






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       snake_case_		(     lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						Optional[Any]      ):
							# Load configuration defined in the metadata file
							with open(lowerCAmelCase_      ) as metadata_file:
														__lowercase      :							Tuple				=     json.load(lowerCAmelCase_      )
							__lowercase      :							Dict				=     LukeConfig(use_entity_aware_attention=lowerCAmelCase_       ,       **metadata["""model_config"""]      )

							# Load in the weights from the checkpoint_path
							__lowercase      :							str				=     torch.load(lowerCAmelCase_       ,       map_location="""cpu"""      )["""module"""]

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

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

							# Add special tokens to the token vocabulary for downstream tasks
							__lowercase      :							str				=     AddedToken("""<ent>"""       ,       lstrip=lowerCAmelCase_       ,       rstrip=lowerCAmelCase_      )
							__lowercase      :							Any				=     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      :							Union[str, Any]				=     json.load(lowerCAmelCase_      )
							__lowercase      :							List[str]				=     """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      :							Dict				=     MLukeTokenizer.from_pretrained(lowerCAmelCase_      )

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

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

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

							__lowercase      :							Any				=     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      :							int				=     OrderedDict()
							for key, value in state_dict.items():
														if not (key.startswith("""lm_head"""      ) or key.startswith("""entity_predictions"""      )):
																					__lowercase      :							List[str]				=     state_dict[key]
														else:
																					__lowercase      :							Union[str, Any]				=     state_dict[key]

							__lowercase			,			__lowercase      :							Union[str, Any]				=     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      :							List[str]				=     MLukeTokenizer.from_pretrained(lowerCAmelCase_       ,       task="""entity_classification"""      )

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

							__lowercase      :							int				=     model(**lowerCAmelCase_      )

							# Verify word hidden states
							if model_size == "large":
														raise NotImplementedError
							else:  # base
														__lowercase      :							List[str]				=     torch.Size((1, 33, 768)      )
														__lowercase      :							Dict				=     torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]      )

							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      :							Any				=     torch.Size((1, 1, 768)      )
														__lowercase      :							Optional[Any]				=     torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]      )

							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      :							List[Any]				=     MLukeTokenizer.from_pretrained(lowerCAmelCase_      )
							__lowercase      :							Optional[Any]				=     """Tokyo is the capital of <mask>."""
							__lowercase      :							Optional[Any]				=     (24, 30)
							__lowercase      :							Dict				=     tokenizer(lowerCAmelCase_       ,       entity_spans=[span]       ,       return_tensors="""pt"""      )

							__lowercase      :							Tuple				=     model(**lowerCAmelCase_      )

							__lowercase      :							Any				=     encoding["""input_ids"""][0].tolist()
							__lowercase      :							Union[str, Any]				=     input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""      )      )
							__lowercase      :							Dict				=     outputs.logits[0][mask_position_id].argmax(dim=-1      )
							assert "Japan" == tokenizer.decode(lowerCAmelCase_      )

							__lowercase      :							List[Any]				=     outputs.entity_logits[0][0].argmax().item()
							__lowercase      :							List[str]				=     [
							    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       snake_case_		(     lowerCAmelCase_       :						List[str]      ):
							__lowercase      :							Any				=     ["""[MASK]""", """[PAD]""", """[UNK]"""]

							__lowercase      :							Tuple				=     [json.loads(lowerCAmelCase_      ) for line in open(lowerCAmelCase_      )]

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


if __name__ == "__main__":
	lowerCamelCase : 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,
	)







import logging
import os
import threading
import time


try:
	import warnings
except ImportError:
	lowerCamelCase : Any						   =       None

try:
	import msvcrt
except ImportError:
	lowerCamelCase : str						   =       None

try:
	import fcntl
except ImportError:
	lowerCamelCase : Optional[Any]						   =       None


# Backward compatibility
# ------------------------------------------------
try:
	TimeoutError
except NameError:
	lowerCamelCase : Union[str, Any]						   =       OSError


# Data
# ------------------------------------------------
lowerCamelCase : Tuple						   =       [
    '''Timeout''',
    '''BaseFileLock''',
    '''WindowsFileLock''',
    '''UnixFileLock''',
    '''SoftFileLock''',
    '''FileLock''',
]

lowerCamelCase : Tuple						   =       '''3.0.12'''


lowerCamelCase : Any						   =       None
def       snake_case_		(     ):
							global _logger
							__lowercase      :							List[str]				=     _logger or logging.getLogger(__name__      )
							return _logger

