"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from sapiens_transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from sapiens_transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def convert_luke_checkpoint(checkpoint_path, metadata_path, entity_vocab_path, pytorch_dump_folder_path, model_size):
    with open(metadata_path) as metadata_file: metadata = json.load(metadata_file)
    config = LukeConfig(use_entity_aware_attention=True, **metadata["model_config"])
    state_dict = torch.load(checkpoint_path, map_location="cpu")["module"]
    entity_vocab = load_original_entity_vocab(entity_vocab_path)
    entity_vocab["[MASK2]"] = max(entity_vocab.values()) + 1
    config.entity_vocab_size += 1
    tokenizer = XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"])
    entity_token_1 = AddedToken("<ent>", lstrip=False, rstrip=False)
    entity_token_2 = AddedToken("<ent2>", lstrip=False, rstrip=False)
    tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_1, entity_token_2]})
    config.vocab_size += 2
    print(f"Saving tokenizer to {pytorch_dump_folder_path}")
    tokenizer.save_pretrained(pytorch_dump_folder_path)
    with open(os.path.join(pytorch_dump_folder_path, "tokenizer_config.json"), "r") as f: tokenizer_config = json.load(f)
    tokenizer_config["tokenizer_class"] = "MLukeTokenizer"
    with open(os.path.join(pytorch_dump_folder_path, "tokenizer_config.json"), "w") as f: json.dump(tokenizer_config, f)
    with open(os.path.join(pytorch_dump_folder_path, MLukeTokenizer.vocab_files_names["entity_vocab_file"]), "w") as f: json.dump(entity_vocab, f)
    tokenizer = MLukeTokenizer.from_pretrained(pytorch_dump_folder_path)
    ent_init_index = tokenizer.convert_tokens_to_ids(["@"])[0]
    ent2_init_index = tokenizer.convert_tokens_to_ids(["#"])[0]
    word_emb = state_dict["embeddings.word_embeddings.weight"]
    ent_emb = word_emb[ent_init_index].unsqueeze(0)
    ent2_emb = word_emb[ent2_init_index].unsqueeze(0)
    state_dict["embeddings.word_embeddings.weight"] = torch.cat([word_emb, ent_emb, ent2_emb])
    for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
        decoder_bias = state_dict[bias_name]
        ent_decoder_bias = decoder_bias[ent_init_index].unsqueeze(0)
        ent2_decoder_bias = decoder_bias[ent2_init_index].unsqueeze(0)
        state_dict[bias_name] = torch.cat([decoder_bias, ent_decoder_bias, ent2_decoder_bias])
    for layer_index in range(config.num_hidden_layers):
        for matrix_name in ["query.weight", "query.bias"]:
            prefix = f"encoder.layer.{layer_index}.attention.self."
            state_dict[prefix + "w2e_" + matrix_name] = state_dict[prefix + matrix_name]
            state_dict[prefix + "e2w_" + matrix_name] = state_dict[prefix + matrix_name]
            state_dict[prefix + "e2e_" + matrix_name] = state_dict[prefix + matrix_name]
    entity_emb = state_dict["entity_embeddings.entity_embeddings.weight"]
    entity_mask_emb = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0)
    state_dict["entity_embeddings.entity_embeddings.weight"] = torch.cat([entity_emb, entity_mask_emb])
    entity_prediction_bias = state_dict["entity_predictions.bias"]
    entity_mask_bias = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0)
    state_dict["entity_predictions.bias"] = torch.cat([entity_prediction_bias, entity_mask_bias])
    model = LukeForMaskedLM(config=config).eval()
    state_dict.pop("entity_predictions.decoder.weight")
    state_dict.pop("lm_head.decoder.weight")
    state_dict.pop("lm_head.decoder.bias")
    state_dict_for_hugging_face = OrderedDict()
    for key, value in state_dict.items():
        if not (key.startswith("lm_head") or key.startswith("entity_predictions")): state_dict_for_hugging_face[f"luke.{key}"] = state_dict[key]
        else: state_dict_for_hugging_face[key] = state_dict[key]
    missing_keys, unexpected_keys = model.load_state_dict(state_dict_for_hugging_face, strict=False)
    if set(unexpected_keys) != {"luke.embeddings.position_ids"}: raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}")
    if set(missing_keys) != {'entity_predictions.decoder.weight', 'lm_head.decoder.weight', 'lm_head.decoder.bias'}: 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()
    tokenizer = MLukeTokenizer.from_pretrained(pytorch_dump_folder_path, task="entity_classification")
    text = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
    span = (0, 9)
    encoding = tokenizer(text, entity_spans=[span], return_tensors="pt")
    outputs = model(**encoding)
    if model_size == "large": raise NotImplementedError
    else:
        expected_shape = torch.Size((1, 33, 768))
        expected_slice = 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], expected_slice, atol=1e-4): raise ValueError
    if model_size == "large": raise NotImplementedError
    else:
        expected_shape = torch.Size((1, 1, 768))
        expected_slice = 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 {expected_shape}")
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4): raise ValueError
    tokenizer = MLukeTokenizer.from_pretrained(pytorch_dump_folder_path)
    text = "Tokyo is the capital of <mask>."
    span = (24, 30)
    encoding = tokenizer(text, entity_spans=[span], return_tensors="pt")
    outputs = model(**encoding)
    input_ids = encoding["input_ids"][0].tolist()
    mask_position_id = input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"))
    predicted_id = outputs.logits[0][mask_position_id].argmax(dim=-1)
    assert "Japan" == tokenizer.decode(predicted_id)
    predicted_entity_id = outputs.entity_logits[0][0].argmax().item()
    multilingual_predicted_entities = [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"
    print("Saving PyTorch model to {}".format(pytorch_dump_folder_path))
    model.save_pretrained(pytorch_dump_folder_path)
def load_original_entity_vocab(entity_vocab_path):
    SPECIAL_TOKENS = ["[MASK]", "[PAD]", "[UNK]"]
    data = [json.loads(line) for line in open(entity_vocab_path)]
    new_mapping = {}
    for entry in data:
        entity_id = entry["id"]
        for entity_name, language in entry["entities"]:
            if entity_name in SPECIAL_TOKENS:
                new_mapping[entity_name] = entity_id
                break
            new_entity_name = f"{language}:{entity_name}"
            new_mapping[new_entity_name] = entity_id
    return new_mapping
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    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.")
    args = parser.parse_args()
    convert_luke_checkpoint(args.checkpoint_path, args.metadata_path, args.entity_vocab_path, args.pytorch_dump_folder_path, args.model_size)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
