





'''simple docstring'''


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_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       List[Any]		,							SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    with open(SCREAMING_SNAKE_CASE_     ) as metadata_file:
        _lowerCAmelCase						      =   json.load(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE_		,							**metadata["model_config"]     )

    # Load in the weights from the checkpoint_path
    _lowerCAmelCase						      =   torch.load(SCREAMING_SNAKE_CASE_		,							map_location="cpu"     )["module"]

    # Load the entity vocab file
    _lowerCAmelCase						      =   load_original_entity_vocab(SCREAMING_SNAKE_CASE_     )
    # add an entry for [MASK2]
    _lowerCAmelCase						      =   max(entity_vocab.values()     ) + 1
    config.entity_vocab_size += 1

    _lowerCAmelCase						      =   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]     )

    # Add special tokens to the token vocabulary for downstream tasks
    _lowerCAmelCase						      =   AddedToken("<ent>"		,							lstrip=SCREAMING_SNAKE_CASE_		,							rstrip=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   AddedToken("<ent2>"		,							lstrip=SCREAMING_SNAKE_CASE_		,							rstrip=SCREAMING_SNAKE_CASE_     )
    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(SCREAMING_SNAKE_CASE_     )
    with open(os.path.join(SCREAMING_SNAKE_CASE_		,							"tokenizer_config.json"     )		,							"r"     ) as f:
        _lowerCAmelCase						      =   json.load(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   "MLukeTokenizer"
    with open(os.path.join(SCREAMING_SNAKE_CASE_		,							"tokenizer_config.json"     )		,							"w"     ) as f:
        json.dump(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

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

    _lowerCAmelCase						      =   MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )

    # Initialize the embeddings of the special tokens
    _lowerCAmelCase						      =   tokenizer.convert_tokens_to_ids(["@"]     )[0]
    _lowerCAmelCase						      =   tokenizer.convert_tokens_to_ids(["#"]     )[0]

    _lowerCAmelCase						      =   state_dict["embeddings.word_embeddings.weight"]
    _lowerCAmelCase						      =   word_emb[ent_init_index].unsqueeze(0     )
    _lowerCAmelCase						      =   word_emb[enta_init_index].unsqueeze(0     )
    _lowerCAmelCase						      =   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						      =   state_dict[bias_name]
        _lowerCAmelCase						      =   decoder_bias[ent_init_index].unsqueeze(0     )
        _lowerCAmelCase						      =   decoder_bias[enta_init_index].unsqueeze(0     )
        _lowerCAmelCase						      =   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						      =   F'''encoder.layer.{layer_index}.attention.self.'''
            _lowerCAmelCase						      =   state_dict[prefix + matrix_name]
            _lowerCAmelCase						      =   state_dict[prefix + matrix_name]
            _lowerCAmelCase						      =   state_dict[prefix + matrix_name]

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

    _lowerCAmelCase						      =   LukeForMaskedLM(config=SCREAMING_SNAKE_CASE_     ).eval()

    state_dict.pop("entity_predictions.decoder.weight"     )
    state_dict.pop("lm_head.decoder.weight"     )
    state_dict.pop("lm_head.decoder.bias"     )
    _lowerCAmelCase						      =   OrderedDict()
    for key, value in state_dict.items():
        if not (key.startswith("lm_head"     ) or key.startswith("entity_predictions"     )):
            _lowerCAmelCase						      =   state_dict[key]
        else:
            _lowerCAmelCase						      =   state_dict[key]

    _lowerCAmelCase      ,      _lowerCAmelCase						      =   model.load_state_dict(SCREAMING_SNAKE_CASE_		,							strict=SCREAMING_SNAKE_CASE_     )

    if set(SCREAMING_SNAKE_CASE_     ) != {"luke.embeddings.position_ids"}:
        raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''     )
    if set(SCREAMING_SNAKE_CASE_     ) != {
        "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						      =   MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_		,							task="entity_classification"     )

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

    _lowerCAmelCase						      =   model(**SCREAMING_SNAKE_CASE_     )

    # Verify word hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        _lowerCAmelCase						      =   torch.Size((1, 33, 768)     )
        _lowerCAmelCase						      =   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]		,							SCREAMING_SNAKE_CASE_		,							atol=1e-4     ):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        raise NotImplementedError
    else:  # base
        _lowerCAmelCase						      =   torch.Size((1, 1, 768)     )
        _lowerCAmelCase						      =   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]		,							SCREAMING_SNAKE_CASE_		,							atol=1e-4     ):
        raise ValueError

    # Verify masked word/entity prediction
    _lowerCAmelCase						      =   MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   "Tokyo is the capital of <mask>."
    _lowerCAmelCase						      =   (24, 30)
    _lowerCAmelCase						      =   tokenizer(SCREAMING_SNAKE_CASE_		,							entity_spans=[span]		,							return_tensors="pt"     )

    _lowerCAmelCase						      =   model(**SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   encoding["input_ids"][0].tolist()
    _lowerCAmelCase						      =   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"     )     )
    _lowerCAmelCase						      =   outputs.logits[0][mask_position_id].argmax(dim=-1     )
    assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   outputs.entity_logits[0][0].argmax().item()
    _lowerCAmelCase						      =   [
        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(SCREAMING_SNAKE_CASE_     )     )
    model.save_pretrained(SCREAMING_SNAKE_CASE_     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   ["[MASK]", "[PAD]", "[UNK]"]

    _lowerCAmelCase						      =   [json.loads(SCREAMING_SNAKE_CASE_     ) for line in open(SCREAMING_SNAKE_CASE_     )]

    _lowerCAmelCase						      =   {}
    for entry in data:
        _lowerCAmelCase						      =   entry["id"]
        for entity_name, language in entry["entities"]:
            if entity_name in SPECIAL_TOKENS:
                _lowerCAmelCase						      =   entity_id
                break
            _lowerCAmelCase						      =   F'''{language}:{entity_name}'''
            _lowerCAmelCase						      =   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,
       )









def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     int 						=      str(bin(lowerCAmelCase__						)						)
		binary_number += "0" * shift_amount
		return binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     Union[str, Any] 						=      str(bin(lowerCAmelCase__						)						)[2:]
		if shift_amount >= len(lowerCAmelCase__						):
				return "0b0"
		lowerCamelCase_	:     List[str] 						=      binary_number[: len(lowerCAmelCase__						) - shift_amount]
		return "0b" + shifted_binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number >= 0:  # Get binary representation of positive number
				lowerCamelCase_	:     List[Any] 						=      '0' + str(bin(lowerCAmelCase__						)						).strip('-'						)[2:]
		else:  # Get binary (2's complement) representation of negative number
				lowerCamelCase_	:     Any 						=      len(bin(lowerCAmelCase__						)[3:]						)  # Find 2's complement of number
				lowerCamelCase_	:     List[str] 						=      bin(abs(lowerCAmelCase__						) - (1 << binary_number_length)						)[3:]
				lowerCamelCase_	:     List[str] 						=      (
				    '1' + '0' * (binary_number_length - len(lowerCAmelCase__						)) + binary_number
				)

		if shift_amount >= len(lowerCAmelCase__						):
				return "0b" + binary_number[0] * len(lowerCAmelCase__						)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(lowerCAmelCase__						) - shift_amount]
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
