import spaces
import gradio as gr
import time
import torch
import numpy as np

from tqdm.auto import tqdm
from torchvision import transforms as tfms
from PIL import Image
from segment_utils import(
    segment_image,
    restore_result,
)
from diffusers import (
    StableDiffusionPipeline,
    DDIMScheduler,
)

# BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-v1-5"
BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

DEFAULT_INPUT_PROMPT = "a woman"
DEFAULT_EDIT_PROMPT = "a woman with linen-blonde-hair"

DEFAULT_CATEGORY = "hair"

basepipeline = StableDiffusionPipeline.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16,
    use_safetensors=True,
)

basepipeline.scheduler = DDIMScheduler.from_config(basepipeline.scheduler.config)

basepipeline = basepipeline.to(DEVICE)

basepipeline.enable_model_cpu_offload()

@spaces.GPU(duration=30)
def image_to_image(
    input_image: Image,
    input_image_prompt: str,
    edit_prompt: str,
    num_steps: int,
    start_step: int,
    guidance_scale: float,
):
    run_task_time = 0
    time_cost_str = ''
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)

    with torch.no_grad():
        input_image_tensor = tfms.functional.to_tensor(input_image).unsqueeze(0).to(DEVICE)
        input_image_tensor = input_image_tensor.to(dtype=torch.float16)
        latent = basepipeline.vae.encode(input_image_tensor * 2 - 1)
    l = 0.18215 * latent.latent_dist.sample()
    inverted_latents = invert(l, input_image_prompt, num_inference_steps=num_steps)
    generated_image = sample(
        edit_prompt,
        start_latents=inverted_latents[-(start_step + 1)][None],
        start_step=start_step,
        num_inference_steps=num_steps,
        guidance_scale=guidance_scale,
    )[0]
    
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)

    return generated_image, time_cost_str

def make_inpaint_condition(image, image_mask):
    image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
    image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0

    assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
    image[image_mask > 0.5] = -1.0  # set as masked pixel
    image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return image

## Inversion
@torch.no_grad()
def invert(
    start_latents,
    prompt,
    guidance_scale=3.5,
    num_inference_steps=80,
    num_images_per_prompt=1,
    do_classifier_free_guidance=True,
    negative_prompt="",
    device=DEVICE,
):

    # Encode prompt
    text_embeddings = basepipeline._encode_prompt(
        prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
    )

    # Latents are now the specified start latents
    latents = start_latents.clone()

    # We'll keep a list of the inverted latents as the process goes on
    intermediate_latents = []

    # Set num inference steps
    basepipeline.scheduler.set_timesteps(num_inference_steps, device=device)

    # Reversed timesteps <<<<<<<<<<<<<<<<<<<<
    timesteps = reversed(basepipeline.scheduler.timesteps)

    for i in tqdm(range(1, num_inference_steps), total=num_inference_steps - 1):

        # We'll skip the final iteration
        if i >= num_inference_steps - 1:
            continue

        t = timesteps[i]

        # Expand the latents if we are doing classifier free guidance
        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
        latent_model_input = basepipeline.scheduler.scale_model_input(latent_model_input, t)

        # Predict the noise residual
        noise_pred = basepipeline.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

        # Perform guidance
        if do_classifier_free_guidance:
            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        current_t = max(0, t.item() - (1000 // num_inference_steps))  # t
        next_t = t  # min(999, t.item() + (1000//num_inference_steps)) # t+1
        alpha_t = basepipeline.scheduler.alphas_cumprod[current_t]
        alpha_t_next = basepipeline.scheduler.alphas_cumprod[next_t]

        # Inverted update step (re-arranging the update step to get x(t) (new latents) as a function of x(t-1) (current latents)
        latents = (latents - (1 - alpha_t).sqrt() * noise_pred) * (alpha_t_next.sqrt() / alpha_t.sqrt()) + (
            1 - alpha_t_next
        ).sqrt() * noise_pred

        # Store
        intermediate_latents.append(latents)

    return torch.cat(intermediate_latents)

# Sample function (regular DDIM)
@torch.no_grad()
def sample(
    prompt,
    start_step=0,
    start_latents=None,
    guidance_scale=3.5,
    num_inference_steps=30,
    num_images_per_prompt=1,
    do_classifier_free_guidance=True,
    negative_prompt="",
    device=DEVICE,
):

