import argparse
import os
from pathlib import Path
import torch
import time
import subprocess
from torch.utils.mobile_optimizer import optimize_for_mobile

if __name__ == "__main__":
    from boxmot.appearance import export_formats
    from boxmot.appearance.backbones import build_model
    from boxmot.appearance.reid_model_factory import get_model_name, load_pretrained_weights
    from boxmot.utils import WEIGHTS, logger
    from boxmot.utils.torch_utils import select_device

    from boxmot.utils.checks import TestRequirements
else:
    from . import export_formats
    from .backbones import build_model
    from .reid_model_factory import get_model_name, load_pretrained_weights
    from ..utils import WEIGHTS, logger
    from ..utils.torch_utils import select_device

    from ..utils.checks import TestRequirements

__tr = TestRequirements()


def file_size(path):
    # Return file/dir size (MB)
    path = Path(path)
    if path.is_file():
        return path.stat().st_size / 1E6
    elif path.is_dir():
        return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / 1E6
    else:
        return 0.0


def export_torchscript(model, im, file, optimize):
    try:
        logger.info(f'\nStarting export with torch {torch.__version__}...')
        f = file.with_suffix('.torchscript')
        print(f)
        ts = torch.jit.trace(model, im, strict=False)
        if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
            optimize_for_mobile(ts)._save_for_lite_interpreter(str(f))
        else:
            ts.save(str(f))

        logger.info(f'Export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        logger.info(f'Export failure: {e}')


def export_onnx(model, im, file, opset, dynamic, fp16, simplify):
    # ONNX export
    try:
        __tr.check_packages(('onnx',))
        import onnx

        f = file.with_suffix('.onnx')
        logger.info(f'\nStarting export with onnx {onnx.__version__}...')

        if dynamic:
            # input --> shape(N,3,640,640), output --> shape(N, feature-size)
            dynamic = {'images': {0: 'batch'}, 'output': {0: 'batch'}}

        torch.onnx.export(
            model.half() if fp16 else model.cpu(),
            im.half() if fp16 else im.cpu(),
            f,
            verbose=False,
            opset_version=opset,
            do_constant_folding=True,
            input_names=['images'],
            output_names=['output'],
            dynamic_axes=dynamic or None
        )
        # Checks
        model_onnx = onnx.load(f)  # load onnx model
        onnx.checker.check_model(model_onnx)  # check onnx model
        onnx.save(model_onnx, f)

        # Simplify
        if simplify:
            try:
                cuda = torch.cuda.is_available()
                __tr.check_packages(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
                import onnxsim

                logger.info(f'simplifying with onnx-simplifier {onnxsim.__version__}...')
                model_onnx, check = onnxsim.simplify(model_onnx)
                assert check, 'assert check failed'
                onnx.save(model_onnx, f)
            except Exception as e:
                logger.info(f'simplifier failure: {e}')
        logger.info(f'Export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        logger.info(f'export failure: {e}')


def export_openvino(file, half):
    __tr.check_packages(('openvino-dev',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
    import openvino.runtime as ov  # noqa
    from openvino.tools import mo  # noqa

    f = str(file).replace(file.suffix, f'_openvino_model{os.sep}')
    f_onnx = file.with_suffix('.onnx')
    f_ov = str(Path(f) / file.with_suffix('.xml').name)
    try:
        logger.info(f'\nStarting export with openvino {ov.__version__}...')
        #cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
        #subprocess.check_output(cmd.split())  # export
        ov_model = mo.convert_model(f_onnx,
                                    model_name=file.with_suffix('.xml'),
                                    framework='onnx',
                                    compress_to_fp16=half)  # export
        ov.serialize(ov_model, f_ov)  # save
    except Exception as e:
        logger.info(f'export failure: {e}')
    logger.info(f'Export success, saved as {f_ov} ({file_size(f_ov):.1f} MB)')
    return f


def export_tflite(file):
    try:
        __tr.check_packages(('onnx2tf', 'tensorflow', 'onnx_graphsurgeon', 'sng4onnx'))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
        import onnx2tf

        logger.info(f'\nStarting {file} export with onnx2tf {onnx2tf.__version__}')
        f = str(file).replace('.onnx', f'_saved_model{os.sep}')
        cmd = f"onnx2tf -i {file} -o {f} -nuo --non_verbose"
        subprocess.check_output(cmd.split())  # export
        logger.info(f'Export success, results saved in {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        logger.info(f'\nExport failure: {e}')


def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False):
    try:
        assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
        try:
            import tensorrt as trt
        except Exception:
            if platform.system() == 'Linux':
                __tr.check_packages(['nvidia-tensorrt'], cmds=('-U --index-url https://pypi.ngc.nvidia.com',))
            import tensorrt as trt

