import gradio as gr
import os
import torch
from PIL import Image
from SDLens import HookedStableDiffusionXLPipeline
from SAE import SparseAutoencoder
from utils import add_feature_on_area
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from utils import add_feature_on_area, replace_with_feature
import threading

code_to_block = {
    "down.2.1": "unet.down_blocks.2.attentions.1",
    "mid.0": "unet.mid_block.attentions.0",
    "up.0.1": "unet.up_blocks.0.attentions.1",
    "up.0.0": "unet.up_blocks.0.attentions.0"
}
lock = threading.Lock()

def process_cache(cache, saes_dict):

    top_features_dict = {}
    sparse_maps_dict = {}

    for code in code_to_block.keys():
        block = code_to_block[code]
        sae = saes_dict[code]

        diff = cache["output"][block] - cache["input"][block]
        diff = diff.permute(0, 1, 3, 4, 2).squeeze(0).squeeze(0)
        with torch.no_grad():
            sparse_maps = sae.encode(diff)
        averages = torch.mean(sparse_maps, dim=(0, 1))

        top_features = torch.topk(averages, 10).indices

        top_features_dict[code] = top_features.cpu().tolist()
        sparse_maps_dict[code] = sparse_maps.cpu().numpy()

    return top_features_dict, sparse_maps_dict


def plot_image_heatmap(cache, block_select, radio):
    code = block_select.split()[0]
    feature = int(radio)
    block = code_to_block[code]
    
    heatmap = cache["heatmaps"][code][:, :, feature]
    heatmap = np.kron(heatmap, np.ones((32, 32)))
    image = cache["image"].convert("RGBA")
    
    jet = plt.cm.jet
    cmap = jet(np.arange(jet.N))
    cmap[:1, -1] = 0
    cmap[1:, -1] = 0.6
    cmap = ListedColormap(cmap)
    heatmap = (heatmap - np.min(heatmap)) / (np.max(heatmap) - np.min(heatmap))
    heatmap_rgba = cmap(heatmap)
    heatmap_image = Image.fromarray((heatmap_rgba * 255).astype(np.uint8))
    heatmap_with_transparency = Image.alpha_composite(image, heatmap_image)

    return heatmap_with_transparency


def create_prompt_part(pipe, saes_dict, demo):
    def image_gen(prompt):
        lock.acquire()
        try:
            images, cache = pipe.run_with_cache(
                prompt,
                positions_to_cache=list(code_to_block.values()),
                num_inference_steps=1,
                generator=torch.Generator(device="cpu").manual_seed(42),
                guidance_scale=0.0,
                save_input=True,
                save_output=True
            )
        finally:
            lock.release()
        
        top_features_dict, top_sparse_maps_dict = process_cache(cache, saes_dict)
        return images.images[0], {
            "image": images.images[0],
            "heatmaps": top_sparse_maps_dict,
            "features": top_features_dict
        }

    def update_radio(cache, block_select):
        code = block_select.split()[0]
        return gr.update(choices=cache["features"][code])

    def update_img(cache, block_select, radio):
        new_img = plot_image_heatmap(cache, block_select, radio)
        return new_img

    with gr.Tab("Explore", elem_classes="tabs") as explore_tab:
        cache = gr.State(value={
            "image": None,
            "heatmaps": None,
            "features": []
        })
        with gr.Row():
            with gr.Column(scale=7):
                with gr.Row(equal_height=True):
                    prompt_field = gr.Textbox(lines=1, label="Enter prompt here", value="A cinematic shot of a professor sloth wearing a tuxedo at a BBQ party and eathing a dish with peas.")
                    button = gr.Button("Generate", elem_classes="generate_button1")

                with gr.Row():
                    image = gr.Image(width=512, height=512, image_mode="RGB", label="Generated image")
            
            with gr.Column(scale=4):
                block_select = gr.Dropdown(
                    choices=["up.0.1 (style)", "down.2.1 (composition)", "up.0.0 (details)", "mid.0"], 
                    value="down.2.1 (composition)",
                    label="Select block", 
                    elem_id="block_select",
                    interactive=True
                )
                radio = gr.Radio(choices=[], label="Select a feature", interactive=True)
        
        button.click(image_gen, [prompt_field], outputs=[image, cache])
        cache.change(update_radio, [cache, block_select], outputs=[radio])
        block_select.select(update_radio, [cache, block_select], outputs=[radio])
        radio.select(update_img, [cache, block_select, radio], outputs=[image])
        demo.load(image_gen, [prompt_field], outputs=[image, cache])

    return explore_tab

def downsample_mask(image, factor):
    downsampled = image.reshape(
        (image.shape[0] // factor, factor,
        image.shape[1] // factor, factor)
    )
    downsampled = downsampled.mean(axis=(1, 3))
    return downsampled

