import os
import platform
from functools import partial

import torch

from fast_sam import FastSamAutomaticMaskGenerator, fast_sam_model_registry
from ia_check_versions import ia_check_versions
from ia_config import IAConfig
from ia_devices import devices
from ia_logging import ia_logging
from mobile_sam import SamAutomaticMaskGenerator as SamAutomaticMaskGeneratorMobile
from mobile_sam import SamPredictor as SamPredictorMobile
from mobile_sam import sam_model_registry as sam_model_registry_mobile
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
from sam2.build_sam import build_sam2
from segment_anything_fb import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
from segment_anything_hq import SamAutomaticMaskGenerator as SamAutomaticMaskGeneratorHQ
from segment_anything_hq import SamPredictor as SamPredictorHQ
from segment_anything_hq import sam_model_registry as sam_model_registry_hq


def check_bfloat16_support() -> bool:
    if torch.cuda.is_available():
        compute_capability = torch.cuda.get_device_capability(torch.cuda.current_device())
        if compute_capability[0] >= 8:
            ia_logging.debug("The CUDA device supports bfloat16")
            return True
        else:
            ia_logging.debug("The CUDA device does not support bfloat16")
            return False
    else:
        ia_logging.debug("CUDA is not available")
        return False


def partial_from_end(func, /, *fixed_args, **fixed_kwargs):
    def wrapper(*args, **kwargs):
        updated_kwargs = {**fixed_kwargs, **kwargs}
        return func(*args, *fixed_args, **updated_kwargs)
    return wrapper


def rename_args(func, arg_map):
    def wrapper(*args, **kwargs):
        new_kwargs = {arg_map.get(k, k): v for k, v in kwargs.items()}
        return func(*args, **new_kwargs)
    return wrapper


arg_map = {"checkpoint": "ckpt_path"}
rename_build_sam2 = rename_args(build_sam2, arg_map)
end_kwargs = dict(device="cpu", mode="eval", hydra_overrides_extra=[], apply_postprocessing=False)
sam2_model_registry = {
    "sam2_hiera_large": partial(partial_from_end(rename_build_sam2, **end_kwargs), "sam2_hiera_l.yaml"),
    "sam2_hiera_base_plus": partial(partial_from_end(rename_build_sam2, **end_kwargs), "sam2_hiera_b+.yaml"),
    "sam2_hiera_small": partial(partial_from_end(rename_build_sam2, **end_kwargs), "sam2_hiera_s.yaml"),
    "sam2_hiera_tiny": partial(partial_from_end(rename_build_sam2, **end_kwargs), "sam2_hiera_t.yaml"),
}


def get_sam_mask_generator(sam_checkpoint, anime_style_chk=False):
    """Get SAM mask generator.

    Args:
        sam_checkpoint (str): SAM checkpoint path

    Returns:
        SamAutomaticMaskGenerator or None: SAM mask generator
    """
    points_per_batch = 64
    if "_hq_" in os.path.basename(sam_checkpoint):
        model_type = os.path.basename(sam_checkpoint)[7:12]
        sam_model_registry_local = sam_model_registry_hq
        SamAutomaticMaskGeneratorLocal = SamAutomaticMaskGeneratorHQ
        points_per_batch = 32
    elif "FastSAM" in os.path.basename(sam_checkpoint):
        model_type = os.path.splitext(os.path.basename(sam_checkpoint))[0]
        sam_model_registry_local = fast_sam_model_registry
        SamAutomaticMaskGeneratorLocal = FastSamAutomaticMaskGenerator
        points_per_batch = None
    elif "mobile_sam" in os.path.basename(sam_checkpoint):
        model_type = "vit_t"
        sam_model_registry_local = sam_model_registry_mobile
        SamAutomaticMaskGeneratorLocal = SamAutomaticMaskGeneratorMobile
        points_per_batch = 64
    elif "sam2_" in os.path.basename(sam_checkpoint):
        model_type = os.path.splitext(os.path.basename(sam_checkpoint))[0]
        sam_model_registry_local = sam2_model_registry
        SamAutomaticMaskGeneratorLocal = SAM2AutomaticMaskGenerator
        points_per_batch = 128
    else:
        model_type = os.path.basename(sam_checkpoint)[4:9]
        sam_model_registry_local = sam_model_registry
        SamAutomaticMaskGeneratorLocal = SamAutomaticMaskGenerator
        points_per_batch = 64

