import torch
import gc

class Iter_ConditioningSwitch:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "output_index": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                "input_count": ("INT", {"default": 2, "min": 1, "max": 64}),
                "use_last_input": ("BOOLEAN", {"default": True}),
            },
        }

    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "switch_conditioning"
    CATEGORY = "CustomNodes/Iterators"

    def switch_conditioning(self, output_index, input_count, use_last_input, **kwargs):
        target_idx = output_index % input_count
        res = None
        for i in range(target_idx, -1, -1):
            val = kwargs.get(f"cond_{i}")
            if val is not None or not use_last_input:
                res = val
                break
        #gc.collect()
        #torch.cuda.empty_cache() 
        return (res,)

class Iter_CLIPSwitch:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "output_index": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                "input_count": ("INT", {"default": 2, "min": 1, "max": 64}),
                "use_last_input": ("BOOLEAN", {"default": True}),
            },
        }

    RETURN_TYPES = ("CLIP",)
    FUNCTION = "switch_clip"
    CATEGORY = "CustomNodes/Iterators"

    def switch_clip(self, output_index, input_count, use_last_input, **kwargs):
        target_idx = output_index % input_count
        res = None
        for i in range(target_idx, -1, -1):
            val = kwargs.get(f"clip_{i}")
            if val is not None or not use_last_input:
                res = val
                break
        #gc.collect()
        #torch.cuda.empty_cache()         
        return (res,)