from __future__ import annotations

import PIL.Image
import torch
from diffusers import UniDiffuserPipeline


class Model:
    def __init__(self):
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        if self.device.type == "cuda":
            self.pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1", torch_dtype=torch.float16)
            self.pipe.to(self.device)
        else:
            self.pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1")

    def run(
        self,
        mode: str,
        prompt: str,
        image: PIL.Image.Image | None,
        seed: int = 0,
        num_steps: int = 20,
        guidance_scale: float = 8.0,
    ) -> tuple[PIL.Image.Image | None, str]:
        generator = torch.Generator(device=self.device).manual_seed(seed)
        if mode == "t2i":
            self.pipe.set_text_to_image_mode()
            sample = self.pipe(
                prompt=prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator
            )
            return sample.images[0], ""
        elif mode == "i2t":
            self.pipe.set_image_to_text_mode()
            sample = self.pipe(
                image=image, num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator
            )
            return None, sample.text[0]
        elif mode == "joint":
            self.pipe.set_joint_mode()
            sample = self.pipe(num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
            return sample.images[0], sample.text[0]
        elif mode == "i":
            self.pipe.set_image_mode()
            sample = self.pipe(num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
            return sample.images[0], ""
        elif mode == "t":
            self.pipe.set_text_mode()
            sample = self.pipe(num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
            return None, sample.text[0]
        elif mode == "i2t2i":
            self.pipe.set_image_to_text_mode()
            sample = self.pipe(
                image=image, num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator
            )
            self.pipe.set_text_to_image_mode()
            sample = self.pipe(
                prompt=sample.text[0],
                num_inference_steps=num_steps,
                guidance_scale=guidance_scale,
                generator=generator,
            )
            return sample.images[0], ""
        elif mode == "t2i2t":
            self.pipe.set_text_to_image_mode()
            sample = self.pipe(
                prompt=prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator
            )
            self.pipe.set_image_to_text_mode()
            sample = self.pipe(
                image=sample.images[0],
                num_inference_steps=num_steps,
                guidance_scale=guidance_scale,
                generator=generator,
            )
            return None, sample.text[0]
        else:
            raise ValueError