import torch
import numpy as np
from torchvision.transforms.functional import to_tensor
from PIL import Image

def blue_loss(images):
    """
    Custom loss function to penalize or encourage the presence of blue hues in the images.
    """
    # Convert images to tensors
    images_tensor = torch.tensor(images).float() / 255.0
    
    # Extract the blue channel (last channel in RGB)
    blue_channel = images_tensor[:, :, :, 2]
    
    # Calculate variance of the blue channel
    variance = torch.var(blue_channel)
    
    # Return negative variance as the loss (penalize less blue)
    return -variance

def generate_with_prompt_style_guidance(prompt, style, seed=42):
    prompt = prompt + ' in style of s'
    
    embed = torch.load(style)

    height = 512
    width = 512
    num_inference_steps = 10
    guidance_scale = 8
    generator = torch.manual_seed(seed)
    batch_size = 1
    contrast_loss_scale = 200
    blue_loss_scale = 100  # Scale for blue loss

    # Prep text
    text_input = tokenizer([prompt], padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
    with torch.no_grad():
        text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]

    input_ids = text_input.input_ids.to(torch_device)

    # Get token embeddings
    token_embeddings = token_emb_layer(input_ids)

    # The new embedding - our special birb word
    replacement_token_embedding = embed[list(embed.keys())[0]].to(torch_device)

    # Insert this into the token embeddings
    token_embeddings[0, torch.where(input_ids[0] == 338)] = replacement_token_embedding.to(torch_device)

    # Combine with pos embs
    input_embeddings = token_embeddings + position_embeddings

    # Feed through to get final output embs
    modified_output_embeddings = get_output_embeds(input_embeddings)

    # And the uncond. input as before:
    max_length = text_input.input_ids.shape[-1]
    uncond_input = tokenizer(
        [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
    )
    with torch.no_grad():
        uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]

    text_embeddings = torch.cat([uncond_embeddings, modified_output_embeddings])

    # Prep Scheduler
    scheduler.set_timesteps(num_inference_steps)

    # Prep latents
    latents = torch.randn(
        (batch_size, unet.config.in_channels, height // 8, width // 8),
        generator=generator,
    )
    latents = latents.to(torch_device)
    latents = latents * scheduler.init_noise_sigma

    # Loop
    for i, t in tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
        # Expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
        latent_model_input = torch.cat([latents] * 2)
        sigma = scheduler.sigmas[i]
        latent_model_input = scheduler.scale_model_input(latent_model_input, t)

        # Predict the noise residual
        with torch.no_grad():
            noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]

        # Perform CFG
        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # Additional Guidance
        if i % 5 == 0:
            # Requires grad on the latents
            latents = latents.detach().requires_grad_()

            # Get the predicted x0
            latents_x0 = latents - sigma * noise_pred

            # Decode to image space
            denoised_images = vae.decode((1 / 0.18215) * latents_x0).sample / 2 + 0.5

            # Calculate losses
            contrast_loss_val = contrast_loss(denoised_images) * contrast_loss_scale
            blue_loss_val = blue_loss(denoised_images) * blue_loss_scale

            # Combine losses
            total_loss = contrast_loss_val + blue_loss_val

            # Get gradient
            cond_grad = torch.autograd.grad(total_loss, latents)[0]

            # Modify the latents based on this gradient
            latents = latents.detach() - cond_grad * sigma**2

        # Now step with scheduler
        latents = scheduler.step(noise_pred, t, latents).prev_sample

    return latents_to_pil(latents)[0]

import gradio as gr

dict_styles = { 
    'Dr Strange': 'styles/learned_embeds_dr_strange.bin', 
    'GTA-5':'styles/learned_embeds_gta5.bin', 
    'Manga':'styles/learned_embeds_manga.bin', 
    'Pokemon':'styles/learned_embeds_pokemon.bin', 
}

def inference(prompt, style): 
    if prompt is not None and style is not None: 
        style = dict_styles[style]
        result = generate_with_prompt_style_guidance(prompt, style)
        return np.array(result)
    else:
        return None

title = "Stable Diffusion and Textual Inversion"
description = "A simple Gradio interface to stylize Stable Diffusion outputs"
examples = [['A man sipping wine wearing a spacesuit on the moon']]

demo = gr.Interface(inference,
                    inputs=[gr.Textbox(label='Prompt'), 
                            gr.Dropdown(['Dr Strange', 'GTA-5', 'Manga', 'Pokemon'], label='Style')],
                    outputs=[gr.Image(label="Stable Diffusion Output")],
                    title=title,
                    description=description,
                    examples=examples)
demo.launch()