    pred_iou_thresh = 0.88 if not anime_style_chk else 0.83
    stability_score_thresh = 0.95 if not anime_style_chk else 0.9

    if "sam2_" in model_type:
        pred_iou_thresh = round(pred_iou_thresh - 0.18, 2)
        stability_score_thresh = round(stability_score_thresh - 0.03, 2)
        sam2_gen_kwargs = dict(
            points_per_side=64,
            points_per_batch=points_per_batch,
            pred_iou_thresh=pred_iou_thresh,
            stability_score_thresh=stability_score_thresh,
            stability_score_offset=0.7,
            crop_n_layers=1,
            box_nms_thresh=0.7,
            crop_n_points_downscale_factor=2)
        if platform.system() == "Darwin":
            sam2_gen_kwargs.update(dict(points_per_side=32, points_per_batch=64, crop_n_points_downscale_factor=1))

    if os.path.isfile(sam_checkpoint):
        sam = sam_model_registry_local[model_type](checkpoint=sam_checkpoint)
        if platform.system() == "Darwin":
            if "FastSAM" in os.path.basename(sam_checkpoint) or not ia_check_versions.torch_mps_is_available:
                sam.to(device=torch.device("cpu"))
            else:
                sam.to(device=torch.device("mps"))
        else:
            if IAConfig.global_args.get("sam_cpu", False):
                ia_logging.info("SAM is running on CPU... (the option has been selected)")
                sam.to(device=devices.cpu)
            else:
                sam.to(device=devices.device)
        sam_gen_kwargs = dict(
            model=sam, points_per_batch=points_per_batch, pred_iou_thresh=pred_iou_thresh, stability_score_thresh=stability_score_thresh)
        if "sam2_" in model_type:
            sam_gen_kwargs.update(sam2_gen_kwargs)
        sam_mask_generator = SamAutomaticMaskGeneratorLocal(**sam_gen_kwargs)
    else:
        sam_mask_generator = None

    return sam_mask_generator


def get_sam_predictor(sam_checkpoint):
    """Get SAM predictor.

    Args:
        sam_checkpoint (str): SAM checkpoint path

    Returns:
        SamPredictor or None: SAM predictor
    """
    # model_type = "vit_h"
    if "_hq_" in os.path.basename(sam_checkpoint):
        model_type = os.path.basename(sam_checkpoint)[7:12]
        sam_model_registry_local = sam_model_registry_hq
        SamPredictorLocal = SamPredictorHQ
    elif "FastSAM" in os.path.basename(sam_checkpoint):
        raise NotImplementedError("FastSAM predictor is not implemented yet.")
    elif "mobile_sam" in os.path.basename(sam_checkpoint):
        model_type = "vit_t"
        sam_model_registry_local = sam_model_registry_mobile
        SamPredictorLocal = SamPredictorMobile
    else:
        model_type = os.path.basename(sam_checkpoint)[4:9]
        sam_model_registry_local = sam_model_registry
        SamPredictorLocal = SamPredictor

    if os.path.isfile(sam_checkpoint):
        sam = sam_model_registry_local[model_type](checkpoint=sam_checkpoint)
        if platform.system() == "Darwin":
            if "FastSAM" in os.path.basename(sam_checkpoint) or not ia_check_versions.torch_mps_is_available:
                sam.to(device=torch.device("cpu"))
            else:
                sam.to(device=torch.device("mps"))
        else:
            if IAConfig.global_args.get("sam_cpu", False):
                ia_logging.info("SAM is running on CPU... (the option has been selected)")
                sam.to(device=devices.cpu)
            else:
                sam.to(device=devices.device)
        sam_predictor = SamPredictorLocal(sam)
    else:
        sam_predictor = None

    return sam_predictor