import subprocess
from pathlib import Path

import einops
import gradio as gr
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision.utils import save_image


class Generator(nn.Module):
    def __init__(self, num_channels=4, latent_dim=100, hidden_size=64):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d(latent_dim, hidden_size * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(hidden_size * 8),
            nn.ReLU(True),
            # state size. (hidden_size*8) x 4 x 4
            nn.ConvTranspose2d(hidden_size * 8, hidden_size * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(hidden_size * 4),
            nn.ReLU(True),
            # state size. (hidden_size*4) x 8 x 8
            nn.ConvTranspose2d(hidden_size * 4, hidden_size * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(hidden_size * 2),
            nn.ReLU(True),
            # state size. (hidden_size*2) x 16 x 16
            nn.ConvTranspose2d(hidden_size * 2, hidden_size, 4, 2, 1, bias=False),
            nn.BatchNorm2d(hidden_size),
            nn.ReLU(True),
            # state size. (hidden_size) x 32 x 32
            nn.ConvTranspose2d(hidden_size, num_channels, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (num_channels) x 64 x 64
        )

    def forward(self, noise):
        pixel_values = self.model(noise)

        return pixel_values

model = Generator()
weights_path = hf_hub_download('huggingnft/dooggies', 'pytorch_model.bin')
model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))


@torch.no_grad()
def interpolate(save_dir='./lerp/', frames=100, rows=8, cols=8):
    save_dir = Path(save_dir)
    save_dir.mkdir(exist_ok=True, parents=True)

    z1 = torch.randn(rows * cols, 100, 1, 1)
    z2 = torch.randn(rows * cols, 100, 1, 1)

    zs = []
    for i in range(frames):
        alpha = i / frames
        z = (1 - alpha) * z1 + alpha * z2
        zs.append(z)

    zs += zs[::-1]  # also go in reverse order to complete loop

    frames = []
    for i, z in enumerate(zs):
        imgs = model(z)

        save_image(imgs, save_dir / f"{i:03}.png", normalize=True)
        img = Image.open(save_dir / f"{i:03}.png").convert('RGBA')
        img.putalpha(255)
        frames.append(img)
        img.save(save_dir / f"{i:03}.png")
    frames[0].save("out.gif", format="GIF", append_images=frames,
                   save_all=True, duration=100, loop=1)


def predict(choice, seed):
    torch.manual_seed(seed)

    if choice == 'interpolation':
        interpolate()
        return 'out.gif'
    else:
        z = torch.randn(64, 100, 1, 1)
        punks = model(z)
        save_image(punks, "image.png", normalize=True)
        img = Image.open(f"image.png").convert('RGBA')
        img.putalpha(255)
        img.save("image.png")
        return 'image.png'


gr.Interface(
    predict,
    inputs=[
        gr.inputs.Dropdown(['image', 'interpolation'], label='Output Type'),
        gr.inputs.Slider(label='Seed', minimum=0, maximum=1000, default=42),
    ],
    outputs="image",
    title="Cryptopunks GAN",
    description="These CryptoPunks do not exist. You have the choice of either generating random punks, or a gif showing the interpolation between two random punk grids.",
    article="<p style='text-align: center'><a href='https://arxiv.org/pdf/1511.06434.pdf'>Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a> | <a href='https://github.com/teddykoker/cryptopunks-gan'>Github Repo</a></p>",
    examples=[["interpolation", 123], ["interpolation", 42], ["image", 456], ["image", 42]],
).launch(cache_examples=True)