"""
File to import matchers. The module's import are within the functions, so that
a module is imported only iff needed, reducing the number of raised errors and
warnings due to unused modules.
"""

from pathlib import Path
from .utils import supress_stdout, add_to_path, get_default_device
from .im_models.base_matcher import BaseMatcher


# add viz2d from lightglue to namespace - thanks lightglue!
THIRD_PARTY_DIR = Path(__file__).parent.joinpath("third_party")

add_to_path(THIRD_PARTY_DIR.joinpath("LightGlue"))

from lightglue import viz2d  # for quick import later 'from matching import viz2d'

WEIGHTS_DIR = Path(__file__).parent.joinpath("model_weights")
WEIGHTS_DIR.mkdir(exist_ok=True)

__version__ = "1.0.0"

available_models = [
    "loftr",
    "eloftr",
    "se2loftr",
    "xoftr",
    "aspanformer",
    "matchformer",
    "sift-lg",
    "superpoint-lg",
    "disk-lg",
    "aliked-lg",
    "doghardnet-lg",
    "roma",
    "tiny-roma",
    "dedode",
    "steerers",
    "affine-steerers",
    "dedode-kornia",
    "sift-nn",
    "orb-nn",
    "patch2pix",
    "superglue",
    "r2d2",
    "d2net",
    "duster",
    "master",
    "doghardnet-nn",
    "xfeat",
    "xfeat-star",
    "xfeat-lg",
    "xfeat-steerers-perm",
    "xfeat-steerers-learned",
    "xfeat-star-steerers-perm",
    "xfeat-star-steerers-learned",
    "dedode-lg",
    "gim-dkm",
    "gim-lg",
    "omniglue",
    "xfeat-subpx",
    "xfeat-lg-subpx",
    "dedode-subpx",
    "splg-subpx",
    "aliked-subpx",
    "sift-sphereglue",
    "superpoint-sphereglue",
    "minima",
    "minima-roma",
    "minima-roma-tiny",
    "minima-splg",
    "minima-loftr",
    "vggt"
]


def get_version(pkg):
    version_num = pkg.__version__.split("-")[0]
    major, minor, patch = [int(num) for num in version_num.split(".")]
    return major, minor, patch


@supress_stdout
def get_matcher(
    matcher_name="sift-lg", device="cpu", max_num_keypoints=2048, *args, **kwargs
):
    if isinstance(matcher_name, list):
        from matching.im_models.base_matcher import EnsembleMatcher

        return EnsembleMatcher(matcher_name, device, *args, **kwargs)
    if "subpx" in matcher_name:
        from matching.im_models import keypt2subpx

        detector_name = matcher_name.removesuffix("-subpx")

        return keypt2subpx.Keypt2SubpxMatcher(
            device, detector_name=detector_name, *args, **kwargs
        )

    if matcher_name == "loftr":
        from matching.im_models import loftr

        return loftr.LoftrMatcher(device, *args, **kwargs)

    if matcher_name == "eloftr":
        from matching.im_models import efficient_loftr

        return efficient_loftr.EfficientLoFTRMatcher(device, *args, **kwargs)

    if matcher_name == "se2loftr":
        from matching.im_models import se2loftr

        return se2loftr.Se2LoFTRMatcher(device, *args, **kwargs)

    if matcher_name == "xoftr":
        from matching.im_models import xoftr

        return xoftr.XoFTRMatcher(device, *args, **kwargs)

    elif matcher_name == "aspanformer":
        from matching.im_models import aspanformer

        return aspanformer.AspanformerMatcher(device, *args, **kwargs)

    elif matcher_name == "matchformer":
        from matching.im_models import matchformer

        return matchformer.MatchformerMatcher(device, *args, **kwargs)

    elif matcher_name == "sift-lg":
        from matching.im_models import lightglue

        return lightglue.SiftLightGlue(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "superpoint-lg":
        from matching.im_models import lightglue

        return lightglue.SuperpointLightGlue(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "disk-lg":
        from matching.im_models import lightglue

        return lightglue.DiskLightGlue(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "aliked-lg":
        from matching.im_models import lightglue

        return lightglue.AlikedLightGlue(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "doghardnet-lg":
        from matching.im_models import lightglue

        return lightglue.DognetLightGlue(device, max_num_keypoints, *args, **kwargs)

    elif "roma" in matcher_name:
        from matching.im_models import roma

        if "tiny" in matcher_name:
            return roma.TinyRomaMatcher(device, max_num_keypoints, *args, **kwargs)
        else:
            return roma.RomaMatcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "dedode":
        from matching.im_models import dedode

        return dedode.DedodeMatcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "dedode-kornia":
        from matching.im_models import dedode

        return dedode.DedodeKorniaMatcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "steerers":
        from matching.im_models import steerers

        return steerers.SteererMatcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name in ["aff-steerers", "affine-steerers"]:
        from matching.im_models import aff_steerers

