import gradio as gr
import open_clip
import torch
from PIL import Image
from open_clip import tokenizer
from rudalle import get_vae
from einops import rearrange
from modules import DenoiseUNet

model_id = "./model_600000.pt"
device = "cuda" if torch.cuda.is_available() else "cpu"

batch_size = 4
steps = 11
scale = 5


def to_pil(images):
    images = images.permute(0, 2, 3, 1).cpu().numpy()
    images = (images * 255).round().astype("uint8")
    images = [Image.fromarray(image) for image in images]
    return images
    
def log(t, eps=1e-20):
    return torch.log(t + eps)

def gumbel_noise(t):
    noise = torch.zeros_like(t).uniform_(0, 1)
    return -log(-log(noise))

def gumbel_sample(t, temperature=1., dim=-1):
    return ((t / max(temperature, 1e-10)) + gumbel_noise(t)).argmax(dim=dim)

def sample(model, c, x=None, mask=None, T=12, size=(32, 32), starting_t=0, temp_range=[1.0, 1.0], typical_filtering=True, typical_mass=0.2, typical_min_tokens=1, classifier_free_scale=-1, renoise_steps=11, renoise_mode='start'):
    with torch.inference_mode():
        r_range = torch.linspace(0, 1, T+1)[:-1][:, None].expand(-1, c.size(0)).to(c.device)
        temperatures = torch.linspace(temp_range[0], temp_range[1], T)
        preds = []
        if x is None:
            x = torch.randint(0, model.num_labels, size=(c.size(0), *size), device=c.device)
        elif mask is not None:
            noise = torch.randint(0, model.num_labels, size=(c.size(0), *size), device=c.device)
            x = noise * mask + (1-mask) * x
        init_x = x.clone()
        for i in range(starting_t, T):
            if renoise_mode == 'prev':
                prev_x = x.clone()
            r, temp = r_range[i], temperatures[i]
            logits = model(x, c, r)
            if classifier_free_scale >= 0:
                logits_uncond = model(x, torch.zeros_like(c), r)
                logits = torch.lerp(logits_uncond, logits, classifier_free_scale)
            x = logits
            x_flat = x.permute(0, 2, 3, 1).reshape(-1, x.size(1))
            if typical_filtering:
                x_flat_norm = torch.nn.functional.log_softmax(x_flat, dim=-1)
                x_flat_norm_p = torch.exp(x_flat_norm)
                entropy = -(x_flat_norm * x_flat_norm_p).nansum(-1, keepdim=True)

                c_flat_shifted = torch.abs((-x_flat_norm) - entropy)
                c_flat_sorted, x_flat_indices = torch.sort(c_flat_shifted, descending=False)
                x_flat_cumsum = x_flat.gather(-1, x_flat_indices).softmax(dim=-1).cumsum(dim=-1)

                last_ind = (x_flat_cumsum < typical_mass).sum(dim=-1)
                sorted_indices_to_remove = c_flat_sorted > c_flat_sorted.gather(1, last_ind.view(-1, 1))
                if typical_min_tokens > 1:
                    sorted_indices_to_remove[..., :typical_min_tokens] = 0
                indices_to_remove = sorted_indices_to_remove.scatter(1, x_flat_indices, sorted_indices_to_remove)
                x_flat = x_flat.masked_fill(indices_to_remove, -float("Inf"))
            # x_flat = torch.multinomial(x_flat.div(temp).softmax(-1), num_samples=1)[:, 0]
            x_flat = gumbel_sample(x_flat, temperature=temp)
            x = x_flat.view(x.size(0), *x.shape[2:])
            if mask is not None:
                x = x * mask + (1-mask) * init_x
            if i < renoise_steps:
                if renoise_mode == 'start':
                    x, _ = model.add_noise(x, r_range[i+1], random_x=init_x)
                elif renoise_mode == 'prev':
                    x, _ = model.add_noise(x, r_range[i+1], random_x=prev_x)
                else: # 'rand'
                    x, _ = model.add_noise(x, r_range[i+1])
            preds.append(x.detach())
    return preds

# Model loading

vqmodel = get_vae().to(device)
vqmodel.eval().requires_grad_(False)

clip_model, _, _ = open_clip.create_model_and_transforms('ViT-g-14', pretrained='laion2b_s12b_b42k')
clip_model = clip_model.to(device).eval().requires_grad_(False)

def encode(x):
    return vqmodel.model.encode((2 * x - 1))[-1][-1]
    
def decode(img_seq, shape=(32,32)):
        img_seq = img_seq.view(img_seq.shape[0], -1)
        b, n = img_seq.shape
        one_hot_indices = torch.nn.functional.one_hot(img_seq, num_classes=vqmodel.num_tokens).float()
        z = (one_hot_indices @ vqmodel.model.quantize.embed.weight)
        z = rearrange(z, 'b (h w) c -> b c h w', h=shape[0], w=shape[1])
        img = vqmodel.model.decode(z)
        img = (img.clamp(-1., 1.) + 1) * 0.5
        return img
    
state_dict = torch.load(model_id, map_location=device)
model = DenoiseUNet(num_labels=8192).to(device)
model.load_state_dict(state_dict)
model.eval().requires_grad_()

