




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       ) ->			str:
	# Load configuration defined in the metadata file
	with open(__lowerCamelCase       ) as metadata_file:
		a									=				json.load(__lowerCamelCase       )
	a									=				LukeConfig(use_entity_aware_attention=__lowerCamelCase				,     **metadata["""model_config"""]       )

	# Load in the weights from the checkpoint_path
	a									=				torch.load(__lowerCamelCase				,     map_location="""cpu"""       )["""module"""]

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

	a									=				XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]       )

	# Add special tokens to the token vocabulary for downstream tasks
	a									=				AddedToken("""<ent>"""				,     lstrip=__lowerCamelCase				,     rstrip=__lowerCamelCase       )
	a									=				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:
		a									=				json.load(__lowerCamelCase       )
	a									=				"""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       )

	a									=				MLukeTokenizer.from_pretrained(__lowerCamelCase       )

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

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

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

	a									=				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"""       )
	a									=				OrderedDict()
	for key, value in state_dict.items():
		if not (key.startswith("""lm_head"""       ) or key.startswith("""entity_predictions"""       )):
			a									=				state_dict[key]
		else:
			a									=				state_dict[key]

	a							,					a									=				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
	a									=				MLukeTokenizer.from_pretrained(__lowerCamelCase				,     task="""entity_classification"""       )

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

	a									=				model(**__lowerCamelCase       )

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

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

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

	a									=				model(**__lowerCamelCase       )

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

	a									=				outputs.entity_logits[0][0].argmax().item()
	a									=				[
	    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       ) ->			Any:
	a									=				["""[MASK]""", """[PAD]""", """[UNK]"""]

	a									=				[json.loads(__lowerCamelCase       ) for line in open(__lowerCamelCase       )]

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


if __name__ == "__main__":
		__UpperCamelCase   :			List[str]          = 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."
		)
		__UpperCamelCase   :			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,
		)







from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__UpperCamelCase   :			Any          = TypeVar("KT")
__UpperCamelCase   :			List[Any]          = TypeVar("VT")

class 						__lowerCAmelCase      (      Generic[KT, VT]					):
							def __init__(		self		:Union[str, Any]						, __magic_name__		:KT | str = "root"						, __magic_name__		:VT | None = None							):







								'''simple docstring'''







								a									=				key
								a									=				value
								a									=				[]
							def __repr__(		self		:List[Any]							):







								'''simple docstring'''







								return F'Node({self.key}: {self.value})'





							@property
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								return len(self.forward							)







class 						__lowerCAmelCase      (      Generic[KT, VT]					):
							def __init__(		self		:Optional[Any]						, __magic_name__		:float = 0.5						, __magic_name__		:int = 16							):







								'''simple docstring'''







								a									=				Node[KT, VT]()
								a									=				0
								a									=				p
								a									=				max_level
							def __str__(		self		:Tuple							):







								'''simple docstring'''







								a									=				list(self							)

								if len(__magic_name__							) == 0:
									return F'SkipList(level={self.level})'

								a									=				max((len(str(__magic_name__							)							) for item in items)						, default=4							)
								a									=				max(__magic_name__						, 4							) + 4

								a									=				self.head
								a									=				[]

								a									=				node.forward.copy()
								lines.append(F'[{node.key}]'.ljust(__magic_name__						, """-"""							) + """* """ * len(__magic_name__							)							)
								lines.append(""" """ * label_size + """| """ * len(__magic_name__							)							)

								while len(node.forward							) != 0:
									a									=				node.forward[0]

									lines.append(
									    F'[{node.key}]'.ljust(__magic_name__						, """-"""							)
									    + """ """.join(str(n.key							) if n.key == node.key else """|""" for n in forwards							)							)
									lines.append(""" """ * label_size + """| """ * len(__magic_name__							)							)
									a									=				node.forward

								lines.append("""None""".ljust(__magic_name__							) + """* """ * len(__magic_name__							)							)
								return F'SkipList(level={self.level})\n' + "\n".join(__magic_name__							)
							def __iter__(		self		:int							):







								'''simple docstring'''







								a									=				self.head

								while len(node.forward							) != 0:
									yield node.forward[0].key
									a									=				node.forward[0]
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								a									=				1
								while random() < self.p and level < self.max_level:
									level += 1

								return level
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:int							):







								'''simple docstring'''







								a									=				[]

								a									=				self.head

								for i in reversed(range(self.level							)							):
									# i < node.level - When node level is lesser than `i` decrement `i`.
									# node.forward[i].key < key - Jumping to node with key value higher
									#                             or equal to searched key would result
									#                             in skipping searched key.
									while i < node.level and node.forward[i].key < key:
										a									=				node.forward[i]
									# Each leftmost node (relative to searched node) will potentially have to
									# be updated.
									update_vector.append(__magic_name__							)

								update_vector.reverse()  # Note that we were inserting values in reverse order.

