import argparse, os, sys, glob
import torch
import torch.nn as nn
import numpy as np
import gradio as gr
from omegaconf import OmegaConf
from PIL import Image, ImageFont, ImageDraw
from itertools import islice
from einops import rearrange, repeat
from torch import autocast
from contextlib import contextmanager, nullcontext
import mimetypes
import random
import math

import k_diffusion as K
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.

    from transformers import logging
    logging.set_verbosity_error()
except:
    pass

# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')

# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8

invalid_filename_chars = '<>:"/\|?*\n'

parser = argparse.ArgumentParser()
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",)
parser.add_argument("--skip_save", action='store_true', help="do not save indiviual samples. For speed measurements.",)
parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",)
parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default='./GFPGAN')
opt = parser.parse_args()

GFPGAN_dir = opt.gfpgan_dir


def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model.cuda()
    model.eval()
    return model


class CFGDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale):
        x_in = torch.cat([x] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        return uncond + (cond - uncond) * cond_scale


class KDiffusionSampler:
    def __init__(self, m):
        self.model = m
        self.model_wrap = K.external.CompVisDenoiser(m)

    def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
        sigmas = self.model_wrap.get_sigmas(S)
        x = x_T * sigmas[0]
        model_wrap_cfg = CFGDenoiser(self.model_wrap)
        samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)

        return samples_ddim, None


def create_random_tensors(seed, shape, count, same_seed=False):
    xs = []
    for i in range(count):
        current_seed = seed if same_seed else seed + i
        torch.manual_seed(current_seed)
        xs.append(torch.randn(shape, device=device))
    x = torch.stack(xs)
    return x


def load_GFPGAN():
    model_name = 'GFPGANv1.3'
    model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
    if not os.path.isfile(model_path):
        raise Exception("GFPGAN model not found at path "+model_path)

    sys.path.append(os.path.abspath(GFPGAN_dir))
    from gfpgan import GFPGANer

    return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)


GFPGAN = None
if os.path.exists(GFPGAN_dir):
    try:
        GFPGAN = load_GFPGAN()
        print("Loaded GFPGAN")
    except Exception:
        import traceback
        print("Error loading GFPGAN:", file=sys.stderr)
        print(traceback.format_exc(), file=sys.stderr)

config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.half().to(device)


def image_grid(imgs, batch_size, round_down=False):
    if opt.n_rows > 0:
        rows = opt.n_rows
    elif opt.n_rows == 0:
        rows = batch_size
    else:
        rows = math.sqrt(len(imgs))
        rows = int(rows) if round_down else round(rows)

    cols = math.ceil(len(imgs) / rows)

    w, h = imgs[0].size
    grid = Image.new('RGB', size=(cols * w, rows * h), color='black')

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))

    return grid


def draw_prompt_matrix(im, width, height, all_prompts):
    def wrap(text, d, font, line_length):
        lines = ['']
        for word in text.split():
            line = f'{lines[-1]} {word}'.strip()
            if d.textlength(line, font=font) <= line_length:
                lines[-1] = line
            else:
                lines.append(word)
        return '\n'.join(lines)

    def draw_texts(pos, x, y, texts, sizes):
        for i, (text, size) in enumerate(zip(texts, sizes)):
            active = pos & (1 << i) != 0

            if not active:
                text = '\u0336'.join(text) + '\u0336'

            d.multiline_text((x, y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center")

            y += size[1] + line_spacing

    fontsize = (width + height) // 25
    line_spacing = fontsize // 2
    fnt = ImageFont.truetype("arial.ttf", fontsize)
    color_active = (0, 0, 0)
    color_inactive = (153, 153, 153)

    pad_top = height // 4
    pad_left = width * 3 // 4

    cols = im.width // width
    rows = im.height // height

    prompts = all_prompts[1:]

    result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
    result.paste(im, (pad_left, pad_top))

    d = ImageDraw.Draw(result)

    boundary = math.ceil(len(prompts) / 2)
    prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
    prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]

    sizes_hor = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]]
    sizes_ver = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]]
    hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
    ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing

    for col in range(cols):
        x = pad_left + width * col + width / 2
        y = pad_top / 2 - hor_text_height / 2

        draw_texts(col, x, y, prompts_horiz, sizes_hor)

    for row in range(rows):
        x = pad_left / 2
        y = pad_top + height * row + height / 2 - ver_text_height / 2

        draw_texts(row, x, y, prompts_vert, sizes_ver)

