import os
import sys
import argparse
import warnings
import onnx
import torch
import torch.nn as nn


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

    def forward(self, x):
        x = x[0]
        boxes = x[:, :, :4]
        objectness = x[:, :, 4:5]
        scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
        return torch.cat((boxes, scores, classes, objectness), dim=2)


def suppress_warnings():
    warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
    warnings.filterwarnings('ignore', category=UserWarning)
    warnings.filterwarnings('ignore', category=DeprecationWarning)


def yolor_export(weights, cfg, size, device):
    if os.path.isfile('models/experimental.py'):
        import models
        from models.experimental import attempt_load
        from utils.activations import Hardswish
        model = attempt_load(weights, map_location=device)
        for k, m in model.named_modules():
            m._non_persistent_buffers_set = set()
            if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish):
                m.act = Hardswish()
            elif isinstance(m, nn.Upsample) and not hasattr(m, 'recompute_scale_factor'):
                m.recompute_scale_factor = None
        model.model[-1].training = False
        model.model[-1].export = False
    else:
        from models.models import Darknet
        model_name = os.path.basename(weights).split('.pt')[0]
        if cfg == '':
            cfg = 'cfg/' + model_name + '.cfg'
            if not os.path.isfile(cfg):
                raise SystemExit('CFG file not found')
        model = Darknet(cfg, img_size=size[::-1]).to(device)
        model.load_state_dict(torch.load(weights, map_location=device)['model'])
        model.float()
        model.fuse()
        model.eval()
        model.module_list[-1].training = False
    return model


def main(args):
    suppress_warnings()
    device = torch.device('cpu')
    model = yolor_export(args.weights, args.cfg, args.size, device)

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

    img_size = args.size * 2 if len(args.size) == 1 else args.size

    if img_size == [640, 640] and args.p6:
        img_size = [1280] * 2

    onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
    onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'

    torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
                      do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)

    if args.simplify:
        import onnxsim
        model_onnx = onnx.load(onnx_output_file)
        model_onnx, _ = onnxsim.simplify(model_onnx)
        onnx.save(model_onnx, onnx_output_file)


def parse_args():
    parser = argparse.ArgumentParser(description='DeepStream YOLOR conversion')
    parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
    parser.add_argument('-c', '--cfg', default='', help='Input cfg (.cfg) file path')
    parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
    parser.add_argument('--p6', action='store_true', help='P6 model')
    parser.add_argument('--opset', type=int, default=12, help='ONNX opset version')
    parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
    args = parser.parse_args()
    if not os.path.isfile(args.weights):
        raise SystemExit('Invalid weights file')
    return args


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