import spaces
import torch


def load_pipeline():
    from diffusers import DiffusionPipeline
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    pipe = DiffusionPipeline.from_pretrained(
        "John6666/rae-diffusion-xl-v2-sdxl-spo-pcm",
        custom_pipeline="lpw_stable_diffusion_xl",
        #custom_pipeline="nyanko7/sdxl_smoothed_energy_guidance",
        torch_dtype=torch.float16,
    )
    pipe.to(device)
    return pipe


def token_auto_concat_embeds(pipe, positive, negative):
    max_length = pipe.tokenizer.model_max_length
    positive_length = pipe.tokenizer(positive, return_tensors="pt").input_ids.shape[-1]
    negative_length = pipe.tokenizer(negative, return_tensors="pt").input_ids.shape[-1]
    
    print(f'Token length is model maximum: {max_length}, positive length: {positive_length}, negative length: {negative_length}.')
    if max_length < positive_length or max_length < negative_length:
        print('Concatenated embedding.')
        if positive_length > negative_length:
            positive_ids = pipe.tokenizer(positive, return_tensors="pt").input_ids.to("cuda")
            negative_ids = pipe.tokenizer(negative, truncation=False, padding="max_length", max_length=positive_ids.shape[-1], return_tensors="pt").input_ids.to("cuda")
        else:
            negative_ids = pipe.tokenizer(negative, return_tensors="pt").input_ids.to("cuda")  
            positive_ids = pipe.tokenizer(positive, truncation=False, padding="max_length", max_length=negative_ids.shape[-1],  return_tensors="pt").input_ids.to("cuda")
    else:
        positive_ids = pipe.tokenizer(positive, truncation=False, padding="max_length", max_length=max_length,  return_tensors="pt").input_ids.to("cuda")
        negative_ids = pipe.tokenizer(negative, truncation=False, padding="max_length", max_length=max_length, return_tensors="pt").input_ids.to("cuda")
    
    positive_concat_embeds = []
    negative_concat_embeds = []
    for i in range(0, positive_ids.shape[-1], max_length):
        positive_concat_embeds.append(pipe.text_encoder(positive_ids[:, i: i + max_length])[0])
        negative_concat_embeds.append(pipe.text_encoder(negative_ids[:, i: i + max_length])[0])
    
    positive_prompt_embeds = torch.cat(positive_concat_embeds, dim=1)
    negative_prompt_embeds = torch.cat(negative_concat_embeds, dim=1)
    return positive_prompt_embeds, negative_prompt_embeds


def save_image(image, metadata, output_dir):
    import os
    import uuid
    import json
    from PIL import PngImagePlugin
    filename = str(uuid.uuid4()) + ".png"
    os.makedirs(output_dir, exist_ok=True)
    filepath = os.path.join(output_dir, filename)
    metadata_str = json.dumps(metadata)
    info = PngImagePlugin.PngInfo()
    info.add_text("metadata", metadata_str)
    image.save(filepath, "PNG", pnginfo=info)
    return filepath


pipe = load_pipeline()


@torch.inference_mode()
@spaces.GPU
def generate_image(prompt, neg_prompt):
    prompt += ", anime, masterpiece, best quality, very aesthetic, absurdres"
    neg_prompt += ", bad hands, bad feet, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], photo, deformed, disfigured, low contrast, photo, deformed, disfigured, low contrast"
    metadata = {
        "prompt": prompt,
        "negative_prompt": neg_prompt,
        "resolution": f"{1024} x {1024}",
        "guidance_scale": 7.0,
        "num_inference_steps": 28,
        "sampler": "Euler",
    }
    try: 
        #positive_embeds, negative_embeds = token_auto_concat_embeds(pipe, prompt, neg_prompt)
        images = pipe(
            prompt=prompt,
            negative_prompt=neg_prompt,
            width=1024,
            height=1024,
            guidance_scale=7.0,# seg_scale=3.0, seg_applied_layers=["mid"],
            num_inference_steps=28,
            output_type="pil",
            clip_skip=1,
        ).images
        if images:
            image_paths = [
                save_image(image, metadata, "./outputs")
                for image in images
            ]
        return image_paths
    except Exception as e:
        print(e)
        return []