import torch
from diffusers import (
    StableDiffusionControlNetPipeline,
    UniPCMultistepScheduler,
    ControlNetModel
)
from diffusers.utils import load_image
from PIL import Image


class GeoPainting:

    DEFAULT_CONTROLNET_MODEL = "lllyasviel/control_v11f1p_sd15_depth"
    DEFAULT_DIFFUSER_MODEL = "geospatial_diffuser"

    def __init__(self, controlnet_model_path=DEFAULT_CONTROLNET_MODEL, diffuser_model=DEFAULT_DIFFUSER_MODEL):
        self.controlnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)
        self.generator = torch.Generator(device="cpu").manual_seed(2)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            diffuser_model,
            low_cpu_mem_usage=False,
            device_map=None,
            controlnet=self.controlnet,
            torch_dtype=torch.float16
        )
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        if torch.cuda.is_available():
            print(":::::::CUDA AVAILABLE::GPU:HUGGINGFACE:::::::")
            self.pipe.enable_model_cpu_offload()
            self.pipe.enable_xformers_memory_efficient_attention()

    def generate_painting(self, input_promp, control_image):
        print(":::::::::::::::::")
        print(type(control_image))
        image = Image.fromarray(control_image.astype('uint8'))
        print(type(image))

        output = self.pipe(
            input_promp,
            image,
            negative_prompt="ugly, disfigured, low quality, blurry, nsfw",
            generator=self.generator,
            num_inference_steps=20,
        )
        return output.images[0]