import os
import sys
import onnx
import paddle
import paddle.nn as nn
from ppdet.core.workspace import load_config, merge_config
from ppdet.utils.check import check_version, check_config
from ppdet.utils.cli import ArgsParser
from ppdet.engine import Trainer
from ppdet.slim import build_slim_model
from ppdet.data.source.category import get_categories


class DeepStreamOutput(nn.Layer):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        boxes = x['bbox']
        x['bbox_num'] = x['bbox_num'].transpose([0, 2, 1])
        scores = paddle.max(x['bbox_num'], 2, keepdim=True)
        classes = paddle.argmax(x['bbox_num'], 2, keepdim=True)
        return boxes, scores, classes


def ppyoloe_export(FLAGS):
    cfg = load_config(FLAGS.config)
    FLAGS.opt['weights'] = FLAGS.weights
    FLAGS.opt['exclude_nms'] = True
    merge_config(FLAGS.opt)
    if FLAGS.slim_config:
        cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test')
    merge_config(FLAGS.opt)
    check_config(cfg)
    check_version()
    trainer = Trainer(cfg, mode='test')
    trainer.load_weights(cfg.weights)
    trainer.model.eval()
    if not os.path.exists('.tmp'):
        os.makedirs('.tmp')
    static_model, _ = trainer._get_infer_cfg_and_input_spec('.tmp')
    os.system('rm -r .tmp')
    return trainer.cfg, static_model


def main(FLAGS):
    print('\nStarting: %s' % FLAGS.weights)

    print('\nOpening PPYOLOE model\n')

    paddle.set_device('cpu')
    cfg, model = ppyoloe_export(FLAGS)

    anno_file = cfg['TestDataset'].get_anno()
    if os.path.isfile(anno_file):
        _, catid2name = get_categories(cfg['metric'], anno_file, 'detection_arch')
        print('\nCreating labels.txt file')
        f = open('labels.txt', 'w')
        for name in catid2name.values():
            f.write(str(name) + '\n')
        f.close()

    model = nn.Sequential(model, DeepStreamOutput())

    img_size = [cfg.eval_height, cfg.eval_width]

    onnx_input_im = {}
    onnx_input_im['image'] = paddle.static.InputSpec(shape=[FLAGS.batch, 3, *img_size], dtype='float32', name='image')
    onnx_input_im['scale_factor'] = paddle.static.InputSpec(shape=[FLAGS.batch, 2], dtype='float32', name='scale_factor')
    onnx_output_file = cfg.filename + '.onnx'

    print('\nExporting the model to ONNX\n')
    paddle.onnx.export(model, cfg.filename, input_spec=[onnx_input_im], opset_version=FLAGS.opset)

    if FLAGS.simplify:
        print('\nSimplifying the ONNX model')
        import onnxsim
        model_onnx = onnx.load(onnx_output_file)
        model_onnx, _ = onnxsim.simplify(model_onnx)
        onnx.save(model_onnx, onnx_output_file)

    print('\nDone: %s\n' % onnx_output_file)


def parse_args():
    parser = ArgsParser()
    parser.add_argument('-w', '--weights', required=True, help='Input weights (.pdparams) file path (required)')
    parser.add_argument('--slim_config', default=None, type=str, help='Slim configuration file of slim method')
    parser.add_argument('--opset', type=int, default=11, help='ONNX opset version')
    parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
    parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
    parser.add_argument('--batch', type=int, default=1, help='Implicit batch-size')
    args = parser.parse_args()
    if not os.path.isfile(args.weights):
        raise SystemExit('\nInvalid weights file')
    if args.dynamic and args.batch > 1:
        raise SystemExit('\nCannot set dynamic batch-size and implicit batch-size at same time')
    elif args.dynamic:
        args.batch = None
    return args


if __name__ == '__main__':
    FLAGS = parse_args()
    sys.exit(main(FLAGS))