






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      :							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,
	)







from argparse import ArgumentParser

from . import BaseTransformersCLICommand
def       snake_case_		(     lowerCAmelCase_       :						Dict      ):
							return DownloadCommand(args.model       ,       args.cache_dir       ,       args.force       ,       args.trust_remote_code      )



class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					@staticmethod
					def       lowerCAmelCase				(  __a				:	ArgumentParser							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     parser.add_parser("""download"""							)
												download_parser.add_argument(
												    """--cache-dir"""		,     type=__a		,     default=__a		,     help="""Path to location to store the models"""							)
												download_parser.add_argument(
												    """--force"""		,     action="""store_true"""		,     help="""Force the model to be download even if already in cache-dir"""							)
												download_parser.add_argument(
												    """--trust-remote-code"""		,     action="""store_true"""		,     help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""		,     )
												download_parser.add_argument("""model"""		,     type=__a		,     help="""Name of the model to download"""							)
												download_parser.set_defaults(func=__a							)



					def __init__(  self				:	Dict		,     __a				:	str		,     __a				:	str		,     __a				:	bool		,     __a				:	bool							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     model
												__lowercase      :							List[Any]				=     cache
												__lowercase      :							Any				=     force
												__lowercase      :							Optional[int]				=     trust_remote_code



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


												"""simple docstring"""

												from ..models.auto import AutoModel, AutoTokenizer

												AutoModel.from_pretrained(
												    self._model		,     cache_dir=self._cache		,     force_download=self._force		,     trust_remote_code=self._trust_remote_code							)
												AutoTokenizer.from_pretrained(
												    self._model		,     cache_dir=self._cache		,     force_download=self._force		,     trust_remote_code=self._trust_remote_code							)