import os
import torch
import torch.nn as nn
from copy import deepcopy

from ultralytics import RTDETR


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

    def forward(self, x):
        boxes = x[:, :, :4]
        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, labels = torch.max(x[:, :, 4:], dim=-1, keepdim=True)
        return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)


def rtdetr_ultralytics_export(weights, device):
    model = RTDETR(weights)
    model = deepcopy(model.model).to(device)
    for p in model.parameters():
        p.requires_grad = False
    model.eval()
    model.float()
    model = model.fuse()
    for k, m in model.named_modules():
        if m.__class__.__name__ in ('Detect', 'RTDETRDecoder'):
            m.dynamic = False
            m.export = True
            m.format = 'onnx'
        elif m.__class__.__name__ == 'C2f':
            m.forward = m.forward_split
    return model


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 Ultralytics model')

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

    if len(model.names.keys()) > 0:
        print('Creating labels.txt file')
        with open('labels.txt', 'w', encoding='utf-8') as f:
            for name in model.names.values():
                f.write(f'{name}\n')

    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 = 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 is not available for this model')

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


def parse_args():
    import argparse
    parser = argparse.ArgumentParser(description='DeepStream RT-DETR Ultralytics conversion')
    parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) 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=17, 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 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)