    return result


def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
    torch.cuda.empty_cache()

    outpath = opt.outdir or "outputs/txt2img-samples"

    if seed == -1:
        seed = random.randrange(4294967294)

    seed = int(seed)
    keep_same_seed = False

    if sampler_name == 'PLMS':
        sampler = PLMSSampler(model)
    elif sampler_name == 'DDIM':
        sampler = DDIMSampler(model)
    elif sampler_name == 'k-diffusion':
        sampler = KDiffusionSampler(model)
    else:
        raise Exception("Unknown sampler: " + sampler_name)

    os.makedirs(outpath, exist_ok=True)

    batch_size = n_samples

    assert prompt is not None
    prompts = batch_size * [prompt]

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))
    grid_count = len(os.listdir(outpath)) - 1

    prompt_matrix_prompts = []
    prompt_matrix_parts = []
    if prompt_matrix:
        keep_same_seed = True

        prompt_matrix_parts = prompt.split("|")
        combination_count = 2 ** (len(prompt_matrix_parts)-1)
        for combination_num in range(combination_count):
            current = prompt_matrix_parts[0]

            for n, text in enumerate(prompt_matrix_parts[1:]):
                if combination_num & (2**n) > 0:
                    current += ("" if text.strip().startswith(",") else ", ") + text

            prompt_matrix_prompts.append(current)
        n_iter = math.ceil(len(prompt_matrix_prompts) / batch_size)

        print(f"Prompt matrix will create {len(prompt_matrix_prompts)} images using a total of {n_iter} batches.")

    precision_scope = autocast if opt.precision == "autocast" else nullcontext
    output_images = []
    with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
        for n in range(n_iter):
            if prompt_matrix:
                prompts = prompt_matrix_prompts[n*batch_size:(n+1)*batch_size]

            uc = None
            if cfg_scale != 1.0:
                uc = model.get_learned_conditioning(len(prompts) * [""])
            if isinstance(prompts, tuple):
                prompts = list(prompts)
            c = model.get_learned_conditioning(prompts)
            shape = [opt_C, height // opt_f, width // opt_f]

            batch_seed = seed if keep_same_seed else seed + n * len(prompts)

            # we manually generate all input noises because each one should have a specific seed
            x = create_random_tensors(batch_seed, shape, count=len(prompts), same_seed=keep_same_seed)

            samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=len(prompts), shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=x)

            x_samples_ddim = model.decode_first_stage(samples_ddim)
            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

            if prompt_matrix or not opt.skip_save or not opt.skip_grid:
                for i, x_sample in enumerate(x_samples_ddim):
                    x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                    x_sample = x_sample.astype(np.uint8)

                    if use_GFPGAN and GFPGAN is not None:
                        cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
                        x_sample = restored_img

                    image = Image.fromarray(x_sample)
                    filename = f"{base_count:05}-{seed if keep_same_seed else batch_seed + i}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"

                    image.save(os.path.join(sample_path, filename))

                    output_images.append(image)
                    base_count += 1

        if prompt_matrix or not opt.skip_grid:
            grid = image_grid(output_images, batch_size, round_down=prompt_matrix)

            if prompt_matrix:
                grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
                output_images.insert(0, grid)

            grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
            grid_count += 1

    del sampler

    info = f"""
{prompt}
Steps: {ddim_steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
    """.strip()

    return output_images, seed, info

class Flagging(gr.FlaggingCallback):

    def setup(self, components, flagging_dir: str):
        pass

    def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
        import csv

        os.makedirs("log/images", exist_ok=True)

        # those must match the "dream" function
        prompt, ddim_steps, sampler_name, use_GFPGAN, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, images, seed, comment = flag_data

        filenames = []

        with open("log/log.csv", "a", encoding="utf8", newline='') as file:
            import time
            import base64

            at_start = file.tell() == 0
            writer = csv.writer(file)
            if at_start:
                writer.writerow(["prompt", "seed", "width", "height", "cfgs", "steps", "filename"])

            filename_base = str(int(time.time() * 1000))
            for i, filedata in enumerate(images):
                filename = "log/images/"+filename_base + ("" if len(images) == 1 else "-"+str(i+1)) + ".png"

                if filedata.startswith("data:image/png;base64,"):
                    filedata = filedata[len("data:image/png;base64,"):]

                with open(filename, "wb") as imgfile:
                    imgfile.write(base64.decodebytes(filedata.encode('utf-8')))

                filenames.append(filename)