        export_onnx(model, im, file, 12, dynamic, half, simplify)  # opset 13
        onnx = file.with_suffix('.onnx')

        logger.info(f'\nStarting export with TensorRT {trt.__version__}...')
        assert onnx.exists(), f'failed to export ONNX file: {onnx}'
        f = file.with_suffix('.engine')  # TensorRT engine file
        logger = trt.Logger(trt.Logger.INFO)
        if verbose:
            logger.min_severity = trt.Logger.Severity.VERBOSE

        builder = trt.Builder(logger)
        config = builder.create_builder_config()
        config.max_workspace_size = workspace * 1 << 30

        flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
        network = builder.create_network(flag)
        parser = trt.OnnxParser(network, logger)
        if not parser.parse_from_file(str(onnx)):
            raise RuntimeError(f'failed to load ONNX file: {onnx}')

        inputs = [network.get_input(i) for i in range(network.num_inputs)]
        outputs = [network.get_output(i) for i in range(network.num_outputs)]
        logger.info(f'Network Description:')
        for inp in inputs:
            logger.info(f'\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
        for out in outputs:
            logger.info(f'\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')

        if dynamic:
            if im.shape[0] <= 1:
                logger.warning(f"WARNING: --dynamic model requires maximum --batch-size argument")
            profile = builder.create_optimization_profile()
            for inp in inputs:
                if half:
                    inp.dtype = trt.float16
                profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
            config.add_optimization_profile(profile)

        logger.info(f'Building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine in {f}')
        if builder.platform_has_fast_fp16 and half:
            config.set_flag(trt.BuilderFlag.FP16)
            config.default_device_type = trt.DeviceType.GPU
        with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
            t.write(engine.serialize())
        logger.info(f'Export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        logger.info(f'\nexport failure: {e}')


if __name__ == "__main__":

    parser = argparse.ArgumentParser(description="ReID export")
    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[256, 128], help='image (h, w)')
    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
    parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
    parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
    parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
    parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
    parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
    parser.add_argument('--weights', type=Path, default=WEIGHTS / 'mobilenetv2_x1_4_dukemtmcreid.pt', help='model.pt path(s)')
    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
    parser.add_argument('--include',
                        nargs='+',
                        default=['torchscript'],
                        help='torchscript, onnx, openvino, engine')
    args = parser.parse_args()

    t = time.time()

    include = [x.lower() for x in args.include]  # to lowercase
    fmts = tuple(export_formats()['Argument'][1:])  # --include arguments
    flags = [x in include for x in fmts]
    assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
    jit, onnx, openvino, engine, tflite = flags  # export booleans

    args.device = select_device(args.device)
    if args.half:
        assert args.device.type != 'cpu', '--half only compatible with GPU export, i.e. use --device 0'
        # assert not args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'

    model = build_model(
        get_model_name(args.weights),
        num_classes=1,
        pretrained=not (args.weights and args.weights.is_file() and args.weights.suffix == '.pt'),
        use_gpu=args.device
    ).to(args.device)
    load_pretrained_weights(model, args.weights)
    model.eval()

    if args.optimize:
        assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'

    # adapt input shapes for lmbn models
    if 'lmbn' in str(args.weights):
        args.imgsz = (384, 128)
         
    im = torch.zeros(args.batch_size, 3, args.imgsz[0], args.imgsz[1]).to(args.device)  # image size(1,3,640,480) BCHW iDetection
    for _ in range(2):
        y = model(im)  # dry runs
    if args.half:
        im, model = im.half(), model.half()  # to FP16
    shape = tuple((y[0] if isinstance(y, tuple) else y).shape)  # model output shape
    logger.info(f"\nStarting from {args.weights} with output shape {shape} ({file_size(args.weights):.1f} MB)")

    # Exports
    f = [''] * len(fmts)  # exported filenames
    if jit:
        f[0] = export_torchscript(model, im, args.weights, args.optimize)  # opset 12
    if engine:  # TensorRT required before ONNX
        f[1] = export_engine(model, im, args.weights, args.half, args.dynamic, args.simplify, args.workspace, args.verbose)
    if onnx:  # OpenVINO requires ONNX
        f[2] = export_onnx(model, im, args.weights, args.opset, args.dynamic, args.half, args.simplify)  # opset 12
    if tflite:
        export_tflite(f[2])
    if openvino:
        f[3] = export_openvino(args.weights, args.half)
    

    # Finish
    f = [str(x) for x in f if x]  # filter out '' and None
    if any(f):
        logger.info(f'\nExport complete ({time.time() - t:.1f}s)'
                    f"\nResults saved to {args.weights.parent.resolve()}"
                    f"\nVisualize:       https://netron.app")
