from PIL import Image, ImageFilter
import torch
from nodes import common_ksampler, VAEEncode, VAEDecode
from utils import pil_to_tensor, tensor_to_pil, get_crop_region, expand_crop, crop_cond, pad_image
from modules import shared

if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
    Image.Resampling = Image


class StableDiffusionProcessing:

    def __init__(self, init_img, model, positive, negative, vae, seed, steps, cfg, sampler_name, scheduler, denoise, upscale_by, force_uniform_tile_size):
        # Variables used by the USDU script
        self.init_images = [init_img]
        self.image_mask = None
        self.mask_blur = 0
        self.inpaint_full_res_padding = 0
        self.width = init_img.width
        self.height = init_img.height

        # ComfyUI Sampler inputs
        self.model = model
        self.positive = positive
        self.negative = negative
        self.vae = vae
        self.seed = seed
        self.steps = steps
        self.cfg = cfg
        self.sampler_name = sampler_name
        self.scheduler = scheduler
        self.denoise = denoise

        # Variables used only by this script
        self.init_size = init_img.width, init_img.height
        self.upscale_by = upscale_by
        self.force_uniform_tile_size = force_uniform_tile_size == "enable"

        # Other required A1111 variables for the USDU script that is currently unused in this script
        self.extra_generation_params = {}


class Processed:

    def __init__(self, p: StableDiffusionProcessing, images: list, seed: int, info: str):
        self.images = images
        self.seed = seed
        self.info = info

    def infotext(self, p: StableDiffusionProcessing, index):
        return None


def fix_seed(p: StableDiffusionProcessing):
    pass


def process_images(p: StableDiffusionProcessing) -> Processed:
    # Where the main image generation happens in A1111

    # Setup
    image_mask = p.image_mask.convert('L')
    init_image = p.init_images[0]

    # Locate the white region of the mask outlining the tile and add padding
    crop_region = get_crop_region(image_mask, p.inpaint_full_res_padding)
    crop_region, tile_size = expand_crop(crop_region, image_mask.width, image_mask.height)

    # Blur the mask
    if p.mask_blur > 0:
        image_mask = image_mask.filter(ImageFilter.GaussianBlur(p.mask_blur))

    # Crop the images to get the tiles that will be used for generation
    tiles = [img.crop(crop_region) for img in shared.batch]
    initial_tile_size = tiles[0].size
    w_pad = 0
    h_pad = 0
    for i in range(len(tiles)):
        if tiles[i].size != tile_size:
            tiles[i] = tiles[i].resize(tile_size, Image.Resampling.LANCZOS)

        if p.force_uniform_tile_size:
            # Pad the tile to center it in an image of size (p.width, p.height)
            w_pad = (p.width - tile_size[0]) // 2
            h_pad = (p.height - tile_size[1]) // 2
            tiles[i] = pad_image(tiles[i], left_pad=w_pad, right_pad=w_pad,
                                 top_pad=h_pad, bottom_pad=h_pad, fill=True, blur=True)

    # Crop conditioning
    positive_cropped = crop_cond(p.positive, crop_region, p.init_size, init_image.size, tile_size, w_pad, h_pad)
    negative_cropped = crop_cond(p.negative, crop_region, p.init_size, init_image.size, tile_size, w_pad, h_pad)

    # Encode the image
    vae_encoder = VAEEncode()
    batched_tiles = torch.cat([pil_to_tensor(tile) for tile in tiles], dim=0)
    (latent,) = vae_encoder.encode(p.vae, batched_tiles)

    # Generate samples
    (samples,) = common_ksampler(p.model, p.seed, p.steps, p.cfg, p.sampler_name,
                                 p.scheduler, positive_cropped, negative_cropped, latent, denoise=p.denoise)

    # Decode the sample
    vae_decoder = VAEDecode()
    (decoded,) = vae_decoder.decode(p.vae, samples)

    # Convert the sample to a PIL image
    tiles_sampled = [tensor_to_pil(decoded, i) for i in range(len(decoded))]

    for i, tile_sampled in enumerate(tiles_sampled):
        init_image = shared.batch[i]

        if p.force_uniform_tile_size:
            # Crop out the padding from the samples
            tile_sampled = tile_sampled.crop((w_pad, h_pad, tile_sampled.width - w_pad, tile_sampled.height - h_pad))

        # Resize back to the original size
        if tile_sampled.size != initial_tile_size:
            tile_sampled = tile_sampled.resize(initial_tile_size, Image.Resampling.LANCZOS)

        # Put the tile into position
        image_tile_only = Image.new('RGBA', init_image.size)
        image_tile_only.paste(tile_sampled, crop_region[:2])

        # Add the mask as an alpha channel
        # Must make a copy due to the possibility of an edge becoming black
        temp = image_tile_only.copy()
        temp.putalpha(image_mask)
        image_tile_only.paste(temp, image_tile_only)

        # Add back the tile to the initial image according to the mask in the alpha channel
        result = init_image.convert('RGBA')
        result.alpha_composite(image_tile_only)

        # Convert back to RGB
        result = result.convert('RGB')

        shared.batch[i] = result

    processed = Processed(p, [shared.batch[0]], p.seed, None)
    return processed