from typing import List
import os
import cv2
import numpy as np
import huggingface_hub as hf
from PIL import Image
from modules import processing, shared, devices


debug = shared.log.trace if os.environ.get('SD_FACE_DEBUG', None) is not None else lambda *args, **kwargs: None
insightface_app = None
swapper = None


def face_swap(p: processing.StableDiffusionProcessing, app, input_images: List[Image.Image], source_image: Image.Image, cache: bool):
    global swapper # pylint: disable=global-statement
    if swapper is None:
        import insightface.model_zoo
        repo_id = 'ezioruan/inswapper_128.onnx'
        model_path = hf.hf_hub_download(repo_id=repo_id, filename='inswapper_128.onnx', cache_dir=shared.opts.hfcache_dir)
        shared.log.debug(f'FaceSwap load: repo="{repo_id}" path="{model_path}"')
        # model_path = hf.hf_hub_download(repo_id='somanchiu/reswapper', filename='reswapper_256-1567500_originalInswapperClassCompatible.onnx', cache_dir=shared.opts.hfcache_dir)
        try:
            router: insightface.model_zoo.model_zoo.INSwapper = insightface.model_zoo.model_zoo.ModelRouter(model_path)
            swapper = router.get_model()
        except Exception as e:
            shared.log.error(f'FaceSwap load: {e}')
            return None

    np_image = cv2.cvtColor(np.array(source_image), cv2.COLOR_RGB2BGR)
    faces = app.get(np_image)
    if faces is None or len(faces) == 0:
        shared.log.warning('FaceSwap: No faces detected')
        return None
    source_face = faces[0]
    processed_images = []
    for image in input_images:
        np_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
        faces = app.get(np_image)
        for i, face in enumerate(faces):
            debug(f'FaceSwap: face={i} source={source_face.bbox} target={face.bbox}')
            np_image = swapper.get(img=np_image, target_face=face, source_face=source_face, paste_back=True) # pylint: disable=unexpected-keyword-arg, no-value-for-parameter
        p.extra_generation_params["FaceSwap"] = f'{len(faces)}'
        np_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)
        processed_images.append(Image.fromarray(np_image))

    if not cache:
        swapper = None
    devices.torch_gc()

    return processed_images