from PIL import Image, ImageOps
from PIL.PngImagePlugin import PngInfo
from typing import Optional, Union, List
from urllib.request import urlopen
import comfy.diffusers_convert
import comfy.samplers
import comfy.sd
import comfy.utils
import comfy.clip_vision
import comfy.model_management
import numpy as np
import os
import subprocess
import sys
import torch

class cstr(str):
    class color:
        END = '\33[0m'
        BOLD = '\33[1m'
        ITALIC = '\33[3m'
        UNDERLINE = '\33[4m'
        BLINK = '\33[5m'
        BLINK2 = '\33[6m'
        SELECTED = '\33[7m'

        BLACK = '\33[30m'
        RED = '\33[31m'
        GREEN = '\33[32m'
        YELLOW = '\33[33m'
        BLUE = '\33[34m'
        VIOLET = '\33[35m'
        BEIGE = '\33[36m'
        WHITE = '\33[37m'

        BLACKBG = '\33[40m'
        REDBG = '\33[41m'
        GREENBG = '\33[42m'
        YELLOWBG = '\33[43m'
        BLUEBG = '\33[44m'
        VIOLETBG = '\33[45m'
        BEIGEBG = '\33[46m'
        WHITEBG = '\33[47m'

        GREY = '\33[90m'
        LIGHTRED = '\33[91m'
        LIGHTGREEN = '\33[92m'
        LIGHTYELLOW = '\33[93m'
        LIGHTBLUE = '\33[94m'
        LIGHTVIOLET = '\33[95m'
        LIGHTBEIGE = '\33[96m'
        LIGHTWHITE = '\33[97m'

        GREYBG = '\33[100m'
        LIGHTREDBG = '\33[101m'
        LIGHTGREENBG = '\33[102m'
        LIGHTYELLOWBG = '\33[103m'
        LIGHTBLUEBG = '\33[104m'
        LIGHTVIOLETBG = '\33[105m'
        LIGHTBEIGEBG = '\33[106m'
        LIGHTWHITEBG = '\33[107m'

        @staticmethod
        def add_code(name, code):
            if not hasattr(cstr.color, name.upper()):
                setattr(cstr.color, name.upper(), code)
            else:
                raise ValueError(f"'cstr' object already contains a code with the name '{name}'.")

    def __new__(cls, text):
        return super().__new__(cls, text)

    def __getattr__(self, attr):
        if attr.lower().startswith("_cstr"):
            code = getattr(self.color, attr.upper().lstrip("_cstr"))
            modified_text = self.replace(f"__{attr[1:]}__", f"{code}")
            return cstr(modified_text)
        elif attr.upper() in dir(self.color):
            code = getattr(self.color, attr.upper())
            modified_text = f"{code}{self}{self.color.END}"
            return cstr(modified_text)
        elif attr.lower() in dir(cstr):
            return getattr(cstr, attr.lower())
        else:
            raise AttributeError(f"'cstr' object has no attribute '{attr}'")

    def print(self, **kwargs):
        print(self, **kwargs)

# Tensor to PIL
def tensor2pil(image):
    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
# PIL to Tensor
def pil2tensor(image):
    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)

# Freeze PIP modules
def packages(versions=False):
    import sys
    import subprocess
    return [( r.decode().split('==')[0] if not versions else r.decode() ) for r in subprocess.check_output([sys.executable, '-s', '-m', 'pip', 'freeze']).split()]
def install_package(package, uninstall_first: Union[List[str], str] = None):
    if os.getenv("WAS_BLOCK_AUTO_INSTALL", 'False').lower() in ('true', '1', 't'):
        cstr(f"Preventing package install of '{package}' due to WAS_BLOCK_INSTALL env").msg.print()
    else:
        if uninstall_first is None:
            return

        if isinstance(uninstall_first, str):
            uninstall_first = [uninstall_first]

        cstr(f"Uninstalling {', '.join(uninstall_first)}..")
        subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'uninstall', *uninstall_first])
        cstr("Installing package...").msg.print()
        subprocess.check_call([sys.executable, '-s', '-m', 'pip', '-q', 'install', package])

class Image_Blending_Mode_Mask:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image_a": ("IMAGE",),
                "image_b": ("IMAGE",),
                "mode": ([
                    "add",
                    "color",
                    "color_burn",
                    "color_dodge",
                    "darken",
                    "difference",
                    "exclusion",
                    "hard_light",
                    "hue",
                    "lighten",
                    "multiply",
                    "overlay",
                    "screen",
                    "soft_light"
                ],),
                "blend_percentage": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                "mask": ("MASK",),  # 添加遮罩输入
            },
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "image_blending_mode_mask"

    CATEGORY = "Image/Image"

    def image_blending_mode_mask(self, image_a, image_b, mode='add', blend_percentage=1.0, mask=None):

        # Install Pilgram
        if 'pilgram' not in packages():
            install_package("pilgram")

        # Import Pilgram module
        import pilgram

        # Convert images to PIL
        img_a = tensor2pil(image_a)
        img_b = tensor2pil(image_b)

        # Ensure images are the same size
        if img_a.size != img_b.size:
            raise ValueError("Input images must have the same dimensions")

        # Apply blending mode
        blending_modes = {
            "color": pilgram.css.blending.color,
            "color_burn": pilgram.css.blending.color_burn,
            "color_dodge": pilgram.css.blending.color_dodge,
            "darken": pilgram.css.blending.darken,
            "difference": pilgram.css.blending.difference,
            "exclusion": pilgram.css.blending.exclusion,
            "hard_light": pilgram.css.blending.hard_light,
            "hue": pilgram.css.blending.hue,
            "lighten": pilgram.css.blending.lighten,
            "multiply": pilgram.css.blending.multiply,
            "add": pilgram.css.blending.normal,
            "overlay": pilgram.css.blending.overlay,
            "screen": pilgram.css.blending.screen,
            "soft_light": pilgram.css.blending.soft_light
        }

        out_image = blending_modes.get(mode, pilgram.css.blending.normal)(img_a, img_b)

        out_image = out_image.convert("RGB")

        # Apply mask if provided
        if mask is not None:
            mask = ImageOps.invert(tensor2pil(mask).convert('L'))
            out_image = Image.composite(img_a, out_image, mask.resize(img_a.size))

        # Blend image based on blend percentage
        blend_mask = Image.new(mode="L", size=img_a.size, color=(round(blend_percentage * 255)))
        blend_mask = ImageOps.invert(blend_mask)
        out_image = Image.composite(img_a, out_image, blend_mask)

        return (pil2tensor(out_image), )