from diffusers import DiffusionPipeline, AutoencoderTiny
from compel import Compel
import torch

try:
    import intel_extension_for_pytorch as ipex  # type: ignore
except:
    pass

import psutil
from config import Args
from pydantic import BaseModel
from PIL import Image
from typing import Callable

base_model = "SimianLuo/LCM_Dreamshaper_v7"
taesd_model = "madebyollin/taesd"


class Pipeline:
    class InputParams(BaseModel):
        seed: int = 2159232
        prompt: str = ""
        guidance_scale: float = 8.0
        strength: float = 0.5
        steps: int = 4
        width: int = 512
        height: int = 512

    def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
        if args.safety_checker:
            self.pipe = DiffusionPipeline.from_pretrained(base_model)
        else:
            self.pipe = DiffusionPipeline.from_pretrained(
                base_model, safety_checker=None
            )
        if args.use_taesd:
            self.pipe.vae = AutoencoderTiny.from_pretrained(
                taesd_model, torch_dtype=torch_dtype, use_safetensors=True
            )

        self.pipe.set_progress_bar_config(disable=True)
        self.pipe.to(device=device, dtype=torch_dtype)
        self.pipe.unet.to(memory_format=torch.channels_last)

        # check if computer has less than 64GB of RAM using sys or os
        if psutil.virtual_memory().total < 64 * 1024**3:
            self.pipe.enable_attention_slicing()

        if args.torch_compile:
            self.pipe.unet = torch.compile(
                self.pipe.unet, mode="reduce-overhead", fullgraph=True
            )
            self.pipe.vae = torch.compile(
                self.pipe.vae, mode="reduce-overhead", fullgraph=True
            )

            self.pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)

        self.compel_proc = Compel(
            tokenizer=self.pipe.tokenizer,
            text_encoder=self.pipe.text_encoder,
            truncate_long_prompts=False,
        )

    def predict(self, params: "Pipeline.InputParams") -> Image.Image:
        generator = torch.manual_seed(params.seed)
        prompt_embeds = self.compel_proc(params.prompt)
        results = self.pipe(
            prompt_embeds=prompt_embeds,
            generator=generator,
            num_inference_steps=params.steps,
            guidance_scale=params.guidance_scale,
            width=params.width,
            height=params.height,
            output_type="pil",
        )
        nsfw_content_detected = (
            results.nsfw_content_detected[0]
            if "nsfw_content_detected" in results
            else False
        )
        if nsfw_content_detected:
            return None
        return results.images[0]