huggingface_segment-anything-2.txt

# File: segment-anything-2-coreml-conversion/coreml/export.py
import argparse
import os
import enum
from typing import List, Optional, Tuple
import ast
import torch
import numpy as np
from PIL import Image
from PIL.Image import Resampling
import coremltools as ct
from coremltools.converters.mil._deployment_compatibility import AvailableTarget
from coremltools import ComputeUnit
from coremltools.converters.mil.mil.passes.defs.quantization import ComputePrecision
from coremltools.converters.mil import register_torch_op
from coremltools.converters.mil.mil import Builder as mb
from sam2.sam2_image_predictor import SAM2ImagePredictor

class SAM2Variant(enum.Enum):
    Tiny = 'tiny'
    Small = 'small'
    BasePlus = 'base-plus'
    Large = 'large'

    def fmt(self):
        if self == SAM2Variant.BasePlus:
            return 'BasePlus'
        return self.value.capitalize()
SAM2_HW = (1024, 1024)

def parse_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
    parser.add_argument('--output-dir', type=str, default='.', help='Provide location to save exported models.')
    parser.add_argument('--variant', type=lambda x: getattr(SAM2Variant, x), choices=[variant for variant in SAM2Variant], default=SAM2Variant.Small, help='SAM2 variant to export.')
    parser.add_argument('--points', type=str, help="List of 2D points, e.g., '[[10,20], [30,40]]'")
    parser.add_argument('--boxes', type=str, help="List of 2D bounding boxes, e.g., '[[10,20,30,40], [50,60,70,80]]'")
    parser.add_argument('--labels', type=str, help='List of binary labels for each points entry, denoting foreground (1) or background (0).')
    parser.add_argument('--min-deployment-target', type=lambda x: getattr(AvailableTarget, x), choices=[target for target in AvailableTarget], default=AvailableTarget.iOS17, help='Minimum deployment target for CoreML model.')
    parser.add_argument('--compute-units', type=lambda x: getattr(ComputeUnit, x), choices=[cu for cu in ComputeUnit], default=ComputeUnit.ALL, help='Which compute units to target for CoreML model.')
    parser.add_argument('--precision', type=lambda x: getattr(ComputePrecision, x), choices=[p for p in ComputePrecision], default=ComputePrecision.FLOAT16, help='Precision to use for quantization.')
    return parser

@register_torch_op
def upsample_bicubic2d(context, node):
    x = context[node.inputs[0]]
    output_size = context[node.inputs[1]].val
    scale_factor_height = output_size[0] / x.shape[2]
    scale_factor_width = output_size[1] / x.shape[3]
    align_corners = context[node.inputs[2]].val
    x = mb.upsample_bilinear(x=x, scale_factor_height=scale_factor_height, scale_factor_width=scale_factor_width, align_corners=align_corners, name=node.name)
    context.add(x)

class SAM2ImageEncoder(torch.nn.Module):

    def __init__(self, model: SAM2ImagePredictor):
        super().__init__()
        self.model = model

    @torch.no_grad()
    def forward(self, image):
        (img_embedding, feats_s0, feats_s1) = self.model.encode_image_raw(image)
        return (img_embedding, feats_s0, feats_s1)

def validate_image_encoder(model: ct.models.MLModel, ground_model: SAM2ImagePredictor, image: Image.Image):
    prepared_image = image.resize(SAM2_HW, Resampling.BILINEAR)
    predictions = model.predict({'image': prepared_image})
    image = np.array(image.convert('RGB'))
    tch_image = ground_model._transforms(image)
    tch_image = tch_image[None, ...].to('cpu')
    (ground_embedding, ground_feats_s0, ground_feats_s1) = ground_model.encode_image_raw(tch_image)
    (ground_embedding, ground_feats_s0, ground_feats_s1) = (ground_embedding.numpy(), ground_feats_s0.numpy(), ground_feats_s1.numpy())
    img_max_diff = np.max(np.abs(predictions['image_embedding'] - ground_embedding))
    img_avg_diff = np.mean(np.abs(predictions['image_embedding'] - ground_embedding))
    s0_max_diff = np.max(np.abs(predictions['feats_s0'] - ground_feats_s0))
    s0_avg_diff = np.mean(np.abs(predictions['feats_s0'] - ground_feats_s0))
    s1_max_diff = np.max(np.abs(predictions['feats_s1'] - ground_feats_s1))
    s1_avg_diff = np.mean(np.abs(predictions['feats_s1'] - ground_feats_s1))
    print(f'Image Embedding: Max Diff: {img_max_diff:.4f}, Avg Diff: {img_avg_diff:.4f}')
    print(f'Feats S0: Max Diff: {s0_max_diff:.4f}, Avg Diff: {s0_avg_diff:.4f}')
    print(f'Feats S1: Max Diff: {s1_max_diff:.4f}, Avg Diff: {s1_avg_diff:.4f}')