def create_intervene_part(pipe: HookedStableDiffusionXLPipeline, saes_dict, means_dict, demo):
    def image_gen(prompt, num_steps):
        lock.acquire()
        try:
            images = pipe.run_with_hooks(
                prompt,
                position_hook_dict={},
                num_inference_steps=num_steps,
                generator=torch.Generator(device="cpu").manual_seed(42),
                guidance_scale=0.0
            )
        finally:
            lock.release()
        return images.images[0]

    def image_mod(prompt, block_str, brush_index, strength, num_steps, input_image):
        block = block_str.split(" ")[0]

        mask = (input_image["layers"][0] > 0)[:, :, -1].astype(float)
        mask = downsample_mask(mask, 32)
        mask = torch.tensor(mask, dtype=torch.float32, device="cuda")

        if mask.sum() == 0:
            gr.Info("No mask selected, please draw on the input image")

        def hook(module, input, output):
            return add_feature_on_area(
                saes_dict[block],
                brush_index,
                mask * means_dict[block][brush_index] * strength,
                module,
                input,
                output
            )

        lock.acquire()
        try:
            image = pipe.run_with_hooks(
                prompt,
                position_hook_dict={code_to_block[block]: hook},
                num_inference_steps=num_steps,
                generator=torch.Generator(device="cpu").manual_seed(42),
                guidance_scale=0.0
            ).images[0]
        finally:
            lock.release()
        return image

    def feature_icon(block_str, brush_index):
        block = block_str.split(" ")[0]
        if block in ["mid.0", "up.0.0"]:
            gr.Info("Note that Feature Icon works best with down.2.1 and up.0.1 blocks but feel free to explore", duration=3)

        def hook(module, input, output):
            return replace_with_feature(
                saes_dict[block],
                brush_index,
                means_dict[block][brush_index] * saes_dict[block].k,
                module,
                input,
                output
            )

        lock.acquire()
        try:
            image = pipe.run_with_hooks(
                "",
                position_hook_dict={code_to_block[block]: hook},
                num_inference_steps=1,
                generator=torch.Generator(device="cpu").manual_seed(42),
                guidance_scale=0.0
            ).images[0]
        finally:
            lock.release()
        return image

    with gr.Tab("Paint!", elem_classes="tabs") as intervene_tab:
        image_state = gr.State(value=None)
        with gr.Row():
            with gr.Column(scale=3):
                # Generation column
                with gr.Row():
                    # prompt and num_steps
                    prompt_field = gr.Textbox(lines=1, label="Enter prompt here", value="A dog plays with a ball, cartoon", elem_id="prompt_input")
                    num_steps = gr.Number(value=1, label="Number of steps", minimum=1, maximum=4, elem_id="num_steps", precision=0)
                with gr.Row():
                    # Generate button
                    button_generate = gr.Button("Generate", elem_id="generate_button")
            with gr.Column(scale=3):
                # Intervention column
                with gr.Row():
                    # dropdowns and number inputs
                    with gr.Column(scale=7):
                        with gr.Row():
                            block_select = gr.Dropdown(
                                choices=["up.0.1 (style)", "down.2.1 (composition)", "up.0.0 (details)", "mid.0"], 
                                value="down.2.1 (composition)",
                                label="Select block", 
                                elem_id="block_select"
                            )
                            brush_index = gr.Number(value=0, label="Brush index", minimum=0, maximum=5119, elem_id="brush_index", precision=0)
                        with gr.Row():
                            button_icon = gr.Button('Feature Icon', elem_id="feature_icon_button")
                    with gr.Column(scale=3):
                        with gr.Row():
                            strength = gr.Number(value=10, label="Strength", minimum=-40, maximum=40, elem_id="strength", precision=2)
                        with gr.Row():
                            button = gr.Button('Apply', elem_id="apply_button")

        with gr.Row():
            with gr.Column():
                # Input image
                i_image = gr.Sketchpad(
                    height=610,
                    layers=False, transforms=[], placeholder="Generate and paint!",
                    brush=gr.Brush(default_size=64, color_mode="fixed", colors=['black']),
                    container=False,
                    canvas_size=(512, 512),
                    label="Input Image")
                clear_button = gr.Button("Clear")
                clear_button.click(lambda x: x, [image_state], [i_image])
            # Output image
            o_image = gr.Image(width=512, height=512, label="Output Image")