            writer.writerow([prompt, seed, width, height, cfg_scale, ddim_steps, filenames[0]])

        print("Logged:", filenames[0])


dream_interface = gr.Interface(
    dream,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
        gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
        gr.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"),
        gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
        gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
        gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
        gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
        gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
        gr.Number(label='Seed', value=-1),
        gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
        gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
    ],
    outputs=[
        gr.Gallery(label="Images"),
        gr.Number(label='Seed'),
        gr.Textbox(label="Copy-paste generation parameters"),
    ],
    title="Stable Diffusion Text-to-Image K",
    description="Generate images from text with Stable Diffusion (using K-LMS)",
    flagging_callback=Flagging()
)


def translation(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int):
    torch.cuda.empty_cache()

    outpath = opt.outdir or "outputs/img2img-samples"

    if seed == -1:
        seed = random.randrange(4294967294)

    model_wrap = K.external.CompVisDenoiser(model)

    os.makedirs(outpath, exist_ok=True)

    batch_size = n_samples

    assert prompt is not None

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))
    grid_count = len(os.listdir(outpath)) - 1

    image = init_img.convert("RGB")
    image = image.resize((width, height), resample=Image.Resampling.LANCZOS)
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)

    output_images = []
    precision_scope = autocast if opt.precision == "autocast" else nullcontext
    with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
        init_image = 2. * image - 1.
        init_image = init_image.to(device)
        init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
        init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))  # move to latent space
        x0 = init_latent

        assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
        t_enc = int(denoising_strength * ddim_steps)

        for n in range(n_iter):
            prompts = batch_size * [prompt]

            uc = None
            if cfg_scale != 1.0:
                uc = model.get_learned_conditioning(batch_size * [""])
            if isinstance(prompts, tuple):
                prompts = list(prompts)
            c = model.get_learned_conditioning(prompts)

            batch_seed = seed + n * len(prompts)

            sigmas = model_wrap.get_sigmas(ddim_steps)
            noise = create_random_tensors(batch_seed, x0.shape[1:], count=len(prompts))
            noise = noise * sigmas[ddim_steps - t_enc - 1]

            xi = x0 + noise
            sigma_sched = sigmas[ddim_steps - t_enc - 1:]
            model_wrap_cfg = CFGDenoiser(model_wrap)
            extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}

            samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False)
            x_samples_ddim = model.decode_first_stage(samples_ddim)
            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

            if not opt.skip_save or not opt.skip_grid:
                for i, x_sample in enumerate(x_samples_ddim):
                    x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                    x_sample = x_sample.astype(np.uint8)

                    if use_GFPGAN and GFPGAN is not None:
                        cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
                        x_sample = restored_img

                    image = Image.fromarray(x_sample)
                    image.save(os.path.join(sample_path, f"{base_count:05}-{batch_seed+i}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"))

                    output_images.append(image)
                    base_count += 1

        if not opt.skip_grid:
            # additionally, save as grid
            grid = image_grid(output_images, batch_size)
            grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
            grid_count += 1

    return output_images, seed


# prompt, init_img, ddim_steps, plms, ddim_eta, n_iter, n_samples, cfg_scale, denoising_strength, seed

img2img_interface = gr.Interface(
    translation,
    inputs=[
        gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
        gr.Image(value="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg", source="upload", interactive=True, type="pil"),
        gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
        gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
        gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
        gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
        gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
        gr.Number(label='Seed', value=-1),
        gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
        gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
    ],
    outputs=[
        gr.Gallery(),
        gr.Number(label='Seed')
    ],
    title="Stable Diffusion Image-to-Image",
    description="Generate images from images with Stable Diffusion",
    allow_flagging="never",
)

interfaces = [
    (dream_interface, "txt2img"),
    (img2img_interface, "img2img")
]

def run_GFPGAN(image, strength):
    image = image.convert("RGB")

    cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
    res = Image.fromarray(restored_img)

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

    return res


if GFPGAN is not None:
    interfaces.append((gr.Interface(
        run_GFPGAN,
        inputs=[
            gr.Image(label="Source", source="upload", interactive=True, type="pil"),
            gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100),
        ],
        outputs=[
            gr.Image(label="Result"),
        ],
        title="GFPGAN",
        description="Fix faces on images",
        allow_flagging="never",
    ), "GFPGAN"))

demo = gr.TabbedInterface(interface_list=[x[0] for x in interfaces], tab_names=[x[1] for x in interfaces])

demo.launch()