import gradio as gr
from PIL import Image  

import torch
import re
import os
import requests

from customization import customize_vae_decoder
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DDIMScheduler, EulerDiscreteScheduler
from torchvision import transforms
from attribution import MappingNetwork

import math
from typing import List
from PIL import Image
import cv2
import numpy as np
import torch

is_gpu_busy = False
PRETRAINED_MODEL_NAME_OR_PATH = "./checkpoints/"


def get_image_grid(images: List[Image.Image]) -> Image:
    num_images = len(images)
    cols = 3#int(math.ceil(math.sqrt(num_images)))
    rows = 1#int(math.ceil(num_images / cols))
    width, height = images[0].size
    grid_image = Image.new('RGB', (cols * width, rows * height))
    for i, img in enumerate(images):
        x = i % cols
        y = i // cols
        grid_image.paste(img, (x * width, y * height))
    return grid_image


class AttributionModel:
    def __init__(self):
        self.pipe = StableDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2')#, safety_checker=None, torch_dtype=torch.float16)
        self.pipe = self.pipe.to("cuda")
        self.resize_transform = transforms.Resize(512, interpolation=transforms.InterpolationMode.BILINEAR)
        self.vae = AutoencoderKL.from_pretrained(
            'stabilityai/stable-diffusion-2', subfolder="vae"
        )
        self.vae = customize_vae_decoder(self.vae, 128, "qkv", "all", False, 1.0)

        self.mapping_network = MappingNetwork(32, 0, 128, None, num_layers=2, w_avg_beta=None, normalization = False).to("cuda")
        
        from torchvision.models import resnet50, ResNet50_Weights
        self.decoding_network = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
        self.decoding_network.fc = torch.nn.Linear(2048,32)
        
        self.vae.decoder.load_state_dict(torch.load(os.path.join(PRETRAINED_MODEL_NAME_OR_PATH, 'vae_decoder.pth')))
        self.mapping_network.load_state_dict(torch.load(os.path.join(PRETRAINED_MODEL_NAME_OR_PATH, 'mapping_network.pth')))
        self.decoding_network.load_state_dict(torch.load(os.path.join(PRETRAINED_MODEL_NAME_OR_PATH, 'decoding_network.pth')))

        self.vae = self.vae.to("cuda")
        self.mapping_network = self.mapping_network.to("cuda")
        self.decoding_network = self.decoding_network.to("cuda")

        self.test_norm = transforms.Compose(
            [
                transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
            ]
        )

    def infer(self, prompt, negative, guidance_scale):
        images = []
        with torch.no_grad():
            out_latents = self.pipe([prompt], output_type="latent", num_inference_steps=10, guidance_scale=guidance_scale).images
        image = self.inference_with_attribution(out_latents)
        print(image[0])
        # image = self.pipe.numpy_to_pil(image)
        # image[0].save("im1.jpg")
        return [image[0]]*3 #, "caption") #get_image_grid(images)

    def inference_without_attribution(self, latents):
        latents = 1 / 0.18215 * latents
        with torch.no_grad():
            image = self.pipe.vae.decode(latents).sample
        image = image.clamp(-1,1)
        return image

    def get_phis(self, phi_dimension, batch_size ,eps = 1e-8):
        phi_length = phi_dimension
        b = batch_size
        phi = torch.empty(b,phi_length).uniform_(0,1)
        return torch.bernoulli(phi) + eps


    def inference_with_attribution(self, latents, key=None):
        if key==None:
            key = self.get_phis(32, 1)

        latents = 1 / 0.18215 * latents
        with torch.no_grad():
            image = self.vae.decode(latents, self.mapping_network(key.cuda())).sample
        image = image.clamp(-1,1)
        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image

    def postprocess(self, image):
        image = self.resize_transform(image)
        return image

    def detect_key(self, image):
        reconstructed_keys = self.decoding_network(self.test_norm((image / 2 + 0.5).clamp(0, 1)))
        return reconstructed_keys


attribution_model = AttributionModel()

with gr.Blocks() as demo:
    with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
        with gr.Column():
            text = gr.Textbox(
                label="Enter your prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                elem_id="prompt-text-input",
            ).style(
                border=(True, False, True, True),
                rounded=(True, False, False, True),
                container=False,
            )
            negative = gr.Textbox(
                label="Enter your negative prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter a negative prompt",
                elem_id="negative-prompt-text-input",
            ).style(
                border=(True, False, True, True),
                rounded=(True, False, False, True),
                container=False,
            )
        btn = gr.Button("Generate image").style(full_width=False)

    with gr.Row():
        img_output_simple = gr.Image()
        img_output_attribute = gr.Image()
        img_output_diff = gr.Image()


    with gr.Row():
        guidance_scale = gr.Slider(
            label="Guidance Scale", minimum=0, maximum=10, value=9, step=0.1
        )
    btn.click(attribution_model.infer, inputs=[text, negative, guidance_scale], outputs=[img_output_simple, img_output_attribute, img_output_diff], postprocess=False)


if __name__=="__main__":
    demo.queue(concurrency_count=1, max_size=20).launch(max_threads=50)