# -----

def infer(prompt):
    latent_shape = (32, 32)
    tokenized_text = tokenizer.tokenize([prompt] * batch_size).to(device)
    with torch.inference_mode():
        with torch.autocast(device_type="cuda"):
            clip_embeddings = clip_model.encode_text(tokenized_text)
            images = sample(
                model, clip_embeddings, T=12, size=latent_shape, starting_t=0, temp_range=[1.0, 1.0],
                typical_filtering=True, typical_mass=0.2, typical_min_tokens=1,
                classifier_free_scale=scale, renoise_steps=steps, renoise_mode="start"
            )
        images = decode(images[-1], latent_shape)    
    return to_pil(images)
    
css = """
        .gradio-container {
            font-family: 'IBM Plex Sans', sans-serif;
        }
        .gr-button {
            color: white;
            border-color: black;
            background: black;
        }
        input[type='range'] {
            accent-color: black;
        }
        .dark input[type='range'] {
            accent-color: #dfdfdf;
        }
        .container {
            max-width: 730px;
            margin: auto;
            padding-top: 1.5rem;
        }
        #gallery {
            min-height: 22rem;
            margin-bottom: 15px;
            margin-left: auto;
            margin-right: auto;
            border-bottom-right-radius: .5rem !important;
            border-bottom-left-radius: .5rem !important;
        }
        #gallery>div>.h-full {
            min-height: 20rem;
        }
        .details:hover {
            text-decoration: underline;
        }
        .gr-button {
            white-space: nowrap;
        }
        .gr-button:focus {
            border-color: rgb(147 197 253 / var(--tw-border-opacity));
            outline: none;
            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
            --tw-border-opacity: 1;
            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
            --tw-ring-opacity: .5;
        }
        .footer {
            margin-bottom: 45px;
            margin-top: 35px;
            text-align: center;
            border-bottom: 1px solid #e5e5e5;
        }
        .footer>p {
            font-size: .8rem;
            display: inline-block;
            padding: 0 10px;
            transform: translateY(10px);
            background: white;
        }
        .dark .footer {
            border-color: #303030;
        }
        .dark .footer>p {
            background: #0b0f19;
        }
        .acknowledgments h4{
            margin: 1.25em 0 .25em 0;
            font-weight: bold;
            font-size: 115%;
        }
        .animate-spin {
            animation: spin 1s linear infinite;
        }
        @keyframes spin {
            from {
                transform: rotate(0deg);
            }
            to {
                transform: rotate(360deg);
            }
        }
        #share-btn-container {
            display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
        }
        #share-btn {
            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
        }
        #share-btn * {
            all: unset;
        }
        .gr-form{
            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
        }
        #prompt-container{
            gap: 0;
        }
"""

block = gr.Blocks(css=css)

with block:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
              >
                <svg
                  width="0.65em"
                  height="0.65em"
                  viewBox="0 0 115 115"
                  fill="none"
                  xmlns="http://www.w3.org/2000/svg"
                >
                  <rect width="23" height="23" fill="white"></rect>
                  <rect y="69" width="23" height="23" fill="white"></rect>
                  <rect x="23" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="46" width="23" height="23" fill="white"></rect>
                  <rect x="46" y="69" width="23" height="23" fill="white"></rect>
                  <rect x="69" width="23" height="23" fill="black"></rect>
                  <rect x="69" y="69" width="23" height="23" fill="black"></rect>
                  <rect x="92" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="115" y="46" width="23" height="23" fill="white"></rect>
                  <rect x="115" y="115" width="23" height="23" fill="white"></rect>
                  <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="92" y="69" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="46" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="115" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="46" y="46" width="23" height="23" fill="black"></rect>
                  <rect x="46" y="115" width="23" height="23" fill="black"></rect>
                  <rect x="46" y="69" width="23" height="23" fill="black"></rect>
                  <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="23" y="69" width="23" height="23" fill="black"></rect>
                </svg>
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  Paella Demo
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                Paella is a novel text-to-image model that uses a compressed quantized latent space, based on a f8 VQGAN, and a masked training objective to achieve fast generation in ~10 inference steps.
              </p>
            </div>
        """
    )
    with gr.Group():
        with gr.Box():
            with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
                text = gr.Textbox(
                    label="Enter your prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                    elem_id="prompt-text-input",
                ).style(
                    border=(True, False, True, True),
                    rounded=(True, False, False, True),
                    container=False,
                )
                btn = gr.Button("Generate image").style(
                    margin=False,
                    rounded=(False, True, True, False),
                    full_width=False,
                )

        gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(grid=[2], height="auto")

        text.submit(infer, inputs=text, outputs=gallery)
        btn.click(infer, inputs=text, outputs=gallery)

        gr.HTML(
            """
                <div class="footer">
                </div>
                <div class="acknowledgments">
                    <p><h4>Resources</h4>
                    <a href="https://arxiv.org/abs/2211.07292" style="text-decoration: underline;">Paper</a>, <a href="https://github.com/dome272/Paella" style="text-decoration: underline;">official implementation</a>.
                    </p>
                    <p><h4>LICENSE</h4>
                    <a href="https://github.com/dome272/Paella/blob/main/LICENSE" style="text-decoration: underline;">MIT</a>.
                    </p>
                    <p><h4>Biases and content acknowledgment</h4>
Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on 600 million images from the improved <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B aesthetic</a> dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes.
                    </p>
               </div>
           """
        )

block.launch()