# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import logging
import torch
import hydra
from hydra import compose, initialize
from hydra.utils import instantiate
from omegaconf import OmegaConf


def build_sam2(
    config_file,
    ckpt_path=None,
    device="cuda",
    mode="eval",
    hydra_overrides_extra=[],
    apply_postprocessing=True,
):

    if apply_postprocessing:
        hydra_overrides_extra = hydra_overrides_extra.copy()
        hydra_overrides_extra += [
            # dynamically fall back to multi-mask if the single mask is not stable
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
        ]
    # Read config and init model
    cfg = compose(config_name=config_file, overrides=hydra_overrides_extra)
    OmegaConf.resolve(cfg)
    model = instantiate(cfg.model, _recursive_=True)
    _load_checkpoint(model, ckpt_path)
    model = model.to(device)
    if mode == "eval":
        model.eval()
    return model


def build_sam2_video_predictor(
    config_file,
    ckpt_path=None,
    device="cuda",
    mode="eval",
    hydra_overrides_extra=[],
    apply_postprocessing=True,
):
    hydra_overrides = [
        "++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictor",
    ]
    if apply_postprocessing:
        hydra_overrides_extra = hydra_overrides_extra.copy()
        hydra_overrides_extra += [
            # dynamically fall back to multi-mask if the single mask is not stable
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
            # the sigmoid mask logits on interacted frames with clicks in the memory encoder so that the encoded masks are exactly as what users see from clicking
            "++model.binarize_mask_from_pts_for_mem_enc=true",
            # fill small holes in the low-res masks up to `fill_hole_area` (before resizing them to the original video resolution)
            "++model.fill_hole_area=8",
        ]
    hydra_overrides.extend(hydra_overrides_extra)
    # Read config and init model
    cfg = compose(config_name=config_file, overrides=hydra_overrides)
    OmegaConf.resolve(cfg)
    model = instantiate(cfg.model, _recursive_=True)
    _load_checkpoint(model, ckpt_path)
    model = model.to(device)
    if mode == "eval":
        model.eval()
    return model


def build_sam2_camera_predictor(
    config_file,
    ckpt_path=None,
    device="cuda",
    mode="eval",
    hydra_overrides_extra=[],
    apply_postprocessing=True,
):
    hydra_overrides = [
        "++model._target_=sam2.sam2_camera_predictor.SAM2CameraPredictor",
    ]
    if apply_postprocessing:
        hydra_overrides_extra = hydra_overrides_extra.copy()
        hydra_overrides_extra += [
            # dynamically fall back to multi-mask if the single mask is not stable
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
            # the sigmoid mask logits on interacted frames with clicks in the memory encoder so that the encoded masks are exactly as what users see from clicking
            "++model.binarize_mask_from_pts_for_mem_enc=true",
            # fill small holes in the low-res masks up to `fill_hole_area` (before resizing them to the original video resolution)
            "++model.fill_hole_area=8",
        ]
    hydra_overrides.extend(hydra_overrides_extra)
    #! caishaofei modify begin
    hydra.core.global_hydra.GlobalHydra.instance().clear()
    config_path = "./sam2_configs"
    initialize(config_path=config_path, version_base='1.3')
    #! caishaofei modify end
    # Read config and init model
    cfg = compose(config_name=config_file, overrides=hydra_overrides)
    OmegaConf.resolve(cfg)
    model = instantiate(cfg.model, _recursive_=True)
    _load_checkpoint(model, ckpt_path)
    model = model.to(device)
    if mode == "eval":
        model.eval()
    return model

def _load_checkpoint(model, ckpt_path):
    if ckpt_path is not None:
        sd = torch.load(ckpt_path, map_location="cpu")["model"]
        missing_keys, unexpected_keys = model.load_state_dict(sd)
        if missing_keys:
            logging.error(missing_keys)
            raise RuntimeError()
        if unexpected_keys:
            logging.error(unexpected_keys)
            raise RuntimeError()
        logging.info("Loaded checkpoint sucessfully")
