"""
    #################################################################################################################################################################################################
    # 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
import torch
from sapiens_transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
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")
    entity_vocab = load_entity_vocab(entity_vocab_path)
    tokenizer = RobertaTokenizer.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, LukeTokenizer.vocab_files_names["entity_vocab_file"]), "w") as f: json.dump(entity_vocab, f)
    tokenizer = LukeTokenizer.from_pretrained(pytorch_dump_folder_path)
    word_emb = state_dict["embeddings.word_embeddings.weight"]
    ent_emb = word_emb[tokenizer.convert_tokens_to_ids(["@"])[0]].unsqueeze(0)
    ent2_emb = word_emb[tokenizer.convert_tokens_to_ids(["#"])[0]].unsqueeze(0)
    state_dict["embeddings.word_embeddings.weight"] = torch.cat([word_emb, ent_emb, ent2_emb])
    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_emb[entity_vocab["[MASK2]"]] = entity_emb[entity_vocab["[MASK]"]]
    model = LukeModel(config=config).eval()
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    if not (len(missing_keys) == 1 and missing_keys[0] == "embeddings.position_ids"): raise ValueError(f"Missing keys {', '.join(missing_keys)}. Expected only missing embeddings.position_ids")
    if not (all(key.startswith("entity_predictions") or key.startswith("lm_head") for key in unexpected_keys)): raise ValueError(f"Unexpected keys {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions') or key.startswith('lm_head'))])}")
    tokenizer = LukeTokenizer.from_pretrained(pytorch_dump_folder_path, task="entity_classification")
    text = ("Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the new world number one avoid a humiliating second- round exit at Wimbledon .")
    span = (39, 42)
    encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt")
    outputs = model(**encoding)
    if model_size == "large":
        expected_shape = torch.Size((1, 42, 1024))
        expected_slice = torch.tensor([[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
    else:
        expected_shape = torch.Size((1, 42, 768))
        expected_slice = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])
    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":
        expected_shape = torch.Size((1, 1, 1024))
        expected_slice = torch.tensor([[0.0466, -0.0106, -0.0179]])
    else:
        expected_shape = torch.Size((1, 1, 768))
        expected_slice = torch.tensor([[0.1457, 0.1044, 0.0174]])
    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
    print("Saving PyTorch model to {}".format(pytorch_dump_folder_path))
    model.save_pretrained(pytorch_dump_folder_path)
def load_entity_vocab(entity_vocab_path):
    entity_vocab = {}
    with open(entity_vocab_path, "r", encoding="utf-8") as f:
        for index, line in enumerate(f):
            title, _ = line.rstrip().split("\t")
            entity_vocab[title] = index
    return entity_vocab
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.                                                                 #
    #################################################################################################################################################################################################
"""