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Any		,     __a				:	Any							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     lock_file
												return None



					def __str__(  self				:	str							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Any				=     F"The file lock '{self.lock_file}' could not be acquired."
												return temp







class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	List[Any]		,     __a				:	Optional[int]							)			->  int:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     lock
												return None



					def __enter__(  self				:	Dict							)			->  Dict:


												"""simple docstring"""

												return self.lock



					def __exit__(  self				:	Optional[int]		,     __a				:	Dict		,     __a				:	Any		,     __a				:	Tuple							)			->  Optional[Any]:


												"""simple docstring"""

												self.lock.release()
												return None







class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	Tuple		,     __a				:	Any		,     __a				:	Dict=-1		,     __a				:	Optional[Any]=None							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     max_filename_length if max_filename_length is not None else 255
												# Hash the filename if it's too long
												__lowercase      :							Dict				=     self.hash_filename_if_too_long(__a		,     __a							)
												# The path to the lock file.
												__lowercase      :							Optional[Any]				=     lock_file

												# The file descriptor for the *_lock_file* as it is returned by the
												# os.open() function.
												# This file lock is only NOT None, if the object currently holds the
												# lock.
												__lowercase      :							int				=     None

												# The default timeout value.
												__lowercase      :							Optional[int]				=     timeout

												# We use this lock primarily for the lock counter.
												__lowercase      :							Optional[Any]				=     threading.Lock()

												# The lock counter is used for implementing the nested locking
												# mechanism. Whenever the lock is acquired, the counter is increased and
												# the lock is only released, when this value is 0 again.
												__lowercase      :							Union[str, Any]				=     0
												return None



					@property
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Optional[int]:


												"""simple docstring"""

												return self._lock_file



					@property
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Optional[Any]:


												"""simple docstring"""

												return self._timeout



					@timeout.setter
					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	Tuple							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Tuple				=     float(__a							)
												return None



					def       lowerCAmelCase				(  self				:	Dict							)			->  Tuple:


												"""simple docstring"""

												raise NotImplementedError()



					def       lowerCAmelCase				(  self				:	Dict							)			->  Any:


												"""simple docstring"""

												raise NotImplementedError()



					@property
					def       lowerCAmelCase				(  self				:	Dict							)			->  List[Any]:


												"""simple docstring"""

												return self._lock_file_fd is not None



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	Optional[Any]=None		,     __a				:	Union[str, Any]=0.05							)			->  List[str]:


												"""simple docstring"""

												if timeout is None:
																			__lowercase      :							Union[str, Any]				=     self.timeout

												# Increment the number right at the beginning.
												# We can still undo it, if something fails.
												with self._thread_lock:
																			self._lock_counter += 1

												__lowercase      :							int				=     id(self							)
												__lowercase      :							Optional[Any]				=     self._lock_file
												__lowercase      :							List[str]				=     time.time()
												try:
																			while True:
																										with self._thread_lock:
																																	if not self.is_locked:
																																								logger().debug(F"Attempting to acquire lock {lock_id} on {lock_filename}"							)
																																								self._acquire()

																										if self.is_locked:
																																	logger().debug(F"Lock {lock_id} acquired on {lock_filename}"							)
																																	break
																										elif timeout >= 0 and time.time() - start_time > timeout:
																																	logger().debug(F"Timeout on acquiring lock {lock_id} on {lock_filename}"							)
																																	raise Timeout(self._lock_file							)
																										else:
																																	logger().debug(
																																	    F"Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ..."							)
																																	time.sleep(__a							)
												except:  # noqa
																			# Something did go wrong, so decrement the counter.
																			with self._thread_lock:
																										__lowercase      :							Optional[int]				=     max(0		,     self._lock_counter - 1							)

																			raise
												return _Acquire_ReturnProxy(lock=self							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Optional[Any]=False							)			->  Optional[Any]:


												"""simple docstring"""

												with self._thread_lock:
																			if self.is_locked:
																										self._lock_counter -= 1

																										if self._lock_counter == 0 or force:
																																	__lowercase      :							Optional[Any]				=     id(self							)
																																	__lowercase      :							str				=     self._lock_file