def validate_prompt_encoder(model: ct.models.MLModel, ground_model: SAM2ImagePredictor, unnorm_coords, labels):
    predictions = model.predict({'points': unnorm_coords, 'labels': labels})
    (ground_sparse, ground_dense) = ground_model.encode_points_raw(unnorm_coords, labels)
    ground_sparse = ground_sparse.numpy()
    ground_dense = ground_dense.numpy()
    sparse_max_diff = np.max(np.abs(predictions['sparse_embeddings'] - ground_sparse))
    sparse_avg_diff = np.mean(np.abs(predictions['sparse_embeddings'] - ground_sparse))
    dense_max_diff = np.max(np.abs(predictions['dense_embeddings'] - ground_dense))
    dense_avg_diff = np.mean(np.abs(predictions['dense_embeddings'] - ground_dense))
    print('Sparse Embeddings: Max Diff: {:.4f}, Avg Diff: {:.4f}'.format(sparse_max_diff, sparse_avg_diff))
    print('Dense Embeddings: Max Diff: {:.4f}, Avg Diff: {:.4f}'.format(dense_max_diff, dense_avg_diff))
    assert np.allclose(predictions['sparse_embeddings'], ground_sparse, atol=0.009)
    assert np.allclose(predictions['dense_embeddings'], ground_dense, atol=0.001)

def validate_mask_decoder(model: ct.models.MLModel, ground_model: SAM2ImagePredictor, image_embedding, sparse_embedding, dense_embedding, feats_s0, feats_s1, precision: ComputePrecision):
    predictions = model.predict({'image_embedding': image_embedding, 'sparse_embedding': sparse_embedding, 'dense_embedding': dense_embedding, 'feats_s0': feats_s0, 'feats_s1': feats_s1})
    (ground_masks, scores) = ground_model.decode_masks_raw(image_embedding, sparse_embedding, dense_embedding, [feats_s0, feats_s1])
    ground_masks = ground_masks.numpy()
    masks_max_diff = np.max(np.abs(predictions['low_res_masks'] - ground_masks))
    masks_avg_diff = np.mean(np.abs(predictions['low_res_masks'] - ground_masks))
    print('Masks: Max Diff: {:.4f}, Avg Diff: {:.4f}'.format(masks_max_diff, masks_avg_diff))
    atol = 0.07 if precision == ComputePrecision.FLOAT32 else 0.3
    assert np.allclose(predictions['low_res_masks'], ground_masks, atol=atol)
    print(f"Scores: {predictions['scores']}, ground: {scores}")

class SAM2PointsEncoder(torch.nn.Module):

    def __init__(self, model: SAM2ImagePredictor):
        super().__init__()
        self.model = model

    @torch.no_grad()
    def forward(self, points, labels):
        prompt_embedding = self.model.encode_points_raw(points, labels)
        return prompt_embedding

class SAM2MaskDecoder(torch.nn.Module):

    def __init__(self, model: SAM2ImagePredictor):
        super().__init__()
        self.model = model

    @torch.no_grad()
    def forward(self, image_embedding, sparse_embedding, dense_embedding, feats_s0, feats_s1):
        (low_res_masks, iou_scores) = self.model.decode_masks_raw(image_embedding, sparse_embedding, dense_embedding, [feats_s0, feats_s1])
        return (low_res_masks, iou_scores)

