import os
import pathlib
import shlex
import shutil
import subprocess
import sys
import cv2
import torch

repo_dir = pathlib.Path(__file__).parent
submodule_dir = repo_dir / 'prismer'
sys.path.insert(0, submodule_dir.as_posix())

from dataset import create_dataset, create_loader
from model.prismer_caption import PrismerCaption


def download_models() -> None:
    if not pathlib.Path('prismer/experts/expert_weights/').exists():
        subprocess.run(shlex.split(
            'python download_checkpoints.py --download_experts=True'), cwd='prismer')

    model_names = [
        'vqa_prismer_base',
        'vqa_prismer_large',
        'pretrain_prismer_base',
        'pretrain_prismer_large',
    ]
    for model_name in model_names:
        if pathlib.Path(f'prismer/logging/{model_name}').exists():
            continue
        subprocess.run(shlex.split(f'python download_checkpoints.py --download_models={model_name}'), cwd='prismer')


def build_deformable_conv() -> None:
    subprocess.run( shlex.split('sh make.sh'), cwd='prismer/experts/segmentation/mask2former/modeling/pixel_decoder/ops')


def run_experts(image_path: str) -> tuple[str | None, ...]:
    helper_dir = submodule_dir / 'helpers'
    shutil.rmtree(helper_dir, ignore_errors=True)
    image_dir = helper_dir / 'images'
    image_dir.mkdir(parents=True, exist_ok=True)
    out_path = image_dir / 'image.jpg'
    cv2.imwrite(out_path.as_posix(), cv2.imread(image_path))

    # expert_names = ['depth', 'edge', 'normal', 'objdet', 'ocrdet', 'segmentation']
    expert_names = ['depth', 'edge', 'normal']
    for expert_name in expert_names:
        env = os.environ.copy()
        if 'PYTHONPATH' in env:
            env['PYTHONPATH'] = f'{submodule_dir.as_posix()}:{env["PYTHONPATH"]}'
        else:
            env['PYTHONPATH'] = submodule_dir.as_posix()

        subprocess.run(shlex.split(f'python experts/generate_{expert_name}.py'), cwd='prismer', env=env, check=True)

    # keys = ['depth', 'edge', 'normal', 'seg_coco', 'obj_detection', 'ocr_detection']
    keys = ['depth', 'edge', 'normal']
    results = [pathlib.Path('prismer/helpers/labels') / key / 'helpers/images/image.png' for key in keys]
    return results[0].as_posix(), results[1].as_posix(), results[2].as_posix()