import collections.abc as collections
import torch


def get_class(mod_path, BaseClass):
    """Get the class object which inherits from BaseClass and is defined in
       the module named mod_name, child of base_path.
    """
    import inspect
    mod = __import__(mod_path, fromlist=[''])
    classes = inspect.getmembers(mod, inspect.isclass)
    # Filter classes defined in the module
    classes = [c for c in classes if c[1].__module__ == mod_path]
    # Filter classes inherited from BaseModel
    classes = [c for c in classes if issubclass(c[1], BaseClass)]
    assert len(classes) == 1, classes
    return classes[0][1]


def get_model(name, _type = "detector"):
    from models.base_model import BaseModel
    if _type == "detector" and name == "superpoint":
        base_path = 'detectors.point2d.SuperPoint.'
    elif _type == "regressor":
        base_path = 'models.'
    return get_class(base_path + name, BaseModel)


def numpy_image_to_torch(image):
    """Normalize the image tensor and reorder the dimensions."""
    if image.ndim == 3:
        image = image.transpose((2, 0, 1))  # HxWxC to CxHxW
    elif image.ndim == 2:
        image = image[None]  # add channel axis
    else:
        raise ValueError(f'Not an image: {image.shape}')
    return torch.from_numpy(image / 255.).float()


def map_tensor(input_, func):
    if isinstance(input_, (str, bytes)):
        return input_
    elif isinstance(input_, collections.Mapping):
        return {k: map_tensor(sample, func) for k, sample in input_.items()}
    elif isinstance(input_, collections.Sequence):
        return [map_tensor(sample, func) for sample in input_]
    else:
        return func(input_)


def batch_to_np(batch):
    return map_tensor(batch, lambda t: t.detach().cpu().numpy()[0])