import os
from contextlib import closing
from pathlib import Path

import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
import gradio as gr

from modules import sd_samplers, images as imgutil
from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
import modules.scripts


def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
    output_dir = output_dir.strip()
    processing.fix_seed(p)

    images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))

    is_inpaint_batch = False
    if inpaint_mask_dir:
        inpaint_masks = shared.listfiles(inpaint_mask_dir)
        is_inpaint_batch = bool(inpaint_masks)

        if is_inpaint_batch:
            print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")

    print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")

    state.job_count = len(images) * p.n_iter

    # extract "default" params to use in case getting png info fails
    prompt = p.prompt
    negative_prompt = p.negative_prompt
    seed = p.seed
    cfg_scale = p.cfg_scale
    sampler_name = p.sampler_name
    steps = p.steps

    for i, image in enumerate(images):
        state.job = f"{i+1} out of {len(images)}"
        if state.skipped:
            state.skipped = False

        if state.interrupted:
            break

        try:
            img = Image.open(image)
        except UnidentifiedImageError as e:
            print(e)
            continue
        # Use the EXIF orientation of photos taken by smartphones.
        img = ImageOps.exif_transpose(img)

        if to_scale:
            p.width = int(img.width * scale_by)
            p.height = int(img.height * scale_by)

        p.init_images = [img] * p.batch_size

        image_path = Path(image)
        if is_inpaint_batch:
            # try to find corresponding mask for an image using simple filename matching
            if len(inpaint_masks) == 1:
                mask_image_path = inpaint_masks[0]
            else:
                # try to find corresponding mask for an image using simple filename matching
                mask_image_dir = Path(inpaint_mask_dir)
                masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*"))

                if len(masks_found) == 0:
                    print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.")
                    continue

                # it should contain only 1 matching mask
                # otherwise user has many masks with the same name but different extensions
                mask_image_path = masks_found[0]

            mask_image = Image.open(mask_image_path)
            p.image_mask = mask_image

        if use_png_info:
            try:
                info_img = img
                if png_info_dir:
                    info_img_path = os.path.join(png_info_dir, os.path.basename(image))
                    info_img = Image.open(info_img_path)
                geninfo, _ = imgutil.read_info_from_image(info_img)
                parsed_parameters = parse_generation_parameters(geninfo)
                parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
            except Exception:
                parsed_parameters = {}

            p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "")
            p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "")
            p.seed = int(parsed_parameters.get("Seed", seed))
            p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale))
            p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
            p.steps = int(parsed_parameters.get("Steps", steps))

        proc = modules.scripts.scripts_img2img.run(p, *args)
        if proc is None:
            if output_dir:
                p.outpath_samples = output_dir
                p.override_settings['save_to_dirs'] = False
                if p.n_iter > 1 or p.batch_size > 1:
                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
                else:
                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
            process_images(p)


def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
    override_settings = create_override_settings_dict(override_settings_texts)

    is_batch = mode == 5

    if mode == 0:  # img2img
        image = init_img.convert("RGB")
        mask = None
    elif mode == 1:  # img2img sketch
        image = sketch.convert("RGB")
        mask = None
    elif mode == 2:  # inpaint
        image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
        mask = mask.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
        image = image.convert("RGB")
    elif mode == 3:  # inpaint sketch
        image = inpaint_color_sketch
        orig = inpaint_color_sketch_orig or inpaint_color_sketch
        pred = np.any(np.array(image) != np.array(orig), axis=-1)
        mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
        mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
        blur = ImageFilter.GaussianBlur(mask_blur)
        image = Image.composite(image.filter(blur), orig, mask.filter(blur))
        image = image.convert("RGB")
    elif mode == 4:  # inpaint upload mask
        image = init_img_inpaint
        mask = init_mask_inpaint
    else:
        image = None
        mask = None

    # Use the EXIF orientation of photos taken by smartphones.
    if image is not None:
        image = ImageOps.exif_transpose(image)

    if selected_scale_tab == 1 and not is_batch:
        assert image, "Can't scale by because no image is selected"

        width = int(image.width * scale_by)
        height = int(image.height * scale_by)

    assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'

    p = StableDiffusionProcessingImg2Img(
        sd_model=shared.sd_model,
        outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
        outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
        prompt=prompt,
        negative_prompt=negative_prompt,
        styles=prompt_styles,
        seed=seed,
        subseed=subseed,
        subseed_strength=subseed_strength,
        seed_resize_from_h=seed_resize_from_h,
        seed_resize_from_w=seed_resize_from_w,
        seed_enable_extras=seed_enable_extras,
        sampler_name=sd_samplers.samplers_for_img2img[sampler_index].name,
        batch_size=batch_size,
        n_iter=n_iter,
        steps=steps,
        cfg_scale=cfg_scale,
        width=width,
        height=height,
        restore_faces=restore_faces,
        tiling=tiling,
        init_images=[image],
        mask=mask,
        mask_blur=mask_blur,
        inpainting_fill=inpainting_fill,
        resize_mode=resize_mode,
        denoising_strength=denoising_strength,
        image_cfg_scale=image_cfg_scale,
        inpaint_full_res=inpaint_full_res,
        inpaint_full_res_padding=inpaint_full_res_padding,
        inpainting_mask_invert=inpainting_mask_invert,
        override_settings=override_settings,
    )

    p.scripts = modules.scripts.scripts_img2img
    p.script_args = args

    p.user = request.username

    if shared.cmd_opts.enable_console_prompts:
        print(f"\nimg2img: {prompt}", file=shared.progress_print_out)

    if mask:
        p.extra_generation_params["Mask blur"] = mask_blur

    with closing(p):
        if is_batch:
            assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"

            process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)

            processed = Processed(p, [], p.seed, "")
        else:
            processed = modules.scripts.scripts_img2img.run(p, *args)
            if processed is None:
                processed = process_images(p)

    shared.total_tqdm.clear()

    generation_info_js = processed.js()
    if opts.samples_log_stdout:
        print(generation_info_js)

    if opts.do_not_show_images:
        processed.images = []

    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