def export_image_encoder(image_predictor: SAM2ImagePredictor, variant: SAM2Variant, output_dir: str, min_target: AvailableTarget, compute_units: ComputeUnit, precision: ComputePrecision) -> Tuple[int, int]:
    image = Image.open('../notebooks/images/truck.jpg')
    image = np.array(image.convert('RGB'))
    orig_hw = (image.shape[0], image.shape[1])
    prepared_image = image_predictor._transforms(image)
    prepared_image = prepared_image[None, ...].to('cpu')
    traced_model = torch.jit.trace(SAM2ImageEncoder(image_predictor).eval(), prepared_image)
    scale = 1 / (0.226 * 255.0)
    bias = [-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225]
    mlmodel = ct.convert(traced_model, inputs=[ct.ImageType(name='image', shape=(1, 3, SAM2_HW[0], SAM2_HW[1]), scale=scale, bias=bias)], outputs=[ct.TensorType(name='image_embedding'), ct.TensorType(name='feats_s0'), ct.TensorType(name='feats_s1')], minimum_deployment_target=min_target, compute_units=compute_units, compute_precision=precision)
    image = Image.open('../notebooks/images/truck.jpg')
    validate_image_encoder(mlmodel, image_predictor, image)
    output_path = os.path.join(output_dir, f'SAM2{variant.fmt()}ImageEncoder{precision.value.upper()}')
    mlmodel.save(output_path + '.mlpackage')
    return orig_hw

def export_points_prompt_encoder(image_predictor: SAM2ImagePredictor, variant: SAM2Variant, input_points: List[List[float]], input_labels: List[int], orig_hw: tuple, output_dir: str, min_target: AvailableTarget, compute_units: ComputeUnit, precision: ComputePrecision):
    image_predictor.model.sam_prompt_encoder.eval()
    points = torch.tensor(input_points, dtype=torch.float32)
    labels = torch.tensor(input_labels, dtype=torch.int32)
    unnorm_coords = image_predictor._transforms.transform_coords(points, normalize=True, orig_hw=orig_hw)
    (unnorm_coords, labels) = (unnorm_coords[None, ...], labels[None, ...])
    traced_model = torch.jit.trace(SAM2PointsEncoder(image_predictor), (unnorm_coords, labels))
    points_shape = ct.Shape(shape=(1, ct.RangeDim(lower_bound=1, upper_bound=16), 2))
    labels_shape = ct.Shape(shape=(1, ct.RangeDim(lower_bound=1, upper_bound=16)))
    mlmodel = ct.convert(traced_model, inputs=[ct.TensorType(name='points', shape=points_shape), ct.TensorType(name='labels', shape=labels_shape)], outputs=[ct.TensorType(name='sparse_embeddings'), ct.TensorType(name='dense_embeddings')], minimum_deployment_target=min_target, compute_units=compute_units, compute_precision=precision)
    validate_prompt_encoder(mlmodel, image_predictor, unnorm_coords, labels)
    output_path = os.path.join(output_dir, f'SAM2{variant.fmt()}PromptEncoder{precision.value.upper()}')
    mlmodel.save(output_path + '.mlpackage')

def export_mask_decoder(image_predictor: SAM2ImagePredictor, variant: SAM2Variant, output_dir: str, min_target: AvailableTarget, compute_units: ComputeUnit, precision: ComputePrecision):
    image_predictor.model.sam_mask_decoder.eval()
    s0 = torch.randn(1, 32, 256, 256)
    s1 = torch.randn(1, 64, 128, 128)
    image_embedding = torch.randn(1, 256, 64, 64)
    sparse_embedding = torch.randn(1, 3, 256)
    dense_embedding = torch.randn(1, 256, 64, 64)
    traced_model = torch.jit.trace(SAM2MaskDecoder(image_predictor), (image_embedding, sparse_embedding, dense_embedding, s0, s1))
    traced_model.eval()
    mlmodel = ct.convert(traced_model, inputs=[ct.TensorType(name='image_embedding', shape=[1, 256, 64, 64]), ct.TensorType(name='sparse_embedding', shape=ct.EnumeratedShapes(shapes=[[1, i, 256] for i in range(2, 16)])), ct.TensorType(name='dense_embedding', shape=[1, 256, 64, 64]), ct.TensorType(name='feats_s0', shape=[1, 32, 256, 256]), ct.TensorType(name='feats_s1', shape=[1, 64, 128, 128])], outputs=[ct.TensorType(name='low_res_masks'), ct.TensorType(name='scores')], minimum_deployment_target=min_target, compute_units=compute_units, compute_precision=precision)
    validate_mask_decoder(mlmodel, image_predictor, image_embedding, sparse_embedding, dense_embedding, s0, s1, precision)
    output_path = os.path.join(output_dir, f'SAM2{variant.fmt()}MaskDecoder{precision.value.upper()}')
    mlmodel.save(output_path + '.mlpackage')
Point = Tuple[float, float]
Box = Tuple[float, float, float, float]