																																	logger().debug(F"Attempting to release lock {lock_id} on {lock_filename}"							)
																																	self._release()
																																	__lowercase      :							List[str]				=     0
																																	logger().debug(F"Lock {lock_id} released on {lock_filename}"							)

												return None



					def __enter__(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												self.acquire()
												return self



					def __exit__(  self				:	List[str]		,     __a				:	str		,     __a				:	int		,     __a				:	List[Any]							)			->  Tuple:


												"""simple docstring"""

												self.release()
												return None



					def __del__(  self				:	Optional[int]							)			->  Optional[int]:


												"""simple docstring"""

												self.release(force=__a							)
												return None



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	str		,     __a				:	int							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     os.path.basename(__a							)
												if len(__a							) > max_length and max_length > 0:
																			__lowercase      :							int				=     os.path.dirname(__a							)
																			__lowercase      :							List[str]				=     str(hash(__a							)							)
																			__lowercase      :							Optional[Any]				=     filename[: max_length - len(__a							) - 8] + """...""" + hashed_filename + """.lock"""
																			return os.path.join(__a		,     __a							)
												else:
																			return path







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Union[str, Any]		,     __a				:	List[Any]		,     __a				:	Optional[int]=-1		,     __a				:	Tuple=None							)			->  List[Any]:


												"""simple docstring"""

												from .file_utils import relative_to_absolute_path

												super().__init__(__a		,     timeout=__a		,     max_filename_length=__a							)
												__lowercase      :							Tuple				=     """\\\\?\\""" + relative_to_absolute_path(self.lock_file							)



					def       lowerCAmelCase				(  self				:	Dict							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     os.O_RDWR | os.O_CREAT | os.O_TRUNC

												try:
																			__lowercase      :							Tuple				=     os.open(self._lock_file		,     __a							)
												except OSError:
																			pass
												else:
																			try:
																										msvcrt.locking(__a		,     msvcrt.LK_NBLCK		,     1							)
																			except OSError:
																										os.close(__a							)
																			else:
																										__lowercase      :							Union[str, Any]				=     fd
												return None



					def       lowerCAmelCase				(  self				:	Any							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     self._lock_file_fd
												__lowercase      :							int				=     None
												msvcrt.locking(__a		,     msvcrt.LK_UNLCK		,     1							)
												os.close(__a							)

												try:
																			os.remove(self._lock_file							)
												# Probably another instance of the application
												# that acquired the file lock.
												except OSError:
																			pass
												return None







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	List[str]		,     __a				:	Optional[Any]		,     __a				:	str=-1		,     __a				:	List[str]=None							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Dict				=     os.statvfs(os.path.dirname(__a							)							).f_namemax
												super().__init__(__a		,     timeout=__a		,     max_filename_length=__a							)



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     os.O_RDWR | os.O_CREAT | os.O_TRUNC
												__lowercase      :							List[str]				=     os.open(self._lock_file		,     __a							)

												try:
																			fcntl.flock(__a		,     fcntl.LOCK_EX | fcntl.LOCK_NB							)
												except OSError:
																			os.close(__a							)
												else:
																			__lowercase      :							str				=     fd
												return None



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							Any				=     self._lock_file_fd
												__lowercase      :							List[str]				=     None
												fcntl.flock(__a		,     fcntl.LOCK_UN							)
												os.close(__a							)
												return None







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def       lowerCAmelCase				(  self				:	List[str]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Tuple				=     os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
												try:
																			__lowercase      :							Union[str, Any]				=     os.open(self._lock_file		,     __a							)
												except OSError:
																			pass
												else:
																			__lowercase      :							Optional[int]				=     fd
												return None



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Tuple:


												"""simple docstring"""

												os.close(self._lock_file_fd							)
												__lowercase      :							int				=     None

												try:
																			os.remove(self._lock_file							)
												# The file is already deleted and that's what we want.
												except OSError:
																			pass
												return None







lowerCamelCase : Optional[Any]						   =       None

if msvcrt:
	lowerCamelCase : List[Any]						   =       WindowsFileLock
elif fcntl:
	lowerCamelCase : List[Any]						   =       UnixFileLock
else:
	lowerCamelCase : Union[str, Any]						   =       SoftFileLock

	if warnings is not None:
		warnings.warn('''only soft file lock is available''')