import os
import onnx
import torch
import torch.nn as nn
import torch.nn.functional as F

from src.core import YAMLConfig


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

    def forward(self, x):
        boxes = x['pred_boxes']
        convert_matrix = torch.tensor(
            [[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], dtype=boxes.dtype, device=boxes.device
        )
        boxes @= convert_matrix
        boxes *= torch.as_tensor([[*self.img_size]]).flip(1).tile([1, 2]).unsqueeze(1)
        scores = F.sigmoid(x['pred_logits']) if self.use_focal_loss else F.softmax(x['pred_logits'])[:, :, :-1]
        scores, labels = torch.max(scores, dim=-1, keepdim=True)
        return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)


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(), cfg.postprocessor.use_focal_loss


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


def main(args):
    suppress_warnings()

    print(f'\nStarting: {args.weights}')

    print('Opening RT-DETR PyTorch model')

    device = torch.device('cpu')
    model, use_focal_loss = 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, use_focal_loss))

    onnx_input_im = torch.zeros(args.batch, 3, *img_size).to(device)
    onnx_output_file = f'{args.weights}.onnx'

    dynamic_axes = {
        'input': {
            0: 'batch'
        },
        'output': {
            0: 'batch'
        }
    }

    print('Exporting 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=['output'], dynamic_axes=dynamic_axes if args.dynamic else None
    )

    if args.simplify:
        print('Simplifying the ONNX model')
        import onnxslim
        model_onnx = onnx.load(onnx_output_file)
        model_onnx = onnxslim.slim(model_onnx)
        onnx.save(model_onnx, onnx_output_file)

    print(f'Done: {onnx_output_file}\n')


def parse_args():
    import argparse
    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()
    main(args)