								# len(node.forward) != 0 - If current node doesn't contain any further
								#                          references then searched key is not present.
								# node.forward[0].key == key - Next node key should be equal to search key
								#                              if key is present.
								if len(node.forward							) != 0 and node.forward[0].key == key:
									return node.forward[0], update_vector
								else:
									return None, update_vector
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:KT							):







								'''simple docstring'''







								a							,					a									=				self._locate_node(__magic_name__							)

								if node is not None:
									for i, update_node in enumerate(__magic_name__							):
										# Remove or replace all references to removed node.
										if update_node.level > i and update_node.forward[i].key == key:
											if node.level > i:
												a									=				node.forward[i]
											else:
												a									=				update_node.forward[:i]
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:KT						, __magic_name__		:VT							):







								'''simple docstring'''







								a							,					a									=				self._locate_node(__magic_name__							)
								if node is not None:
									a									=				value
								else:
									a									=				self.random_level()

									if level > self.level:
										# After level increase we have to add additional nodes to head.
										for _ in range(self.level - 1						, __magic_name__							):
											update_vector.append(self.head							)
										a									=				level

									a									=				Node(__magic_name__						, __magic_name__							)

									for i, update_node in enumerate(update_vector[:level]							):
										# Change references to pass through new node.
										if update_node.level > i:
											new_node.forward.append(update_node.forward[i]							)

										if update_node.level < i + 1:
											update_node.forward.append(__magic_name__							)
										else:
											a									=				new_node





							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:VT							):







								'''simple docstring'''







								a							,					a									=				self._locate_node(__magic_name__							)

								if node is not None:
									return node.value

								return None







def 						__A						(      ) ->			Tuple:
	a									=				SkipList()
	skip_list.insert("""Key1"""				,     3       )
	skip_list.insert("""Key2"""				,     12       )
	skip_list.insert("""Key3"""				,     41       )
	skip_list.insert("""Key4"""				,     -19       )

	a									=				skip_list.head
	a									=				{}
	while node.level != 0:
		a									=				node.forward[0]
		a									=				node.value

	assert len(__lowerCamelCase       ) == 4
	assert all_values["Key1"] == 3
	assert all_values["Key2"] == 12
	assert all_values["Key3"] == 41
	assert all_values["Key4"] == -19





def 						__A						(      ) ->			Tuple:
	a									=				SkipList()
	skip_list.insert("""Key1"""				,     10       )
	skip_list.insert("""Key1"""				,     12       )

	skip_list.insert("""Key5"""				,     7       )
	skip_list.insert("""Key7"""				,     10       )
	skip_list.insert("""Key10"""				,     5       )

	skip_list.insert("""Key7"""				,     7       )
	skip_list.insert("""Key5"""				,     5       )
	skip_list.insert("""Key10"""				,     10       )

	a									=				skip_list.head
	a									=				{}
	while node.level != 0:
		a									=				node.forward[0]
		a									=				node.value

	if len(__lowerCamelCase       ) != 4:
		print()
	assert len(__lowerCamelCase       ) == 4
	assert all_values["Key1"] == 12
	assert all_values["Key7"] == 7
	assert all_values["Key5"] == 5
	assert all_values["Key10"] == 10





def 						__A						(      ) ->			Optional[int]:
	a									=				SkipList()
	assert skip_list.find("""Some key"""       ) is None





def 						__A						(      ) ->			Optional[int]:
	a									=				SkipList()

	skip_list.insert("""Key2"""				,     20       )
	assert skip_list.find("""Key2"""       ) == 20

	skip_list.insert("""Some Key"""				,     10       )
	skip_list.insert("""Key2"""				,     8       )
	skip_list.insert("""V"""				,     13       )

	assert skip_list.find("""Y"""       ) is None
	assert skip_list.find("""Key2"""       ) == 8
	assert skip_list.find("""Some Key"""       ) == 10
	assert skip_list.find("""V"""       ) == 13





