import torch
import torch.nn as nn

def freeze_model(model, pretrained_state_dict=None, freeze_keywords=('blocks',), keep_keywords=('Caption_embedding', 'condition_layers', 'cross_attn_inject')):
    """
    冻结模型中已在预训练权重中出现的参数(来自 state_dict),但保留 keep_keywords 中包含的模块可训练。
    
    :param model: 要处理的 PyTorch 模型
    :param pretrained_state_dict: 来自 torch.load() 的 state_dict,默认不提供时使用关键词模式
    :param freeze_keywords: 当未提供 state_dict 时，根据关键词冻结
    :param keep_keywords: 始终保留可训练的关键词( )
    """
    frozen, trainable = [], []

    if pretrained_state_dict is not None:
        pretrained_keys = set(pretrained_state_dict.keys())
        for name, param in model.named_parameters():
            if name in pretrained_keys and not any(k in name for k in keep_keywords):
                param.requires_grad = False
                frozen.append(name)
            else:
                param.requires_grad = True
                trainable.append(name)
    else:
        # fallback：关键词方式
        for name, param in model.named_parameters():
            if any(k in name for k in freeze_keywords) and not any(k in name for k in keep_keywords):
                param.requires_grad = False
                frozen.append(name)
            else:
                param.requires_grad = True
                trainable.append(name)

    # print(f"[Freeze Summary]  Frozen: {len(frozen)},  Trainable: {len(trainable)}")
    # for name in frozen:
    #     print(f"  [Frozen]    {name}")
    # for name in trainable:
    #     print(f"  [Trainable] {name}")