import numpy as np
from PIL import Image

from modules import processing, shared, images
from modules.shared import opts
import modules.gfpgan_model
from modules.ui import plaintext_to_html
import modules.codeformer_model

cached_images = {}


def run_extras(image, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
    processing.torch_gc()

    image = image.convert("RGB")
    info = ""

    outpath = opts.outdir_samples or opts.outdir_extras_samples

    if gfpgan_visibility > 0:
        restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
        res = Image.fromarray(restored_img)

        if gfpgan_visibility < 1.0:
            res = Image.blend(image, res, gfpgan_visibility)

        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
        image = res

    if codeformer_visibility > 0:
        restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
        res = Image.fromarray(restored_img)

        if codeformer_visibility < 1.0:
            res = Image.blend(image, res, codeformer_visibility)

        info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility)}\n"
        image = res

    if upscaling_resize != 1.0:
        def upscale(image, scaler_index, resize):
            small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
            pixels = tuple(np.array(small).flatten().tolist())
            key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels

            c = cached_images.get(key)
            if c is None:
                upscaler = shared.sd_upscalers[scaler_index]
                c = upscaler.upscale(image, image.width * resize, image.height * resize)
                cached_images[key] = c

            return c

        info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
        res = upscale(image, extras_upscaler_1, upscaling_resize)

        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
            res2 = upscale(image, extras_upscaler_2, upscaling_resize)
            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
            res = Image.blend(res, res2, extras_upscaler_2_visibility)

        image = res

    while len(cached_images) > 2:
        del cached_images[next(iter(cached_images.keys()))]

    images.save_image(image, outpath, "", None, info=info, extension=opts.samples_format, short_filename=True, no_prompt=True, pnginfo_section_name="extras")

    return image, plaintext_to_html(info), ''


def run_pnginfo(image):
    info = ''
    for key, text in image.info.items():
        info += f"""
<div>
<p><b>{plaintext_to_html(str(key))}</b></p>
<p>{plaintext_to_html(str(text))}</p>
</div>
""".strip()+"\n"

    if len(info) == 0:
        message = "Nothing found in the image."
        info = f"<div><p>{message}<p></div>"

    return '', '', info