import random

import gradio as gr
import numpy as np
import spaces
import torch
import torchvision.transforms as transforms
from PIL import Image
from torchmetrics.functional.image import structural_similarity_index_measure as ssim
from transformers import CLIPModel, CLIPProcessor

from tools import synth

device = "cuda" if torch.cuda.is_available() else "cpu"
model_path = "runwayml/stable-diffusion-v1-5"
clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14").to(device)
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = synth.pipe_img(
        model_path=model_path,
        device=device,
        use_torchcompile=False,
    )
else:
    pipe = synth.pipe_img(
        model_path=model_path,
        device=device,
        apply_optimization=False,
    )

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


@spaces.GPU
def infer(
    input_image,
    prompt1,
    prompt2,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    interpolation_step,
    num_inference_steps,
    num_interpolation_steps,
):
    device = "cuda" if torch.cuda.is_available() else "cpu"

    # Input Validation
    try:
        assert num_interpolation_steps % 2 == 0
    except AssertionError:
        raise ValueError("num_interpolation_steps must be an even number")

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    prompts = [prompt1, prompt2]
    generator = torch.Generator().manual_seed(seed)

    sample_mid_interpolation = num_interpolation_steps
    remove_n_middle = 0

    interpolated_prompt_embeds, prompt_metadata = synth.interpolatePrompts(
        prompts,
        pipe,
        num_interpolation_steps,
        sample_mid_interpolation,
        remove_n_middle=remove_n_middle,
        device=device,
    )
    negative_prompts = [negative_prompt, negative_prompt]
    if negative_prompts != ["", ""]:
        interpolated_negative_prompts_embeds, _ = synth.interpolatePrompts(
            negative_prompts,
            pipe,
            num_interpolation_steps,
            sample_mid_interpolation,
            remove_n_middle=remove_n_middle,
            device=device,
        )
    else:
        interpolated_negative_prompts_embeds, _ = [None] * len(
            interpolated_prompt_embeds
        ), None

    latents = torch.randn(
        (1, pipe.unet.config.in_channels, height // 8, width // 8),
        generator=generator,
    ).to(device)
    embed_pairs = zip(interpolated_prompt_embeds, interpolated_negative_prompts_embeds)
    embed_pairs_list = list(embed_pairs)
    # offset step by -1
    prompt_embeds, negative_prompt_embeds = embed_pairs_list[interpolation_step - 1]
    preprocess_input = transforms.Compose(
        [transforms.ToTensor(), transforms.Resize((512, 512))]
    )
    input_img_tensor = preprocess_input(input_image).unsqueeze(0)
    if negative_prompt_embeds is not None:
        npe = negative_prompt_embeds[None, ...]
    else:
        npe = None
    images_list = pipe(
        height=height,
        width=width,
        num_images_per_prompt=1,
        prompt_embeds=prompt_embeds[None, ...],
        negative_prompt_embeds=npe,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        generator=generator,
        latents=latents,
        image=input_img_tensor,
    )
    if images_list["nsfw_content_detected"][0]:
        image = Image.open("samples/unsafe.jpeg")
        return image, seed, "Unsafe content detected", "Unsafe content detected"
    else:
        image = images_list.images[0]

    pred_image = transforms.ToTensor()(image).unsqueeze(0)
    ssim_score = ssim(pred_image, input_img_tensor).item()
    real_inputs = clip_processor(
        text=prompts, padding=True, images=input_image, return_tensors="pt"
    ).to(device)
    real_output = clip_model(**real_inputs)
    synth_inputs = clip_processor(
        text=prompts, padding=True, images=image, return_tensors="pt"
    ).to(device)
    synth_output = clip_model(**synth_inputs)
    cos_sim = torch.nn.CosineSimilarity(dim=1)
    cosine_sim = (
        cos_sim(real_output.image_embeds, synth_output.image_embeds)
        .detach()
        .cpu()
        .numpy()
        .squeeze()
        * 100
    )

    return image, seed, round(ssim_score, 4), round(cosine_sim, 2)


examples1 = [
    "A photo of a garbage truck, dustcart",
    "A photo of a Shih-Tzu, a type of dog",
]
examples2 = [
    "A photo of a cassette player",
    "A photo of a beagle, a type of dog",
]


def update_steps(total_steps, interpolation_step):
    return gr.update(maximum=total_steps)


def update_format(image_format):
    return gr.update(format=image_format)


if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(title="Generative Date Augmentation Demo") as demo:

    gr.Markdown(
        """
    # Data Augmentation with Image-to-Image Diffusion Models via Prompt Interpolation.
    Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/generative-data-augmentation) | Image Classification Demo: [Generative Augmented Classifiers](https://huggingface.co/spaces/czl/generative-augmented-classifiers).
    """
    )
    with gr.Row():
        with gr.Column():

            input_image = gr.Image(type="pil", label="Image to Augment")