        # Set up the click events
        button_generate.click(image_gen, inputs=[prompt_field, num_steps], outputs=[image_state])
        image_state.change(lambda x: x, [image_state], [i_image])
        button.click(image_mod, 
                    inputs=[prompt_field, block_select, brush_index, strength, num_steps, i_image], 
                    outputs=o_image)
        button_icon.click(feature_icon, inputs=[block_select, brush_index], outputs=o_image)
        demo.load(image_gen, [prompt_field, num_steps], outputs=[image_state])


    return intervene_tab


def create_top_images_part(demo):
    def update_top_images(block_select, brush_index):
        block = block_select.split(" ")[0]
        url = f"https://huggingface.co/surokpro2/sdxl_sae_images/resolve/main/{block}/{brush_index}.jpg"
        return url

    with gr.Tab("Top Images", elem_classes="tabs") as top_images_tab:
        with gr.Row():
            block_select = gr.Dropdown(
                choices=["up.0.1 (style)", "down.2.1 (composition)", "up.0.0 (details)", "mid.0"], 
                value="down.2.1 (composition)",
                label="Select block"
            )
            brush_index = gr.Number(value=0, label="Brush index", minimum=0, maximum=5119, precision=0)
        with gr.Row():
            image = gr.Image(width=600, height=600, label="Top Images")

        block_select.select(update_top_images, [block_select, brush_index], outputs=[image])
        brush_index.change(update_top_images, [block_select, brush_index], outputs=[image])
        demo.load(update_top_images, [block_select, brush_index], outputs=[image])
    return top_images_tab


def create_intro_part():
    with gr.Tab("Instructions", elem_classes="tabs") as intro_tab:
        gr.Markdown(
            '''# Unpacking SDXL Turbo with Sparse Autoencoders
            ## Demo Overview
            This demo showcases the use of Sparse Autoencoders (SAEs) to understand the features learned by the Stable Diffusion XL Turbo model. 
            
            ## How to Use
            ### Explore
            * Enter a prompt in the text box and click on the "Generate" button to generate an image.
            * You can observe the active features in different blocks plot on top of the generated image.
            ### Top Images
            * For each feature, you can view the top images that activate the feature the most.
            ### Paint!
            * Generate an image using the prompt.
            * Paint on the generated image to apply interventions.
            * Use the "Feature Icon" button to understand how the selected brush functions.

            ### Remarks
            * Not all brushes mix well with all images. Experiment with different brushes and strengths.
            * Feature Icon works best with `down.2.1 (composition)` and `up.0.1 (style)` blocks.
            * This demo is provided for research purposes only. We do not take responsibility for the content generated by the demo.

            ### Interesting features to try
            To get started, try the following features:
            - down.2.1 (composition): 2301 (evil) 3747 (image frame) 4998 (cartoon)
            - up.0.1 (style): 4977 (tiger stripes) 90 (fur) 2615 (twilight blur)
            '''
        )
    
    return intro_tab


def create_demo(pipe, saes_dict, means_dict):
    custom_css = """
    .tabs button {
        font-size: 20px !important; /* Adjust font size for tab text */
        padding: 10px !important;   /* Adjust padding to make the tabs bigger */
        font-weight: bold !important; /* Adjust font weight to make the text bold */
    }
    .generate_button1 {
        max-width: 160px !important;
        margin-top: 20px !important;
        margin-bottom: 20px !important;
    }
    """
    
    with gr.Blocks(css=custom_css) as demo:
        with create_intro_part():
            pass
        with create_prompt_part(pipe, saes_dict, demo):
            pass
        with create_top_images_part(demo):
            pass
        with create_intervene_part(pipe, saes_dict, means_dict, demo):
            pass
        
    return demo


if __name__ == "__main__":
    import os
    import gradio as gr
    import torch
    from SDLens import HookedStableDiffusionXLPipeline
    from SAE import SparseAutoencoder
    
    dtype=torch.float32
    pipe = HookedStableDiffusionXLPipeline.from_pretrained(
        'stabilityai/sdxl-turbo',
        torch_dtype=dtype,
        device_map="balanced",
        variant=("fp16" if dtype==torch.float16 else None)
    )
    pipe.set_progress_bar_config(disable=True)

    path_to_checkpoints = './checkpoints/'

    code_to_block = {
        "down.2.1": "unet.down_blocks.2.attentions.1",
        "mid.0": "unet.mid_block.attentions.0",
        "up.0.1": "unet.up_blocks.0.attentions.1",
        "up.0.0": "unet.up_blocks.0.attentions.0"
    }

    saes_dict = {}
    means_dict = {}

    for code, block in code_to_block.items():
        sae = SparseAutoencoder.load_from_disk(
            os.path.join(path_to_checkpoints, f"{block}_k10_hidden5120_auxk256_bs4096_lr0.0001", "final"),
        )
        means = torch.load(
            os.path.join(path_to_checkpoints, f"{block}_k10_hidden5120_auxk256_bs4096_lr0.0001", "final", "mean.pt"),
            weights_only=True
        )
        saes_dict[code] = sae.to('cuda', dtype=dtype)
        means_dict[code] = means.to('cuda', dtype=dtype)

    demo = create_demo(pipe, saes_dict, means_dict)
    demo.launch()