



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

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

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

		lowerCAmelCase_			:     Optional[Any]									= XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )

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

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

		lowerCAmelCase_			:     int									= MLukeTokenizer.from_pretrained(__UpperCamelCase )

		# Initialize the embeddings of the special tokens
		lowerCAmelCase_			:     Dict									= tokenizer.convert_tokens_to_ids(['''@'''] )[0]
		lowerCAmelCase_			:     Optional[int]									= tokenizer.convert_tokens_to_ids(['''#'''] )[0]

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

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

		lowerCAmelCase_			:     Dict									= LukeForMaskedLM(config=__UpperCamelCase ).eval()

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

		lowerCAmelCase_					,  lowerCAmelCase_			:     int									= model.load_state_dict(__UpperCamelCase     ,strict=__UpperCamelCase )

		if set(__UpperCamelCase ) != {"luke.embeddings.position_ids"}:
				raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}""" )
		if set(__UpperCamelCase ) != {
		    "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
		lowerCAmelCase_			:     Union[str, Any]									= MLukeTokenizer.from_pretrained(__UpperCamelCase     ,task='''entity_classification''' )

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

		lowerCAmelCase_			:     Any									= model(**__UpperCamelCase )

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				lowerCAmelCase_			:     Union[str, Any]									= torch.Size((1, 33, 768) )
				lowerCAmelCase_			:     Any									= 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]     ,__UpperCamelCase     ,atol=1e-4 ):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				lowerCAmelCase_			:     List[str]									= torch.Size((1, 1, 768) )
				lowerCAmelCase_			:     Dict									= 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]     ,__UpperCamelCase     ,atol=1e-4 ):
				raise ValueError

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

		lowerCAmelCase_			:     Optional[int]									= model(**__UpperCamelCase )

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

		lowerCAmelCase_			:     str									= outputs.entity_logits[0][0].argmax().item()
		lowerCAmelCase_			:     Optional[int]									= [
		    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(__UpperCamelCase ) )
		model.save_pretrained(__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		List[Any] ):
		lowerCAmelCase_			:     int									= ['''[MASK]''', '''[PAD]''', '''[UNK]''']

		lowerCAmelCase_			:     Dict									= [json.loads(__UpperCamelCase ) for line in open(__UpperCamelCase )]

		lowerCAmelCase_			:     List[str]									= {}
		for entry in data:
				lowerCAmelCase_			:     str									= entry['''id''']
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								lowerCAmelCase_			:     Optional[int]									= entity_id
								break
						lowerCAmelCase_			:     Tuple									= f"""{language}:{entity_name}"""
						lowerCAmelCase_			:     Optional[int]									= entity_id
		return new_mapping


if __name__ == "__main__":
						A__  :  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.'''
						)
						A__  :  Dict								=					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 string import ascii_lowercase, ascii_uppercase





def lowerCamelCase						(     __lowerCamelCase :    str    )						->str:
    if not sentence:
        return ""
    _SCREAMING_SNAKE_CASE									=			dict(zip(__lowerCamelCase					,       __lowerCamelCase    )    )
    return lower_to_upper.get(sentence[0]					,       sentence[0]    ) + sentence[1:]


if __name__ == "__main__":
      from doctest import testmod

      testmod()



