import gc
import numpy as np
import numpy
import torch
from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel

from matplotlib import pyplot as plt
from pathlib import Path
from PIL import Image
from torch import autocast
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer, logging
import os
from diffusers import StableDiffusionPipeline, DiffusionPipeline

# large or small model

# configurations
height, width       = 128, 128
guidance_scale      = 8
custom_loss_scale   = 200
batch_size          = 1
torch_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"


pretrained_model_name_or_path = "CompVis/stable-diffusion-v1-4"
pipe = DiffusionPipeline.from_pretrained(
    pretrained_model_name_or_path,
    torch_dtype=torch.float32
).to(torch_device)

# Load SD concepts
sdconcepts = ['<morino-hon>', '<space-style>', '<tesla-bot>', '<midjourney-style>', ' <hanfu-anime-style>'] 

pipe.load_textual_inversion("sd-concepts-library/morino-hon-style") 
pipe.load_textual_inversion("sd-concepts-library/space-style") 
pipe.load_textual_inversion("sd-concepts-library/tesla-bot") 
pipe.load_textual_inversion("sd-concepts-library/midjourney-style") 
pipe.load_textual_inversion("sd-concepts-library/hanfu-anime-style")

# define seeds
seed_list = [1, 2, 3, 4, 5]


def custom_loss(images):
    
    # Gradient loss
    gradient_x = torch.abs(images[:, :, :, :-1] - images[:, :, :, 1:]).mean()
    gradient_y = torch.abs(images[:, :, :-1, :] - images[:, :, 1:, :]).mean()
    error = gradient_x + gradient_y
    #Variational loss
    # diff_x = torch.abs(images[:, :, :, :-1] - images[:, :, :, 1:])
    # diff_y = torch.abs(images[:, :, :-1, :] - images[:, :, 1:, :])
    # error = diff_x.mean() + diff_y.mean()

    return error

def latents_to_pil(latents):
    # bath of latents -> list of images
    latents = (1 / 0.18215) * latents
    with torch.no_grad():
        image = pipe.vae.decode(latents).sample
    image = (image / 2 + 0.5).clamp(0, 1) # 0 to 1
    image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
    images = (image * 255).round().astype("uint8")
    pil_images = [Image.fromarray(image) for image in images]
    return pil_images
    
def generate_latents(prompts, num_inference_steps, seed_nums, loss_apply=False):
    
    generator = torch.manual_seed(seed_nums)
    
    # scheduler
    scheduler    = LMSDiscreteScheduler(beta_start = 0.00085, beta_end = 0.012, beta_schedule = "scaled_linear", num_train_timesteps = 1000)
    scheduler.set_timesteps(num_inference_steps)
    scheduler.timesteps = scheduler.timesteps.to(torch.float32)

    # text embeddings of the prompt
    text_input = pipe.tokenizer(prompts, padding='max_length', max_length = pipe.tokenizer.model_max_length, truncation= True, return_tensors="pt")
    input_ids = text_input.input_ids.to(torch_device)

    with torch.no_grad():
        text_embeddings = pipe.text_encoder(text_input.input_ids.to(torch_device))[0]

    max_length = text_input.input_ids.shape[-1]
    uncond_input = pipe.tokenizer(
          [""] * batch_size, padding="max_length", max_length= max_length, return_tensors="pt"
    )

    with torch.no_grad():
        uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0]

    text_embeddings = torch.cat([uncond_embeddings,text_embeddings]) # 2,77,768

    # random latent
    latents = torch.randn(
        (batch_size, pipe.unet.config.in_channels, height// 8, width //8),
        generator = generator,
    ) .to(torch.float16)


    latents = latents.to(torch_device)
    latents = latents * scheduler.init_noise_sigma

    for i, t in tqdm(enumerate(scheduler.timesteps), total = len(scheduler.timesteps)):

        latent_model_input = torch.cat([latents] * 2)
        sigma = scheduler.sigmas[i]
        latent_model_input = scheduler.scale_model_input(latent_model_input, t)

        with torch.no_grad():
            noise_pred = pipe.unet(latent_model_input.to(torch.float32), t, encoder_hidden_states=text_embeddings)["sample"]
            #noise_pred = pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]

        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        if (loss_apply and i%5 == 0): 
            
            latents = latents.detach().requires_grad_()
            #latents_x0 = scheduler.step(noise_pred,t, latents).pred_original_sample # this line does not work
            latents_x0 = latents - sigma * noise_pred

            # use vae to decode the image
            denoised_images = pipe.vae.decode((1/ 0.18215) * latents_x0).sample / 2 + 0.5 # range(0,1)

            loss = custom_loss(denoised_images) * custom_loss_scale
            print(f"Custom gradient loss {loss}")
            
            cond_grad = torch.autograd.grad(loss, latents)[0]
            latents = latents.detach() - cond_grad * sigma**2

        latents = scheduler.step(noise_pred,t, latents).prev_sample
        
    return latents

    
# Function to convert PIL images to NumPy arrays
def pil_to_np(image):
    return np.array(image)
    
def generate_gradio_images(prompt, num_inference_steps, loss_flag = False):
    # after loss is applied
    latents_list = []
    for seed_no, sd in zip(seed_list, sdconcepts):
        prompts = [f'{prompt} {sd}']
        latents = generate_latents(prompts,num_inference_steps, seed_no, loss_apply=loss_flag)
        latents_list.append(latents)
    # show all
    latents_list = torch.vstack(latents_list)
    images = latents_to_pil(latents_list)
    return images