            with gr.Row():
                prompt1 = gr.Text(
                    label="Prompt for the image to synthesize. (Actual class)",
                    show_label=True,
                    max_lines=1,
                    placeholder="Enter Prompt for the image to synthesize. (Actual class)",
                    container=False,
                )
            with gr.Row():
                prompt2 = gr.Text(
                    label="Prompt to augment against. (Confusing class)",
                    show_label=True,
                    max_lines=1,
                    placeholder="Enter Prompt to augment against. (Confusing class)",
                    container=False,
                )
            with gr.Row():
                gr.Examples(
                    examples=[
                        "samples/n03417042_5234.JPEG",
                        "samples/n02086240_2799.JPEG",
                    ],
                    inputs=[input_image],
                    label="Example Images",
                )
                gr.Examples(
                    examples=examples1,
                    inputs=[prompt1],
                    label="Example for Prompt 1 (Actual class)",
                )
                gr.Examples(
                    examples=examples2,
                    inputs=[prompt2],
                    label="Example for Prompt 2 (Confusing class)",
                )

            with gr.Row():
                num_interpolation_steps = gr.Slider(
                    label="Total Interpolation Steps",
                    minimum=2,
                    maximum=128,
                    step=2,
                    value=16,
                )
                interpolation_step = gr.Slider(
                    label="Sample Interpolation Step",
                    minimum=1,
                    maximum=16,
                    step=1,
                    value=8,
                )
                num_interpolation_steps.change(
                    fn=update_steps,
                    inputs=[num_interpolation_steps, interpolation_step],
                    outputs=[interpolation_step],
                )
                run_button = gr.Button("Run", scale=0)
            with gr.Accordion("Advanced Settings", open=True):
                negative_prompt = gr.Text(
                    label="Negative prompt",
                    max_lines=1,
                    placeholder="Enter a negative prompt",
                    visible=False,
                )
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=0,
                )
                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                gr.Markdown("Negative Prompt: ")
                with gr.Row():
                    negative_prompt = gr.Text(
                        label="Negative Prompt",
                        show_label=True,
                        max_lines=1,
                        value="blurry image, disfigured, deformed, distorted, cartoon, drawings",
                        container=False,
                    )
                with gr.Row():
                    width = gr.Slider(
                        label="Width",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=512,
                    )
                    height = gr.Slider(
                        label="Height",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=512,
                    )
                with gr.Row():
                    guidance_scale = gr.Slider(
                        label="Guidance scale",
                        minimum=0.0,
                        maximum=10.0,
                        step=0.1,
                        value=8.0,
                    )
                    num_inference_steps = gr.Slider(
                        label="Number of inference steps",
                        minimum=1,
                        maximum=80,
                        step=1,
                        value=25,
                    )
                with gr.Row():
                    image_type = gr.Radio(
                        choices=[
                            "webp",
                            "png",
                            "jpeg",
                        ],
                        label="Download Image Format",
                        value="jpeg",
                    )
        with gr.Column():
            result = gr.Image(label="Result", show_label=False, format="jpeg")
            image_type.change(
                fn=update_format,
                inputs=[image_type],
                outputs=[result],
            )
            gr.Markdown(
                """
                Metadata:
                """
            )
            with gr.Row():
                show_seed = gr.Label(label="Seed:", value="Randomized seed")
                ssim_score = gr.Label(
                    label="SSIM Score:", value="Generate to see score"
                )
                cos_sim = gr.Label(label="CLIP Score:", value="Generate to see score")
            if power_device == "GPU":
                gr.Markdown(
                    f"""
Currently running on {power_device}.
                    """
                )
            else:
                gr.Markdown(
                    f"""
Currently running on {power_device}.
Note: Running on CPU will take longer (approx. 6 minutes with default settings).
                    """
                )
            gr.Markdown(
                """
This demo is created as part of the 'Investigating the Effectiveness of Generative Diffusion Models in Synthesizing Images for Data Augmentation in Image Classification' dissertation.

The user can augment an image by interpolating between two prompts, and specify the number of interpolation steps and the specific step to generate the image.

View the files used in this demo [here](https://huggingface.co/spaces/czl/generative-data-augmentation-demo/tree/main).

Usage Instructions & Documentation [here](https://huggingface.co/spaces/czl/generative-data-augmentation-demo/blob/main/README.md).

Note: Safety checker is enabled to prevent unsafe content from being displayed in this public demo.
            """
            )
        run_button.click(
            fn=infer,
            inputs=[
                input_image,
                prompt1,
                prompt2,
                negative_prompt,
                seed,
                randomize_seed,
                width,
                height,
                guidance_scale,
                interpolation_step,
                num_inference_steps,
                num_interpolation_steps,
            ],
            outputs=[result, show_seed, ssim_score, cos_sim],
        )

demo.queue().launch(show_error=True)