# -*- coding: utf-8 -*-
# Author: ximing
# Description: inpaint_util
# Copyright (c) 2023, XiMing Xing.
# License: MIT License

import os
import pathlib

import cv2
import numpy as np
from omegaconf import OmegaConf
from tqdm import trange
import torch
from torch.utils.data._utils.collate import default_collate


def apply_lama_inpaint(predict_config, device):
    # local import
    from lama.saicinpainting.evaluation.utils import move_to_device
    from lama.saicinpainting.evaluation.refinement import refine_predict
    from lama.saicinpainting.training.data.datasets import make_default_val_dataset
    from lama.saicinpainting.training.trainers import load_checkpoint

    try:
        train_config_path = pathlib.Path(predict_config.model.path) / 'config.yaml'
        train_config = OmegaConf.load(train_config_path)

        train_config.training_model.predict_only = True
        train_config.visualizer.kind = 'noop'

        out_ext = predict_config.get('out_ext', '.png')

        checkpoint_path = os.path.join(
            predict_config.model.path, 'models', predict_config.model.checkpoint
        )
        model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')
        model.freeze()

        if not predict_config.get('refine', False):
            model.to(device)

        if not predict_config.indir.endswith('/'):
            predict_config.indir += '/'

        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
        for img_i in trange(len(dataset)):
            mask_fname = dataset.mask_filenames[img_i]
            cur_out_fname = os.path.join(
                predict_config.outdir,
                os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext
            )
            os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
            batch = default_collate([dataset[img_i]])

            if predict_config.get('refine', False):
                assert 'unpad_to_size' in batch, "Unpadded size is required for the refinement"
                # image unpadding is taken care of in the refiner, so that output image
                # is same size as the input image
                cur_res = refine_predict(batch, model, **predict_config.refiner)
                cur_res = cur_res[0].permute(1, 2, 0).detach().cpu().numpy()
            else:
                with torch.no_grad():
                    batch = move_to_device(batch, device)
                    batch['mask'] = (batch['mask'] > 0) * 1
                    batch = model(batch)
                    cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
                    unpad_to_size = batch.get('unpad_to_size', None)
                    if unpad_to_size is not None:
                        orig_height, orig_width = unpad_to_size
                        cur_res = cur_res[:orig_height, :orig_width]

            cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
            cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
            cv2.imwrite(cur_out_fname, cur_res)

    except KeyboardInterrupt:
        print('Interrupted by user')
    except Exception as ex:
        print(f'Prediction failed due to:')
        print(f'{ex}')
        import sys
        sys.exit(1)