import numpy as np
import scipy.ndimage
import torch
import comfy.utils
import node_helpers
import folder_paths
import random

import nodes
from nodes import MAX_RESOLUTION

def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
    source = source.to(destination.device)
    if resize_source:
        source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")

    source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])

    x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
    y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))

    left, top = (x // multiplier, y // multiplier)
    right, bottom = (left + source.shape[3], top + source.shape[2],)

    if mask is None:
        mask = torch.ones_like(source)
    else:
        mask = mask.to(destination.device, copy=True)
        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
        mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])

    # calculate the bounds of the source that will be overlapping the destination
    # this prevents the source trying to overwrite latent pixels that are out of bounds
    # of the destination
    visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)

    mask = mask[:, :, :visible_height, :visible_width]
    inverse_mask = torch.ones_like(mask) - mask

    source_portion = mask * source[:, :, :visible_height, :visible_width]
    destination_portion = inverse_mask  * destination[:, :, top:bottom, left:right]

    destination[:, :, top:bottom, left:right] = source_portion + destination_portion
    return destination



class MyMaskComposite:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "destination": ("MASK",),
                "source": ("MASK",),
                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "offset": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "operation": (["multiply", "add", "subtract", "and", "or", "xor"],),
            }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)

    FUNCTION = "combine"

    def combine(self, destination, source, x, y, offset, operation):
        output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
        source = source.reshape((-1, source.shape[-2], source.shape[-1]))
        print(source.shape)  # 输出数组的形状

        left, top = (x, y,)
        right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
        visible_width, visible_height = (right - left, bottom - top,)
        
        source[:, :offset, :] = 0

        source_portion = source[:, :visible_height, :visible_width]
        destination_portion = output[:, top:bottom, left:right]

        print(f"left:{left} top:{top} right:{right} bottom:{bottom} visible_width:{visible_width} visible_height:{visible_height}")

        if operation == "multiply":
            output[:, top:bottom, left:right] = destination_portion * source_portion
        elif operation == "add":
            output[:, top:bottom, left:right] = destination_portion + source_portion
        elif operation == "subtract":
            output[:, top:bottom, left:right] = destination_portion - source_portion
        elif operation == "and":
            output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
        elif operation == "or":
            output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()
        elif operation == "xor":
            output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()

        output = torch.clamp(output, 0.0, 1.0)

        return (output,)




NODE_CLASS_MAPPINGS = {
    "MyMaskComposite": MyMaskComposite,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "MyMaskComposite": "MyMaskComposite",
}