import spaces
import torch
import gradio as gr
from PIL import Image
import random
from diffusers import (
    DiffusionPipeline,
    AutoencoderKL,
    StableDiffusionControlNetPipeline,
    ControlNetModel,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionImg2ImgPipeline,
    StableDiffusionControlNetImg2ImgPipeline,
    DPMSolverMultistepScheduler,
    EulerDiscreteScheduler
)
import tempfile
import time
import os
from transformers import CLIPImageProcessor

BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"

# Initialize both pipelines
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)

# Initialize the safety checker conditionally
SAFETY_CHECKER_ENABLED = os.environ.get("SAFETY_CHECKER", "0") == "1"
safety_checker = None
feature_extractor = None

if SAFETY_CHECKER_ENABLED:
    safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker").to("cuda")
    feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32")

main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
    BASE_MODEL,
    controlnet=controlnet,
    vae=vae,
    safety_checker=safety_checker,
    feature_extractor=feature_extractor,
    torch_dtype=torch.float16,
).to("cuda")

# Sampler map
SAMPLER_MAP = {
    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
}

def center_crop_resize(img, output_size=(512, 512)):
    width, height = img.size
    new_dimension = min(width, height)
    left = (width - new_dimension) / 2
    top = (height - new_dimension) / 2
    right = (width + new_dimension) / 2
    bottom = (height + new_dimension) / 2
    
    img = img.crop((left, top, right, bottom))
    img = img.resize(output_size)
    
    return img

def common_upscale(samples, width, height, upscale_method, crop=False):
    if crop == "center":
        old_width = samples.shape[3]
        old_height = samples.shape[2]
        old_aspect = old_width / old_height
        new_aspect = width / height
        
        x = 0
        y = 0
        
        if old_aspect > new_aspect:
            x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
        elif old_aspect < new_aspect:
            y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
        
        s = samples[:, :, y:old_height - y, x:old_width - x]
    else:
        s = samples
    
    return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)

def upscale(samples, upscale_method, scale_by):
    width = round(samples["images"].shape[3] * scale_by)
    height = round(samples["images"].shape[2] * scale_by)
    
    s = common_upscale(samples["images"], width, height, upscale_method, "disabled")
    
    return s

def check_inputs(prompt: str, control_image: Image.Image):
    if control_image is None:
        raise gr.Error("Please select or upload an Input Illusion")
    
    if prompt is None or prompt == "":
        raise gr.Error("Prompt is required")

@spaces.GPU 
def inference(control_image: Image.Image, prompt: str, negative_prompt: str,
              guidance_scale: float = 8.0,
              controlnet_conditioning_scale: float = 1,
              control_guidance_start: float = 1,
              control_guidance_end: float = 1,
              upscaler_strength: float = 0.5,
              seed: int = -1,
              sampler="DPM++ Karras SDE",
              progress=gr.Progress(track_tqdm=True),
              profile=None):
    
    start_time = time.time()
    
    control_image_small = center_crop_resize(control_image)
    
    main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
    
    my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
    generator = torch.Generator(device="cuda").manual_seed(my_seed)

    out = main_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image=control_image_small,
        guidance_scale=float(guidance_scale),
        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
        generator=generator,
        control_guidance_start=float(control_guidance_start),
        control_guidance_end=float(control_guidance_end),
        num_inference_steps=15,
        output_type="latent"
    )
    
    upscaled_latents = upscale(out, "nearest-exact", 2)

    out_image = main_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        control_image=center_crop_resize(control_image, (1024, 1024)),
        image=upscaled_latents,
        guidance_scale=float(guidance_scale),
        generator=generator,
        num_inference_steps=20,
        strength=upscaler_strength,
        control_guidance_start=float(control_guidance_start),
        control_guidance_end=float(control_guidance_end),
        controlnet_conditioning_scale=float(controlnet_conditioning_scale)
    )

    end_time = time.time()
    
    # Save image + metadata logic here

with gr.Blocks() as app:
    gr.Markdown('''
      <div style="text-align: center;">
      <h1>Illusion Diffusion HQ 🌀</h1>
      <p style="font-size:16px;">Generate stunning high quality illusion artwork with Stable Diffusion</p>
      </div>
      ''')
    
    with gr.Row():
        with gr.Column():
            control_image = gr.Image(label="Input Illusion", type="pil")
            prompt = gr.Textbox(label="Prompt", placeholder="Medieval village scene with busy streets and castle in the distance")
            negative_prompt = gr.Textbox(label="Negative Prompt", value="low quality")
            run_btn = gr.Button("Run")
            
            result_image = gr.Image(label="Illusion Diffusion Output", interactive=False)

            run_btn.click(check_inputs, inputs=[prompt, control_image]).success(
                inference, inputs=[control_image, prompt, negative_prompt], outputs=[result_image]
            )

if __name__ == "__main__":
    app.launch()