





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__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[Any]:
		# Load configuration defined in the metadata file
		with open(lowerCAmelCase__		) as metadata_file:
				lowerCamelCase   : Optional[int]								=json.load(lowerCAmelCase__		)
		lowerCamelCase   : str								=LukeConfig(use_entity_aware_attention=lowerCAmelCase__							,       **metadata['''model_config''']		)

		# Load in the weights from the checkpoint_path
		lowerCamelCase   : str								=torch.load(lowerCAmelCase__							,       map_location='''cpu'''		)['''module''']

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

		lowerCamelCase   : List[Any]								=XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']		)

		# Add special tokens to the token vocabulary for downstream tasks
		lowerCamelCase   : Union[str, Any]								=AddedToken('''<ent>'''							,       lstrip=lowerCAmelCase__							,       rstrip=lowerCAmelCase__		)
		lowerCamelCase   : Optional[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:
				lowerCamelCase   : int								=json.load(lowerCAmelCase__		)
		lowerCamelCase   : Any								='''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__		)

		lowerCamelCase   : List[str]								=MLukeTokenizer.from_pretrained(lowerCAmelCase__		)

		# Initialize the embeddings of the special tokens
		lowerCamelCase   : List[str]								=tokenizer.convert_tokens_to_ids(['''@''']		)[0]
		lowerCamelCase   : Tuple								=tokenizer.convert_tokens_to_ids(['''#''']		)[0]

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

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

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

		lowerCamelCase		,   lowerCamelCase   : List[str]								=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
		lowerCamelCase   : Tuple								=MLukeTokenizer.from_pretrained(lowerCAmelCase__							,       task='''entity_classification'''		)

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

		lowerCamelCase   : Dict								=model(**lowerCAmelCase__		)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				lowerCamelCase   : List[Any]								=torch.Size((1, 3_3, 7_6_8)		)
				lowerCamelCase   : Tuple								=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]							,       lowerCAmelCase__							,       atol=1E-4		):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				lowerCamelCase   : str								=torch.Size((1, 1, 7_6_8)		)
				lowerCamelCase   : List[Any]								=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]							,       lowerCAmelCase__							,       atol=1E-4		):
				raise ValueError

		# Verify masked word/entity prediction
		lowerCamelCase   : Optional[int]								=MLukeTokenizer.from_pretrained(lowerCAmelCase__		)
		lowerCamelCase   : int								='''Tokyo is the capital of <mask>.'''
		lowerCamelCase   : str								=(2_4, 3_0)
		lowerCamelCase   : Tuple								=tokenizer(lowerCAmelCase__							,       entity_spans=[span]							,       return_tensors='''pt'''		)

		lowerCamelCase   : Tuple								=model(**lowerCAmelCase__		)

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

		lowerCamelCase   : str								=outputs.entity_logits[0][0].argmax().item()
		lowerCamelCase   : Tuple								=[
		    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__	(       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : int								=['''[MASK]''', '''[PAD]''', '''[UNK]''']

		lowerCamelCase   : Dict								=[json.loads(lowerCAmelCase__		) for line in open(lowerCAmelCase__		)]

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


if __name__ == "__main__":
				snake_case_							=			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.'''
				)
				snake_case_							=			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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_							=			{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''InstructBlipQFormerModel''',
				    '''InstructBlipPreTrainedModel''',
				    '''InstructBlipForConditionalGeneration''',
				    '''InstructBlipVisionModel''',
				]

if TYPE_CHECKING:
				from .configuration_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    InstructBlipConfig,
				    InstructBlipQFormerConfig,
				    InstructBlipVisionConfig,
				)
				from .processing_instructblip import InstructBlipProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_instructblip import (
								    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    InstructBlipForConditionalGeneration,
								    InstructBlipPreTrainedModel,
								    InstructBlipQFormerModel,
								    InstructBlipVisionModel,
								)

else:
				import sys

				snake_case_							=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

