import copy
import os
import random
import urllib.request

import numpy as np
import torch
import torch.nn.functional as FF
import torch.optim
from torchvision import utils
from tqdm import tqdm

from stylegan2.model import Generator


class DownloadProgressBar(tqdm):
    def update_to(self, b=1, bsize=1, tsize=None):
        if tsize is not None:
            self.total = tsize
        self.update(b * bsize - self.n)


def get_path(base_path):
    BASE_DIR = os.path.join('checkpoints')

    save_path = os.path.join(BASE_DIR, base_path)
    if not os.path.exists(save_path):
        url = f"https://huggingface.co/aaronb/StyleGAN2/resolve/main/{base_path}"
        print(f'{base_path} not found')
        print('Try to download from huggingface: ', url)
        os.makedirs(os.path.dirname(save_path), exist_ok=True)
        download_url(url, save_path)
        print('Downloaded to ', save_path)
    return save_path


def download_url(url, output_path):
    with DownloadProgressBar(unit='B', unit_scale=True,
                             miniters=1, desc=url.split('/')[-1]) as t:
        urllib.request.urlretrieve(url, filename=output_path, reporthook=t.update_to)


class CustomGenerator(Generator):
    def prepare(
        self,
        styles,
        inject_index=None,
        truncation=1,
        truncation_latent=None,
        input_is_latent=False,
        noise=None,
        randomize_noise=True,
    ):
        if not input_is_latent:
            styles = [self.style(s) for s in styles]

        if noise is None:
            if randomize_noise:
                noise = [None] * self.num_layers
            else:
                noise = [
                    getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
                ]

        if truncation < 1:
            style_t = []

            for style in styles:
                style_t.append(
                    truncation_latent + truncation * (style - truncation_latent)
                )

            styles = style_t

        if len(styles) < 2:
            inject_index = self.n_latent

            if styles[0].ndim < 3:
                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)

            else:
                latent = styles[0]

        else:
            if inject_index is None:
                inject_index = random.randint(1, self.n_latent - 1)

            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
            latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)

            latent = torch.cat([latent, latent2], 1)

        return latent, noise

    def generate(
        self,
        latent,
        noise,
    ):
        out = self.input(latent)
        out = self.conv1(out, latent[:, 0], noise=noise[0])

        skip = self.to_rgb1(out, latent[:, 1])
        i = 1
        for conv1, conv2, noise1, noise2, to_rgb in zip(
            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
        ):
            out = conv1(out, latent[:, i], noise=noise1)
            out = conv2(out, latent[:, i + 1], noise=noise2)
            skip = to_rgb(out, latent[:, i + 2], skip)
            if out.shape[-1] == 256: F = out
            i += 2

        image = skip
        F = FF.interpolate(F, image.shape[-2:], mode='bilinear')
        return image, F


def stylegan2(
    size=1024,
    channel_multiplier=2,
    latent=512,
    n_mlp=8,
    ckpt='stylegan2-ffhq-config-f.pt'
):
    g_ema = CustomGenerator(size, latent, n_mlp, channel_multiplier=channel_multiplier)
    checkpoint = torch.load(get_path(ckpt))
    g_ema.load_state_dict(checkpoint["g_ema"], strict=False)
    g_ema.requires_grad_(False)
    g_ema.eval()
    return g_ema


def bilinear_interpolate_torch(im, y, x):
    """
    im : B,C,H,W
    y : 1,numPoints -- pixel location y float
    x : 1,numPOints -- pixel location y float
    """

    x0 = torch.floor(x).long()
    x1 = x0 + 1

    y0 = torch.floor(y).long()
    y1 = y0 + 1

    wa = (x1.float() - x) * (y1.float() - y)
    wb = (x1.float() - x) * (y - y0.float())
    wc = (x - x0.float()) * (y1.float() - y)
    wd = (x - x0.float()) * (y - y0.float())
    # Instead of clamp
    x1 = x1 - torch.floor(x1 / im.shape[3]).int()
    y1 = y1 - torch.floor(y1 / im.shape[2]).int()
    Ia = im[:, :, y0, x0]
    Ib = im[:, :, y1, x0]
    Ic = im[:, :, y0, x1]
    Id = im[:, :, y1, x1]

