import torch
import gc

class Iter_TextSwitch:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                # 預設為空字串，允許不接線
                "add_text": ("STRING", {"forceInput": True, "default": ""}),
                "output_index": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                "input_count": ("INT", {"default": 2, "min": 1, "max": 128, "step": 1}),
                "use_last_input": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                # 這裡不需要寫死 text_0，因為 JS 會動態產生所有輸入
            }
        }

    RETURN_TYPES = ("STRING",)
    FUNCTION = "switch_and_combine"
    CATEGORY = "CustomNodes/Logic"

    def switch_and_combine(self, add_text="", output_index=0, input_count=1, use_last_input=False, **kwargs):
        """
        # 1. 蒐集所有動態傳入的字串
        texts = []
        for i in range(input_count):
            val = kwargs.get(f"text_{i}")
            # 只有當該接點有連線且有值時才加入（避免計算空的接點）
            if val is not None:
                texts.append(val)
        
        # 2. 安全性檢查：如果完全沒有輸入接點有連線
        if not texts:
            # 如果連 add_text 都沒接，就回傳純空字串
            return (add_text if add_text is not None else "",)

        # 3. 根據索引選取文本（防止除以零）
        target_index = output_index % len(texts)
        selected_text = texts[target_index]

        # 4. 串接並回傳
        # 確保兩者皆為字串，避免 NoneType 相加報錯
        final_header = add_text if add_text is not None else ""
        final_selected = selected_text if selected_text is not None else ""
        gc.collect()
        torch.cuda.empty_cache() 
        return (final_header + final_selected,)
        """
        # 1. 取得目標索引（對 input_count 取模，確保不溢出）
        # 這樣 index 0~n 就會嚴格對應到 image_0 ~ image_n
        target_idx = output_index % input_count
        
        # 2. 從目標索引開始「往回」搜尋有效接點
        res_txt = None
        for i in range(target_idx, -1, -1):
            val = kwargs.get(f"text_{i}")
            if val is not None or not use_last_input:
                res_txt = val
                break
        
        #gc.collect()
        #torch.cuda.empty_cache() 
        return (add_text + res_txt,)