        return aff_steerers.AffSteererMatcher(
            device, max_num_keypoints, *args, **kwargs
        )

    elif matcher_name == "sift-nn":
        from matching.im_models import handcrafted

        return handcrafted.SiftNNMatcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "orb-nn":
        from matching.im_models import handcrafted

        return handcrafted.OrbNNMatcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "patch2pix":
        from matching.im_models import matching_toolbox

        return matching_toolbox.Patch2pixMatcher(device, *args, **kwargs)

    elif matcher_name == "superglue":
        from matching.im_models import matching_toolbox

        return matching_toolbox.SuperGlueMatcher(
            device, max_num_keypoints, *args, **kwargs
        )

    elif matcher_name == "r2d2":
        from matching.im_models import matching_toolbox

        return matching_toolbox.R2D2Matcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "d2net":
        from matching.im_models import matching_toolbox

        return matching_toolbox.D2netMatcher(device, *args, **kwargs)

    elif matcher_name in ["duster", "dust3r"]:
        from matching.im_models import duster

        return duster.Dust3rMatcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name in ["master", "mast3r"]:
        from matching.im_models import master

        return master.Mast3rMatcher(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "doghardnet-nn":
        from matching.im_models import matching_toolbox

        return matching_toolbox.DogAffHardNNMatcher(
            device, max_num_keypoints=max_num_keypoints, *args, **kwargs
        )

    elif "xfeat" in matcher_name:
        if "steerers" in matcher_name:
            from matching.im_models import xfeat_steerers

            if kwargs.get("mode", None) is None:
                # only use matcher_name to assign mode if mode is not a given kwarg
                kwargs["mode"] = "semi-dense" if "star" in matcher_name else "sparse"
           

            if kwargs.get("steerer_type", None) is None:
                if "perm" in matcher_name:
                    kwargs["steerer_type"] = "perm"
                else:
                    kwargs["steerer_type"] = (
                        "learned"  # learned performs better, should be default
                    )

            return xfeat_steerers.xFeatSteerersMatcher(
                device, max_num_keypoints, *args, **kwargs
            )

        else:
            from matching.im_models import xfeat

            kwargs["mode"] = "semi-dense" if "star" in matcher_name else "sparse"
    
            if matcher_name.removeprefix("xfeat").removeprefix("-") in [
                "lg",
                "lightglue",
                "lighterglue",
            ]:
                kwargs["mode"] = "lighterglue"
            return xfeat.xFeatMatcher(
                device, max_num_keypoints=max_num_keypoints, *args, **kwargs
            )

    elif matcher_name == "dedode-lg":
        from matching.im_models import kornia

        return kornia.DeDoDeLightGlue(device, *args, **kwargs)

    elif matcher_name == "gim-dkm":
        from matching.im_models import gim

        return gim.GIM_DKM(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "gim-lg":
        from matching.im_models import gim

        return gim.GIM_LG(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "silk":
        from matching.im_models import silk

        return silk.SilkMatcher(device, *args, **kwargs)

    elif matcher_name == "omniglue":
        from matching.im_models import omniglue

        return omniglue.OmniglueMatcher(device, *args, **kwargs)

    elif matcher_name == "sift-sphereglue":
        from matching.im_models import sphereglue

        return sphereglue.SiftSphereGlue(device, max_num_keypoints, *args, **kwargs)

    elif matcher_name == "superpoint-sphereglue":
        from matching.im_models import sphereglue

        return sphereglue.SuperpointSphereGlue(
            device, max_num_keypoints, *args, **kwargs
        )
    

    elif "minima" in matcher_name:
        from matching.im_models import minima

        if "model_type" not in kwargs.keys():
            if "lg" in matcher_name:
                kwargs["model_type"] = "sp_lg"
            elif "roma" in matcher_name:
                kwargs["model_type"] = "roma"
                if "tiny" in matcher_name:
                    kwargs["model_size"] = "tiny"
                else:
                    kwargs["model_size"] = "large"
            elif "loftr" in matcher_name:
                kwargs["model_type"] = "loftr"
            else:  # set default to sp_lg
                print("no model type set. Using sp-lg as default...")
                kwargs["model_type"] = "sp_lg"

        if kwargs["model_type"] == "sp_lg":
            return minima.MINIMASpLgMatcher(device, *args, **kwargs)
        if kwargs["model_type"] == "loftr":
            return minima.MINIMALoFTRMatcher(device, *args, **kwargs)
        if kwargs["model_type"] == "roma":
            return minima.MINIMARomaMatcher(device, *args, **kwargs)
    elif 'vggt' in matcher_name:
        from matching.im_models.vggt import VGGTMatcher

        return VGGTMatcher(device,max_num_keypoints,device,  *args, **kwargs )
    else:
        raise RuntimeError(
            f"Matcher {matcher_name} not yet supported. Consider submitted a PR to add it. Available models: {available_models}"
        )