    return Ia * wa + Ib * wb + Ic * wc + Id * wd


def drag_gan(g_ema, latent: torch.Tensor, noise, F, handle_points, target_points, mask, max_iters=1000):
    handle_points0 = copy.deepcopy(handle_points)
    n = len(handle_points)
    r1, r2, lam, d = 3, 12, 20, 1

    def neighbor(x, y, d):
        points = []
        for i in range(x - d, x + d):
            for j in range(y - d, y + d):
                points.append(torch.tensor([i, j]).float().cuda())
        return points

    F0 = F.detach().clone()
    # latent = latent.detach().clone().requires_grad_(True)
    latent_trainable = latent[:, :6, :].detach().clone().requires_grad_(True)
    latent_untrainable = latent[:, 6:, :].detach().clone().requires_grad_(False)
    optimizer = torch.optim.Adam([latent_trainable], lr=2e-3)
    for iter in range(max_iters):
        for s in range(1):
            optimizer.zero_grad()
            latent = torch.cat([latent_trainable, latent_untrainable], dim=1)
            sample2, F2 = g_ema.generate(latent, noise)

            # motion supervision
            loss = 0
            for i in range(n):
                pi, ti = handle_points[i], target_points[i]
                di = (ti - pi) / torch.sum((ti - pi)**2)

                for qi in neighbor(int(pi[0]), int(pi[1]), r1):
                    # f1 = F[..., int(qi[0]), int(qi[1])]
                    # f2 = F2[..., int(qi[0] + di[0]), int(qi[1] + di[1])]
                    f1 = bilinear_interpolate_torch(F2, qi[0], qi[1]).detach()
                    f2 = bilinear_interpolate_torch(F2, qi[0] + di[0], qi[1] + di[1])
                    loss += FF.l1_loss(f2, f1)

            # loss += ((F-F0) * (1-mask)).abs().mean() * lam

            loss.backward()
            optimizer.step()

            print(latent_trainable[0, 0, :10])
            # if s % 10 ==0:
            #     utils.save_image(sample2, "test2.png", normalize=True, range=(-1, 1))

        # point tracking
        with torch.no_grad():
            sample2, F2 = g_ema.generate(latent, noise)
            for i in range(n):
                pi = handle_points0[i]
                # f = F0[..., int(pi[0]), int(pi[1])]
                f0 = bilinear_interpolate_torch(F0, pi[0], pi[1])
                minv = 1e9
                minx = 1e9
                miny = 1e9
                for qi in neighbor(int(handle_points[i][0]), int(handle_points[i][1]), r2):
                    # f2 = F2[..., int(qi[0]), int(qi[1])]
                    try:
                        f2 = bilinear_interpolate_torch(F2, qi[0], qi[1])
                    except:
                        import ipdb
                        ipdb.set_trace()
                    v = torch.norm(f2 - f0, p=1)
                    if v < minv:
                        minv = v
                        minx = int(qi[0])
                        miny = int(qi[1])
                handle_points[i][0] = minx
                handle_points[i][1] = miny

        F = F2.detach().clone()
        if iter % 1 == 0:
            print(iter, loss.item(), handle_points, target_points)
            # p = handle_points[0].int()
            # sample2[0, :, p[0] - 5:p[0] + 5, p[1] - 5:p[1] + 5] = sample2[0, :, p[0] - 5:p[0] + 5, p[1] - 5:p[1] + 5] * 0
            # t = target_points[0].int()
            # sample2[0, :, t[0] - 5:t[0] + 5, t[1] - 5:t[1] + 5] = sample2[0, :, t[0] - 5:t[0] + 5, t[1] - 5:t[1] + 5] * 255

            # sample2[0, :, 210, 134] = sample2[0, :, 210, 134] * 0
            utils.save_image(sample2, "test2.png", normalize=True, range=(-1, 1))

        yield sample2, latent, F2