import os
import sys
import argparse
import warnings
import onnx
import torch
import torch.nn as nn
from src.core import YAMLConfig


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

    def forward(self, x):
        boxes = x['pred_boxes']
        boxes[:, :, [0, 2]] *= self.img_size[1]
        boxes[:, :, [1, 3]] *= self.img_size[0]
        scores, classes = torch.max(x['pred_logits'], 2, keepdim=True)
        classes = classes.float()
        return boxes, scores, classes
    

class DeepStreamInput(nn.Module):
    def __init__(self, img_size, device):
        self.img_size = img_size
        self.device = device
        super().__init__()

    def forward(self, x):
        size = torch.tensor([[*self.img_size]]).to(self.device)
        return x, size


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


def rtdetr_pytorch_export(weights, cfg_file, device):
    cfg = YAMLConfig(cfg_file, resume=weights)
    checkpoint = torch.load(weights, map_location=device)
    if 'ema' in checkpoint:
        state = checkpoint['ema']['module']
    else:
        state = checkpoint['model']
    cfg.model.load_state_dict(state)
    return cfg.model.deploy()


def main(args):
    suppress_warnings()

    print('\nStarting: %s' % args.weights)

    print('Opening RT-DETR PyTorch model\n')

    device = torch.device('cpu')
    model = rtdetr_pytorch_export(args.weights, args.config, device)

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

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

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

    dynamic_axes = {
        'input': {
            0: 'batch'
        },
        'boxes': {
            0: 'batch'
        },
        'scores': {
            0: 'batch'
        },
        'classes': {
            0: 'batch'
        }
    }

    print('\nExporting the model to 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=['boxes', 'scores', 'classes'],
                      dynamic_axes=dynamic_axes if args.dynamic else None)

    if args.simplify:
        print('Simplifying 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('Done: %s\n' % onnx_output_file)


def parse_args():
    parser = argparse.ArgumentParser(description='DeepStream RT-DETR PyTorch conversion')
    parser.add_argument('-w', '--weights', required=True, help='Input weights (.pth) file path (required)')
    parser.add_argument('-c', '--config', required=True, help='Input YAML (.yml) file path (required)')
    parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
    parser.add_argument('--opset', type=int, default=16, 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='Static batch-size')
    args = parser.parse_args()
    if not os.path.isfile(args.weights):
        raise SystemExit('Invalid weights file')
    if not os.path.isfile(args.config):
        raise SystemExit('Invalid config file')
    if args.dynamic and args.batch > 1:
        raise SystemExit('Cannot set dynamic batch-size and static batch-size at same time')
    return args


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