"""
Apply the delta weights on top of a base model.

Usage:
python3 apply_delta.py --base ~/model_weights/llama-7b --target ~/model_weights/ChatYuan-7b --delta ~/model_weights/ChatYuan-7b-delta
"""
import argparse
import gc
import glob
import json
import os
import shutil
import tempfile

import torch
from torch import nn
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig


GB = 1 << 30


def split_files(model_path, tmp_path, split_size):
    if not os.path.exists(tmp_path):
        os.makedirs(tmp_path)

    file_pattern = os.path.join(model_path, "pytorch_model-*.bin")
    files = glob.glob(file_pattern)

    part = 0
    try:
        for file_path in tqdm(files):
            state_dict = torch.load(file_path)
            new_state_dict = {}

            current_size = 0
            for name, param in state_dict.items():
                param_size = param.numel() * param.element_size()

                if current_size + param_size > split_size:
                    new_file_name = f"pytorch_model-{part}.bin"
                    new_file_path = os.path.join(tmp_path, new_file_name)
                    torch.save(new_state_dict, new_file_path)
                    current_size = 0
                    new_state_dict = None
                    gc.collect()
                    new_state_dict = {}
                    part += 1

                new_state_dict[name] = param
                current_size += param_size

            new_file_name = f"pytorch_model-{part}.bin"
            new_file_path = os.path.join(tmp_path, new_file_name)
            torch.save(new_state_dict, new_file_path)
            new_state_dict = None
            gc.collect()
            new_state_dict = {}
            part += 1
    except Exception as e:
        print(f"An error occurred during split_files: {e}")
        shutil.rmtree(tmp_path)
        raise


def apply_delta_low_cpu_mem(base_model_path, target_model_path, delta_path):
    delta_tokenizer = AutoTokenizer.from_pretrained(delta_path, use_fast=False)
    delta_config = AutoConfig.from_pretrained(delta_path)

    if os.path.exists(target_model_path):
        shutil.rmtree(target_model_path)
    os.makedirs(target_model_path)

    split_size = 4 * GB

    with tempfile.TemporaryDirectory() as tmp_base_path, tempfile.TemporaryDirectory() as tmp_delta_path:
        print(f"Split files for the base model to {tmp_base_path}")
        split_files(base_model_path, tmp_base_path, split_size)
        print(f"Split files for the delta weights to {tmp_delta_path}")
        split_files(delta_path, tmp_delta_path, split_size)

        base_pattern = os.path.join(tmp_base_path, "pytorch_model-*.bin")
        base_files = glob.glob(base_pattern)
        delta_pattern = os.path.join(tmp_delta_path, "pytorch_model-*.bin")
        delta_files = glob.glob(delta_pattern)
        delta_state_dict = torch.load(delta_files[0])

        print("Applying the delta")
        weight_map = {}
        total_size = 0

        for i, base_file in tqdm(enumerate(base_files)):
            state_dict = torch.load(base_file)
            file_name = f"pytorch_model-{i}.bin"
            for name, param in state_dict.items():
                if name not in delta_state_dict:
                    for delta_file in delta_files:
                        delta_state_dict = torch.load(delta_file)
                        gc.collect()
                        if name in delta_state_dict:
                            break

                state_dict[name] += delta_state_dict[name]
                weight_map[name] = file_name
                total_size += param.numel() * param.element_size()
                gc.collect()
            torch.save(state_dict, os.path.join(target_model_path, file_name))

        with open(
            os.path.join(target_model_path, "pytorch_model.bin.index.json"), "w"
        ) as f:
            json.dump(
                {"weight_map": weight_map, "metadata": {"total_size": total_size}}, f
            )

    print(f"Saving the target model to {target_model_path}")
    delta_tokenizer.save_pretrained(target_model_path)
    delta_config.save_pretrained(target_model_path)


def apply_delta(base_model_path, target_model_path, delta_path):
    print(f"Loading the delta weights from {delta_path}")
    delta_tokenizer = AutoTokenizer.from_pretrained(delta_path, use_fast=False)
    delta = AutoModelForCausalLM.from_pretrained(
        delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
    )

    print(f"Loading the base model from {base_model_path}")
    base = AutoModelForCausalLM.from_pretrained(
        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
    )

    print("Applying the delta")
    for name, param in tqdm(base.state_dict().items(), desc="Applying delta"):
        assert name in delta.state_dict()
        param.data += delta.state_dict()[name]

    print(f"Saving the target model to {target_model_path}")
    base.save_pretrained(target_model_path)
    delta_tokenizer.save_pretrained(target_model_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--base-model-path", type=str, required=True)
    parser.add_argument("--target-model-path", type=str, required=True)
    parser.add_argument("--delta-path", type=str, required=True)
    parser.add_argument(
        "--low-cpu-mem",
        action="store_true",
        help="Lower the cpu memory usage. This will split large files and use "
        "disk as swap to reduce the memory usage below 10GB.",
    )
    args = parser.parse_args()

    print(args.base_model_path, args.target_model_path, args.delta_path)

    if args.low_cpu_mem:
        apply_delta_low_cpu_mem(
            args.base_model_path, args.target_model_path, args.delta_path
        )
    else:
        apply_delta(args.base_model_path, args.target_model_path, args.delta_path)