    # Encode prompt
    text_embeddings = basepipeline._encode_prompt(
        prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
    )

    # Set num inference steps
    basepipeline.scheduler.set_timesteps(num_inference_steps, device=device)

    # Create a random starting point if we don't have one already
    if start_latents is None:
        start_latents = torch.randn(1, 4, 64, 64, device=device)
        start_latents *= basepipeline.scheduler.init_noise_sigma

    latents = start_latents.clone()

    for i in tqdm(range(start_step, num_inference_steps)):

        t = basepipeline.scheduler.timesteps[i]

        # Expand the latents if we are doing classifier free guidance
        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
        latent_model_input = basepipeline.scheduler.scale_model_input(latent_model_input, t)

        # Predict the noise residual
        noise_pred = basepipeline.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

        # Perform guidance
        if do_classifier_free_guidance:
            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # Normally we'd rely on the scheduler to handle the update step:
        # latents = pipe.scheduler.step(noise_pred, t, latents).prev_sample

        # Instead, let's do it ourselves:
        prev_t = max(1, t.item() - (1000 // num_inference_steps))  # t-1
        alpha_t = basepipeline.scheduler.alphas_cumprod[t.item()]
        alpha_t_prev = basepipeline.scheduler.alphas_cumprod[prev_t]
        predicted_x0 = (latents - (1 - alpha_t).sqrt() * noise_pred) / alpha_t.sqrt()
        direction_pointing_to_xt = (1 - alpha_t_prev).sqrt() * noise_pred
        latents = alpha_t_prev.sqrt() * predicted_x0 + direction_pointing_to_xt

    # Post-processing
    images = basepipeline.decode_latents(latents)
    images = basepipeline.numpy_to_pil(images)

    return images

def get_time_cost(run_task_time, time_cost_str):
    now_time = int(time.time()*1000)
    if run_task_time == 0:
        time_cost_str = 'start'
    else:
        if time_cost_str != '': 
            time_cost_str += f'-->'
        time_cost_str += f'{now_time - run_task_time}'
    run_task_time = now_time
    return run_task_time, time_cost_str

def create_demo() -> gr.Blocks:
    with gr.Blocks() as demo:
        croper = gr.State()
        with gr.Row():
            with gr.Column():
                input_image_prompt = gr.Textbox(lines=1, label="Input Image Prompt", value=DEFAULT_INPUT_PROMPT)
                edit_prompt = gr.Textbox(lines=1, label="Edit Prompt", value=DEFAULT_EDIT_PROMPT)
            with gr.Column():
                num_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, label="Num Steps")
                start_step = gr.Slider(minimum=0, maximum=100, value=15, step=1, label="Start Step")
                guidance_scale = gr.Slider(minimum=0, maximum=30, value=5, step=0.5, label="Guidance Scale")
            with gr.Column():
                generate_size = gr.Number(label="Generate Size", value=512)
                with gr.Accordion("Advanced Options", open=False):
                    mask_expansion = gr.Number(label="Mask Expansion", value=50, visible=True)
                    mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation")
                    category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
                g_btn = gr.Button("Edit Image")
                
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(label="Input Image", type="pil")
            with gr.Column():
                restored_image = gr.Image(label="Restored Image", type="pil", interactive=False)
            with gr.Column():
                origin_area_image = gr.Image(label="Origin Area Image", type="pil", interactive=False)
                generated_image = gr.Image(label="Generated Image", type="pil", interactive=False)
                generated_cost = gr.Textbox(label="Time cost by step (ms):", visible=True, interactive=False)
        
        g_btn.click(
            fn=segment_image,
            inputs=[input_image, category, generate_size, mask_expansion, mask_dilation],
            outputs=[origin_area_image, croper],
        ).success(
            fn=image_to_image,
            inputs=[origin_area_image, input_image_prompt, edit_prompt, num_steps, start_step, guidance_scale],
            outputs=[generated_image, generated_cost],
        ).success(
            fn=restore_result,
            inputs=[croper, category, generated_image],
            outputs=[restored_image],
        )

    return demo