'''

Converts a transformers model to a format compatible with flexgen.

'''

import argparse
import os
import numpy as np
from pathlib import Path
from sys import argv

import torch
from tqdm import tqdm
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
 
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
args = parser.parse_args()

def disable_torch_init():
    """
    Disable the redundant torch default initialization to accelerate model creation.
    """
    import torch
    global torch_linear_init_backup
    global torch_layer_norm_init_backup

    torch_linear_init_backup = torch.nn.Linear.reset_parameters
    setattr(torch.nn.Linear, "reset_parameters", lambda self: None)

    torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters
    setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)


def restore_torch_init():
    """Rollback the change made by disable_torch_init."""
    import torch
    setattr(torch.nn.Linear, "reset_parameters", torch_linear_init_backup)
    setattr(torch.nn.LayerNorm, "reset_parameters", torch_layer_norm_init_backup)

if __name__ == '__main__':
    path = Path(args.MODEL)
    model_name = path.name

    print(f"Loading {model_name}...")
    #disable_torch_init()
    model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
    #restore_torch_init()

    tokenizer = AutoTokenizer.from_pretrained(path)

    out_folder = Path(f"models/{model_name}-np")
    if not Path(out_folder).exists():
        os.mkdir(out_folder)

    print(f"Saving the converted model to {out_folder}...")
    for name, param in tqdm(list(model.model.named_parameters())):
        name = name.replace("decoder.final_layer_norm", "decoder.layer_norm")
        param_path = os.path.join(out_folder, name)
        with open(param_path, "wb") as f:
            np.save(f, param.cpu().detach().numpy())