#!/usr/bin/env python3

# Example command:
# ./bin/predict.py \
#       model.path=<path to checkpoint, prepared by make_checkpoint.py> \
#       indir=<path to input data> \
#       outdir=<where to store predicts>

import logging
import os
import sys
import traceback
#import os
#import sys
import inspect

currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)

# from saicinpainting.evaluation.utils import move_to_device
# from saicinpainting.evaluation.refinement import refine_predict
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'

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

# from saicinpainting.training.data.datasets import make_default_val_dataset
# from saicinpainting.training.trainers import load_checkpoint
# from saicinpainting.utils import register_debug_signal_handlers

LOGGER = logging.getLogger(__name__)


@hydra.main(config_path='../configs/prediction', config_name='default.yaml')
def main(predict_config: OmegaConf):
    for k in predict_config.keys():
        print(k, predict_config[k])
#     try:
#         register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log

#         device = torch.device(predict_config.device)

#         train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
#         with open(train_config_path, 'r') as f:
#             train_config = OmegaConf.create(yaml.safe_load(f))
        
#         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 tqdm.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:
#         LOGGER.warning('Interrupted by user')
#     except Exception as ex:
#         LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
#         sys.exit(1)


if __name__ == '__main__':
    main()