =      []
				# Stores indexes of each item for supporting updates and deletion.
				_snake_case          =      {}
				# Stores current size of heap.
				_snake_case          =      0
				# Stores function used to evaluate the score of an item on which basis ordering
				# will be done.
				_snake_case          =      key or (lambda lowercase     : x)



	def 			A							(  self     :   List[str]      ,  lowercase     :   int     ):


				'''simple docstring'''


				return int((i - 1) / 2     ) if i > 0 else None



	def 			A							(  self     :   List[Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      int(2 * i + 1     )
				return left if 0 < left < self.size else None



	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      int(2 * i + 2     )
				return right if 0 < right < self.size else None



	def 			A							(  self     :   int      ,  lowercase     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case     , _snake_case          =      (
				    self.pos_map[self.arr[j][0]],
				    self.pos_map[self.arr[i][0]],
				)
				# Then swap the items in the list.
				_snake_case     , _snake_case          =      self.arr[j], self.arr[i]



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				return self.arr[i][1] < self.arr[j][1]



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      self._left(lowercase     )
				_snake_case          =      self._right(lowercase     )
				_snake_case          =      i

				if left is not None and not self._cmp(lowercase      ,  lowercase     ):
							_snake_case          =      left
				if right is not None and not self._cmp(lowercase      ,  lowercase     ):
							_snake_case          =      right

				return valid_parent



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      self._parent(lowercase     )
				while parent is not None and not self._cmp(lowercase      ,  lowercase     ):
							self._swap(lowercase      ,  lowercase     )
							_snake_case     , _snake_case          =      parent, self._parent(lowercase     )



	def 			A							(  self     :   List[str]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      self._get_valid_parent(lowercase     )
				while valid_parent != index:
							self._swap(lowercase      ,  lowercase     )
							_snake_case     , _snake_case          =      valid_parent, self._get_valid_parent(lowercase     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				if item not in self.pos_map:
							return
				_snake_case          =      self.pos_map[item]
				_snake_case          =      [item, self.key(lowercase     )]
				# Make sure heap is right in both up and down direction.
				# Ideally only one of them will make any change.
				self._heapify_up(lowercase     )
				self._heapify_down(lowercase     )



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				if item not in self.pos_map:
							return
				_snake_case          =      self.pos_map[item]
				del self.pos_map[item]
				_snake_case          =      self.arr[self.size - 1]
				_snake_case          =      index
				self.size -= 1
				# Make sure heap is right in both up and down direction. Ideally only one
				# of them will make any change- so no performance loss in calling both.
				if self.size > index:
							self._heapify_up(lowercase     )
							self._heapify_down(lowercase     )



	def 			A							(  self     :   int      ,  lowercase     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      len(self.arr     )
				if arr_len == self.size:
							self.arr.append([item, self.key(lowercase     )]     )
				else:
							_snake_case          =      [item, self.key(lowercase     )]
				_snake_case          =      self.size
				self.size += 1
				self._heapify_up(self.size - 1     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return self.arr[0] if self.size else None





	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.get_top()
				if top_item_tuple:
							self.delete_item(top_item_tuple[0]     )
				return top_item_tuple






def 	a_		(	)			->  None:
			pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()

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_		(	__lowercase       :     Optional[Any]	, __lowercase       :     Optional[int]	, __lowercase       :     str	, __lowercase       :     Optional[int]	, __lowercase       :     str			)			->  Union[str, Any]:
			# Load configuration defined in the metadata file
			with open(__lowercase			) as metadata_file:
						_snake_case          =      json.load(__lowercase			)
			_snake_case          =      LukeConfig(use_entity_aware_attention=__lowercase	, **metadata['model_config']			)

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

			# Load the entity vocab file
			_snake_case          =      load_original_entity_vocab(__lowercase			)
			# add an entry for [MASK2]
			_snake_case          =      max(entity_vocab.values()			) + 1
			config.entity_vocab_size += 1

			_snake_case          =      XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']			)

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

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

			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase			)

			# Initialize the embeddings of the special tokens
			_snake_case          =      tokenizer.convert_tokens_to_ids(['@']			)[0]
			_snake_case          =      tokenizer.convert_tokens_to_ids(['#']			)[0]

			_snake_case          =      state_dict['embeddings.word_embeddings.weight']
			_snake_case          =      word_emb[ent_init_index].unsqueeze(0			)
			_snake_case          =      word_emb[enta_init_index].unsqueeze(0			)
			_snake_case          =      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          =      state_dict[bias_name]
						_snake_case          =      decoder_bias[ent_init_index].unsqueeze(0			)
						_snake_case          =      decoder_bias[enta_init_index].unsqueeze(0			)
						_snake_case          =      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          =      f'''encoder.layer.{layer_index}.attention.self.'''
									_snake_case          =      state_dict[prefix + matrix_name]
									_snake_case          =      state_dict[prefix + matrix_name]
									_snake_case          =      state_dict[prefix + matrix_name]

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

			_snake_case          =      LukeForMaskedLM(config=__lowercase			).eval()

			state_dict.pop('entity_predictions.decoder.weight'			)
			state_dict.pop('lm_head.decoder.weight'			)
			state_dict.pop('lm_head.decoder.bias'			)
			_snake_case          =      OrderedDict()
			for key, value in state_dict.items():
						if not (key.startswith('lm_head'			) or key.startswith('entity_predictions'			)):
									_snake_case          =      state_dict[key]
						else:
									_snake_case          =      state_dict[key]

			_snake_case     , _snake_case          =      model.load_state_dict(__lowercase	, strict=__lowercase			)

			if set(__lowercase			) != {"luke.embeddings.position_ids"}:
						raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''			)
			if set(__lowercase			) != {
			    "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          =      MLukeTokenizer.from_pretrained(__lowercase	, task='entity_classification'			)

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

			_snake_case          =      model(**__lowercase			)

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

			# Verify entity hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						_snake_case          =      torch.Size((1, 1, 768)			)
						_snake_case          =      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]	, __lowercase	, atol=1E-4			):
						raise ValueError

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

			_snake_case          =      model(**__lowercase			)

			_snake_case          =      encoding['input_ids'][0].tolist()
			_snake_case          =      input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'			)			)
			_snake_case          =      outputs.logits[0][mask_position_id].argmax(dim=-1			)
			assert "Japan" == tokenizer.decode(__lowercase			)

			_snake_case          =      outputs.entity_logits[0][0].argmax().item()
			_snake_case          =      [
			    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(__lowercase			)			)
			model.save_pretrained(__lowercase			)






def 	a_		(	__lowercase       :     int			)			->  int:
			_snake_case          =      ['[MASK]', '[PAD]', '[UNK]']

			_snake_case          =      [json.loads(__lowercase			) for line in open(__lowercase			)]

			_snake_case          =      {}
			for entry in data:
						_snake_case          =      entry['id']
						for entity_name, language in entry["entities"]:
									if entity_name in SPECIAL_TOKENS:
												_snake_case          =      entity_id
												break
									_snake_case          =      f'''{language}:{entity_name}'''
									_snake_case          =      entity_id
			return new_mapping


if __name__ == "__main__":
			_lowerCamelCase	:    Union[str, 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.'''
			)
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)