import math
from typing import List, Optional

from PIL import Image

from internals.data.result import Result
from internals.pipelines.commons import AbstractPipeline, Img2Img
from internals.util.cache import clear_cuda_and_gc
from internals.util.config import get_base_dimension, get_model_dir


class HighRes(AbstractPipeline):
    def load(self, img2img: Optional[Img2Img] = None):
        if hasattr(self, "pipe"):
            return

        if not img2img:
            img2img = Img2Img()
            img2img.load(get_model_dir())

        self.pipe = img2img.pipe
        self.img2img = img2img

    def apply(
        self,
        prompt: List[str],
        negative_prompt: List[str],
        images,
        width: int,
        height: int,
        num_inference_steps: int,
        strength: float = 0.5,
        guidance_scale: int = 9,
        **kwargs,
    ):
        clear_cuda_and_gc()

        images = [image.resize((width, height)) for image in images]
        kwargs = {
            "prompt": prompt,
            "image": images,
            "strength": strength,
            "negative_prompt": negative_prompt,
            "guidance_scale": guidance_scale,
            "num_inference_steps": num_inference_steps,
            **kwargs,
        }
        result = self.pipe.__call__(**kwargs)
        return Result.from_result(result)

    @staticmethod
    def get_intermediate_dimension(target_width: int, target_height: int):
        def_size = get_base_dimension()

        desired_pixel_count = def_size * def_size
        actual_pixel_count = target_width * target_height

        scale = math.sqrt(desired_pixel_count / actual_pixel_count)

        firstpass_width = math.ceil(scale * target_width / 64) * 64
        firstpass_height = math.ceil(scale * target_height / 64) * 64

        return firstpass_width, firstpass_height