





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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	# Load configuration defined in the metadata file
	with open(_lowerCAmelCase				) as metadata_file:
		snake_case_						:     Dict     =		json.load(_lowerCAmelCase				)
	snake_case_						:     List[Any]     =		LukeConfig(use_entity_aware_attention=_lowerCAmelCase				,	**metadata['model_config']				)

	# Load in the weights from the checkpoint_path
	snake_case_						:     List[Any]     =		torch.load(_lowerCAmelCase				,	map_location='cpu'				)["module"]

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

	snake_case_						:     Tuple     =		XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']				)

	# Add special tokens to the token vocabulary for downstream tasks
	snake_case_						:     str     =		AddedToken('<ent>'				,	lstrip=_lowerCAmelCase				,	rstrip=_lowerCAmelCase				)
	snake_case_						:     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:
		snake_case_						:     Optional[int]     =		json.load(_lowerCAmelCase				)
	snake_case_						:     Dict     =		"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				)

	snake_case_						:     Dict     =		MLukeTokenizer.from_pretrained(_lowerCAmelCase				)

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

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

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

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

	snake_case_						:     Optional[int]     =		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
	snake_case_						:     List[Any]     =		MLukeTokenizer.from_pretrained(_lowerCAmelCase				,	task='entity_classification'				)

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

	snake_case_						:     Any     =		model(**_lowerCAmelCase				)

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		snake_case_						:     Dict     =		torch.Size((1, 33, 7_68)				)
		snake_case_						:     List[str]     =		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
		snake_case_						:     Union[str, Any]     =		torch.Size((1, 1, 7_68)				)
		snake_case_						:     Any     =		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
	snake_case_						:     Tuple     =		MLukeTokenizer.from_pretrained(_lowerCAmelCase				)
	snake_case_						:     Any     =		"Tokyo is the capital of <mask>."
	snake_case_						:     List[Any]     =		(24, 30)
	snake_case_						:     Dict     =		tokenizer(_lowerCAmelCase				,	entity_spans=[span]				,	return_tensors='pt'				)

	snake_case_						:     Any     =		model(**_lowerCAmelCase				)

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

	snake_case_						:     int     =		outputs.entity_logits[0][0].argmax().item()
	snake_case_						:     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(_lowerCAmelCase				)				)
	model.save_pretrained(_lowerCAmelCase				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[Any]     =		["[MASK]", "[PAD]", "[UNK]"]

	snake_case_						:     int     =		[json.loads(_lowerCAmelCase				) for line in open(_lowerCAmelCase				)]

	snake_case_						:     str     =		{}
	for entry in data:
		snake_case_						:     str     =		entry["id"]
		for entity_name, language in entry["entities"]:
			if entity_name in SPECIAL_TOKENS:
				snake_case_						:     str     =		entity_id
				break
			snake_case_						:     Any     =		f"""{language}:{entity_name}"""
			snake_case_						:     List[Any]     =		entity_id
	return new_mapping


if __name__ == "__main__":
						_SCREAMING_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."""
						)
						_SCREAMING_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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InformerForPrediction""",
						    """InformerModel""",
						    """InformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_informer import (
												    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InformerForPrediction,
												    InformerModel,
												    InformerPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

