import math

import modules.scripts as scripts
import gradio as gr
from PIL import Image

from modules import processing, shared, sd_samplers, images, devices
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state


class Script(scripts.Script):
    def title(self):
        return "SD upscale"

    def show(self, is_img2img):
        return is_img2img

    def ui(self, is_img2img):
        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
        scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0)
        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")

        return [info, overlap, upscaler_index, scale_factor]

    def run(self, p, _, overlap, upscaler_index, scale_factor):
        processing.fix_seed(p)
        upscaler = shared.sd_upscalers[upscaler_index]

        p.extra_generation_params["SD upscale overlap"] = overlap
        p.extra_generation_params["SD upscale upscaler"] = upscaler.name

        initial_info = None
        seed = p.seed

        init_img = p.init_images[0]
        init_img = images.flatten(init_img, opts.img2img_background_color)

        if upscaler.name != "None":
            img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
        else:
            img = init_img

        devices.torch_gc()

        grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)

        batch_size = p.batch_size
        upscale_count = p.n_iter
        p.n_iter = 1
        p.do_not_save_grid = True
        p.do_not_save_samples = True

        work = []

        for y, h, row in grid.tiles:
            for tiledata in row:
                work.append(tiledata[2])

        batch_count = math.ceil(len(work) / batch_size)
        state.job_count = batch_count * upscale_count

        print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")

        result_images = []
        for n in range(upscale_count):
            start_seed = seed + n
            p.seed = start_seed

            work_results = []
            for i in range(batch_count):
                p.batch_size = batch_size
                p.init_images = work[i * batch_size:(i + 1) * batch_size]

                state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
                processed = processing.process_images(p)

                if initial_info is None:
                    initial_info = processed.info

                p.seed = processed.seed + 1
                work_results += processed.images

            image_index = 0
            for y, h, row in grid.tiles:
                for tiledata in row:
                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
                    image_index += 1

            combined_image = images.combine_grid(grid)
            result_images.append(combined_image)

            if opts.samples_save:
                images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)

        processed = Processed(p, result_images, seed, initial_info)

        return processed