# Copyright (c) ModelScope Contributors. All rights reserved.
import os

from swift.arguments import ExportArguments
from swift.model import save_checkpoint
from swift.tuners import Swift
from swift.utils import HfConfigFactory, get_logger
from ..utils import prepare_model_template

logger = get_logger()


def check_tie_word_embeddings(model):
    config = model.config
    try:
        from peft.utils import ModulesToSaveWrapper
        if not HfConfigFactory.get_config_attr(config, 'tie_word_embeddings'):
            return
        for module in [model.get_input_embeddings(), model.get_output_embeddings()]:
            if not isinstance(module, ModulesToSaveWrapper):
                return
        HfConfigFactory.set_config_attr(config, 'tie_word_embeddings', False)
    except Exception:
        pass


def merge_lora(args: ExportArguments, device_map=None, replace_if_exists=False) -> None:
    if replace_if_exists:
        logger.info(f'replace_if_exists: {replace_if_exists}')
    output_dir = getattr(args, 'output_dir', None) or f'{args.adapters[0]}-merged'
    if os.path.exists(output_dir) and not replace_if_exists:
        logger.info(f'The weight directory for the merged LoRA already exists in {output_dir}, '
                    'skipping the saving process.')
    else:
        # If the model is quantized, perform the merge on the original (unquantized) model.
        # https://github.com/huggingface/peft/issues/2321
        args.quant_method = None
        origin_device_map = args.device_map
        args.device_map = device_map or args.device_map
        logger.info(f'merge_device_map: {device_map}')
        model, template = prepare_model_template(args)
        logger.info('Merge LoRA...')
        check_tie_word_embeddings(model)
        Swift.merge_and_unload(model)
        model = model.model
        logger.info('Saving merged weights...')

        save_checkpoint(
            model,
            template.processor,
            output_dir,
            safe_serialization=args.safe_serialization,
            model_dirs=args.adapters,
            max_shard_size=args.max_shard_size,
            additional_saved_files=model.model_meta.additional_saved_files)
        logger.info(f'Successfully merged LoRA and saved in `{output_dir}`.')
        args.device_map = origin_device_map

    args.model = output_dir
    args.model_dir = output_dir
    args.adapters = []