def export(output_dir: str, variant: SAM2Variant, points: Optional[List[Point]], boxes: Optional[List[Box]], labels: Optional[List[int]], min_target: AvailableTarget, compute_units: ComputeUnit, precision: ComputePrecision):
    os.makedirs(output_dir, exist_ok=True)
    device = torch.device('cpu')
    sam2_checkpoint = f'facebook/sam2-hiera-{variant.value}'
    with torch.no_grad():
        img_predictor = SAM2ImagePredictor.from_pretrained(sam2_checkpoint, device=device)
        img_predictor.model.eval()
        orig_hw = export_image_encoder(img_predictor, variant, output_dir, min_target, compute_units, precision)
        if boxes is not None and points is None:
            raise ValueError('Boxes are not supported yet')
        else:
            export_points_prompt_encoder(img_predictor, variant, points, labels, orig_hw, output_dir, min_target, compute_units, precision)
        export_mask_decoder(img_predictor, variant, output_dir, min_target, compute_units, precision)
if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='SAM2 -> CoreML CLI')
    parser = parse_args(parser)
    args = parser.parse_args()
    (points, boxes, labels) = (None, None, None)
    if args.points:
        points = [tuple(p) for p in ast.literal_eval(args.points)]
    if args.boxes:
        boxes = [tuple(b) for b in ast.literal_eval(args.boxes)]
    if args.labels:
        labels = ast.literal_eval(args.labels)
    if boxes and points:
        raise ValueError('Cannot provide both points and boxes')
    if points:
        if not isinstance(points, list) or not all((isinstance(p, tuple) and len(p) == 2 for p in points)):
            raise ValueError('Points must be a tuple of 2D points')
    if labels:
        if not isinstance(labels, list) or not all((isinstance(l, int) and l in [0, 1] for l in labels)):
            raise ValueError('Labels must denote foreground (1) or background (0)')
    if points:
        if len(points) != len(labels):
            raise ValueError('Number of points must match the number of labels')
        if len(points) > 16:
            raise ValueError('Number of points must be less than or equal to 16')
    if boxes:
        if not isinstance(boxes, list) or not all((isinstance(b, tuple) and len(b) == 4 for b in boxes)):
            raise ValueError('Boxes must be a tuple of 4D bounding boxes')
    export(args.output_dir, args.variant, points, boxes, labels, args.min_deployment_target, args.compute_units, args.precision)

# File: segment-anything-2-coreml-conversion/sam2/automatic_mask_generator.py
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import torch
from torchvision.ops.boxes import batched_nms, box_area
from sam2.modeling.sam2_base import SAM2Base
from sam2.sam2_image_predictor import SAM2ImagePredictor
from sam2.utils.amg import area_from_rle, batch_iterator, batched_mask_to_box, box_xyxy_to_xywh, build_all_layer_point_grids, calculate_stability_score, coco_encode_rle, generate_crop_boxes, is_box_near_crop_edge, mask_to_rle_pytorch, MaskData, remove_small_regions, rle_to_mask, uncrop_boxes_xyxy, uncrop_masks, uncrop_points

class SAM2AutomaticMaskGenerator:

    def __init__(self, model: SAM2Base, points_per_side: Optional[int]=32, points_per_batch: int=64, pred_iou_thresh: float=0.8, stability_score_thresh: float=0.95, stability_score_offset: float=1.0, mask_threshold: float=0.0, box_nms_thresh: float=0.7, crop_n_layers: int=0, crop_nms_thresh: float=0.7, crop_overlap_ratio: float=512 / 1500, crop_n_points_downscale_factor: int=1, point_grids: Optional[List[np.ndarray]]=None, min_mask_region_area: int=0, output_mode: str='binary_mask', use_m2m: bool=False, multimask_output: bool=True, **kwargs) -> None:
        assert (points_per_side is None) != (point_grids is None), 'Exactly one of points_per_side or point_grid must be provided.'
        if points_per_side is not None:
            self.point_grids = build_all_layer_point_grids(points_per_side, crop_n_layers, crop_n_points_downscale_factor)
        elif point_grids is not None:
            self.point_grids = point_grids
        else:
            raise ValueError("Can't have both points_per_side and point_grid be None.")
        assert output_mode in ['binary_mask', 'uncompressed_rle', 'coco_rle'], f'Unknown output_mode {output_mode}.'
        if output_mode == 'coco_rle':
            try:
                from pycocotools import mask as mask_utils
            except ImportError as e:
                print('Please install pycocotools')
                raise e
        self.predictor = SAM2ImagePredictor(model, max_hole_area=min_mask_region_area, max_sprinkle_area=min_mask_region_area)
        self.points_per_batch = points_per_batch
        self.pred_iou_thresh = pred_iou_thresh
        self.stability_score_thresh = stability_score_thresh
        self.stability_score_offset = stability_score_offset
        self.mask_threshold = mask_threshold
        self.box_nms_thresh = box_nms_thresh
        self.crop_n_layers = crop_n_layers
        self.crop_nms_thresh = crop_nms_thresh
        self.crop_overlap_ratio = crop_overlap_ratio
        self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
        self.min_mask_region_area = min_mask_region_area
        self.output_mode = output_mode
        self.use_m2m = use_m2m
        self.multimask_output = multimask_output

    @classmethod
    def from_pretrained(cls, model_id: str, **kwargs) -> 'SAM2AutomaticMaskGenerator':
        from sam2.build_sam import build_sam2_hf
        sam_model = build_sam2_hf(model_id, **kwargs)
        return cls(sam_model, **kwargs)

    @torch.no_grad()
    def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
        mask_data = self._generate_masks(image)
        if self.output_mode == 'coco_rle':
            mask_data['segmentations'] = [coco_encode_rle(rle) for rle in mask_data['rles']]
        elif self.output_mode == 'binary_mask':
            mask_data['segmentations'] = [rle_to_mask(rle) for rle in mask_data['rles']]
        else:
            mask_data['segmentations'] = mask_data['rles']
        curr_anns = []
        for idx in range(len(mask_data['segmentations'])):
            ann = {'segmentation': mask_data['segmentations'][idx], 'area': area_from_rle(mask_data['rles'][idx]), 'bbox': box_xyxy_to_xywh(mask_data['boxes'][idx]).tolist(), 'predicted_iou': mask_data['iou_preds'][idx].item(), 'point_coords': [mask_data['points'][idx].tolist()], 'stability_score': mask_data['stability_score'][idx].item(), 'crop_box': box_xyxy_to_xywh(mask_data['crop_boxes'][idx]).tolist()}
            curr_anns.append(ann)
        return curr_anns

    def _generate_masks(self, image: np.ndarray) -> MaskData:
        orig_size = image.shape[:2]
        (crop_boxes, layer_idxs) = generate_crop_boxes(orig_size, self.crop_n_layers, self.crop_overlap_ratio)
        data = MaskData()
        for (crop_box, layer_idx) in zip(crop_boxes, layer_idxs):
            crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
            data.cat(crop_data)
        if len(crop_boxes) > 1:
            scores = 1 / box_area(data['crop_boxes'])
            scores = scores.to(data['boxes'].device)
            keep_by_nms = batched_nms(data['boxes'].float(), scores, torch.zeros_like(data['boxes'][:, 0]), iou_threshold=self.crop_nms_thresh)
            data.filter(keep_by_nms)
        data.to_numpy()
        return data

    def _process_crop(self, image: np.ndarray, crop_box: List[int], crop_layer_idx: int, orig_size: Tuple[int, ...]) -> MaskData:
        (x0, y0, x1, y1) = crop_box
        cropped_im = image[y0:y1, x0:x1, :]
        cropped_im_size = cropped_im.shape[:2]
        self.predictor.set_image(cropped_im)
        points_scale = np.array(cropped_im_size)[None, ::-1]
        points_for_image = self.point_grids[crop_layer_idx] * points_scale
        data = MaskData()
        for (points,) in batch_iterator(self.points_per_batch, points_for_image):
            batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size, normalize=True)
            data.cat(batch_data)
            del batch_data
        self.predictor.reset_predictor()
        keep_by_nms = batched_nms(data['boxes'].float(), data['iou_preds'], torch.zeros_like(data['boxes'][:, 0]), iou_threshold=self.box_nms_thresh)
        data.filter(keep_by_nms)
        data['boxes'] = uncrop_boxes_xyxy(data['boxes'], crop_box)
        data['points'] = uncrop_points(data['points'], crop_box)
        data['crop_boxes'] = torch.tensor([crop_box for _ in range(len(data['rles']))])
        return data