def 						__A						(      ) ->			str:
	a									=				SkipList()
	skip_list.delete("""Some key"""       )

	assert len(skip_list.head.forward       ) == 0





def 						__A						(      ) ->			int:
	a									=				SkipList()

	skip_list.insert("""Key1"""				,     12       )
	skip_list.insert("""V"""				,     13       )
	skip_list.insert("""X"""				,     14       )
	skip_list.insert("""Key2"""				,     15       )

	skip_list.delete("""V"""       )
	skip_list.delete("""Key2"""       )

	assert skip_list.find("""V"""       ) is None
	assert skip_list.find("""Key2"""       ) is None





def 						__A						(      ) ->			Union[str, Any]:
	a									=				SkipList()

	skip_list.insert("""Key1"""				,     12       )
	skip_list.insert("""V"""				,     13       )
	skip_list.insert("""X"""				,     14       )
	skip_list.insert("""Key2"""				,     15       )

	skip_list.delete("""V"""       )
	assert skip_list.find("""V"""       ) is None
	assert skip_list.find("""X"""       ) == 14
	assert skip_list.find("""Key1"""       ) == 12
	assert skip_list.find("""Key2"""       ) == 15

	skip_list.delete("""X"""       )
	assert skip_list.find("""V"""       ) is None
	assert skip_list.find("""X"""       ) is None
	assert skip_list.find("""Key1"""       ) == 12
	assert skip_list.find("""Key2"""       ) == 15

	skip_list.delete("""Key1"""       )
	assert skip_list.find("""V"""       ) is None
	assert skip_list.find("""X"""       ) is None
	assert skip_list.find("""Key1"""       ) is None
	assert skip_list.find("""Key2"""       ) == 15

	skip_list.delete("""Key2"""       )
	assert skip_list.find("""V"""       ) is None
	assert skip_list.find("""X"""       ) is None
	assert skip_list.find("""Key1"""       ) is None
	assert skip_list.find("""Key2"""       ) is None





def 						__A						(      ) ->			Dict:
	a									=				SkipList()

	skip_list.insert("""Key1"""				,     12       )
	skip_list.insert("""V"""				,     13       )
	skip_list.insert("""X"""				,     142       )
	skip_list.insert("""Key2"""				,     15       )

	skip_list.delete("""X"""       )

	def traverse_keys(__lowerCamelCase       ):
		yield node.key
		for forward_node in node.forward:
			yield from traverse_keys(__lowerCamelCase       )

	assert len(set(traverse_keys(skip_list.head       )       )       ) == 4





def 						__A						(      ) ->			Optional[Any]:
	def is_sorted(__lowerCamelCase       ):
		return all(next_item >= item for item, next_item in zip(__lowerCamelCase				,     lst[1:]       )       )

	a									=				SkipList()
	for i in range(10       ):
		skip_list.insert(__lowerCamelCase				,     __lowerCamelCase       )
	assert is_sorted(list(__lowerCamelCase       )       )
	skip_list.delete(5       )
	skip_list.delete(8       )
	skip_list.delete(2       )
	assert is_sorted(list(__lowerCamelCase       )       )
	skip_list.insert(-12				,     -12       )
	skip_list.insert(77				,     77       )
	assert is_sorted(list(__lowerCamelCase       )       )





def 						__A						(      ) ->			Optional[Any]:
	for _ in range(100       ):
		# Repeat test 100 times due to the probabilistic nature of skip list
		# random values == random bugs
		test_insert()
		test_insert_overrides_existing_value()

		test_searching_empty_list_returns_none()
		test_search()

		test_deleting_item_from_empty_list_do_nothing()
		test_deleted_items_are_not_founded_by_find_method()
		test_delete_removes_only_given_key()
		test_delete_doesnt_leave_dead_nodes()

		test_iter_always_yields_sorted_values()





def 						__A						(      ) ->			Optional[int]:
	a									=				SkipList()
	skip_list.insert(2				,     """2"""       )
	skip_list.insert(4				,     """4"""       )
	skip_list.insert(6				,     """4"""       )
	skip_list.insert(4				,     """5"""       )
	skip_list.insert(8				,     """4"""       )
	skip_list.insert(9				,     """4"""       )

	skip_list.delete(4       )

	print(__lowerCamelCase       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()