    def _process_batch(self, points: np.ndarray, im_size: Tuple[int, ...], crop_box: List[int], orig_size: Tuple[int, ...], normalize=False) -> MaskData:
        (orig_h, orig_w) = orig_size
        points = torch.as_tensor(points, dtype=torch.float32, device=self.predictor.device)
        in_points = self.predictor._transforms.transform_coords(points, normalize=normalize, orig_hw=im_size)
        in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
        (masks, iou_preds, low_res_masks) = self.predictor._predict(in_points[:, None, :], in_labels[:, None], multimask_output=self.multimask_output, return_logits=True)
        data = MaskData(masks=masks.flatten(0, 1), iou_preds=iou_preds.flatten(0, 1), points=points.repeat_interleave(masks.shape[1], dim=0), low_res_masks=low_res_masks.flatten(0, 1))
        del masks
        if not self.use_m2m:
            if self.pred_iou_thresh > 0.0:
                keep_mask = data['iou_preds'] > self.pred_iou_thresh
                data.filter(keep_mask)
            data['stability_score'] = calculate_stability_score(data['masks'], self.mask_threshold, self.stability_score_offset)
            if self.stability_score_thresh > 0.0:
                keep_mask = data['stability_score'] >= self.stability_score_thresh
                data.filter(keep_mask)
        else:
            in_points = self.predictor._transforms.transform_coords(data['points'], normalize=normalize, orig_hw=im_size)
            labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
            (masks, ious) = self.refine_with_m2m(in_points, labels, data['low_res_masks'], self.points_per_batch)
            data['masks'] = masks.squeeze(1)
            data['iou_preds'] = ious.squeeze(1)
            if self.pred_iou_thresh > 0.0:
                keep_mask = data['iou_preds'] > self.pred_iou_thresh
                data.filter(keep_mask)
            data['stability_score'] = calculate_stability_score(data['masks'], self.mask_threshold, self.stability_score_offset)
            if self.stability_score_thresh > 0.0:
                keep_mask = data['stability_score'] >= self.stability_score_thresh
                data.filter(keep_mask)
        data['masks'] = data['masks'] > self.mask_threshold
        data['boxes'] = batched_mask_to_box(data['masks'])
        keep_mask = ~is_box_near_crop_edge(data['boxes'], crop_box, [0, 0, orig_w, orig_h])
        if not torch.all(keep_mask):
            data.filter(keep_mask)
        data['masks'] = uncrop_masks(data['masks'], crop_box, orig_h, orig_w)
        data['rles'] = mask_to_rle_pytorch(data['masks'])
        del data['masks']
        return data

    @staticmethod
    def postprocess_small_regions(mask_data: MaskData, min_area: int, nms_thresh: float) -> MaskData:
        if len(mask_data['rles']) == 0:
            return mask_data
        new_masks = []
        scores = []
        for rle in mask_data['rles']:
            mask = rle_to_mask(rle)
            (mask, changed) = remove_small_regions(mask, min_area, mode='holes')
            unchanged = not changed
            (mask, changed) = remove_small_regions(mask, min_area, mode='islands')
            unchanged = unchanged and (not changed)
            new_masks.append(torch.as_tensor(mask).unsqueeze(0))
            scores.append(float(unchanged))
        masks = torch.cat(new_masks, dim=0)
        boxes = batched_mask_to_box(masks)
        keep_by_nms = batched_nms(boxes.float(), torch.as_tensor(scores), torch.zeros_like(boxes[:, 0]), iou_threshold=nms_thresh)
        for i_mask in keep_by_nms:
            if scores[i_mask] == 0.0:
                mask_torch = masks[i_mask].unsqueeze(0)
                mask_data['rles'][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
                mask_data['boxes'][i_mask] = boxes[i_mask]
        mask_data.filter(keep_by_nms)
        return mask_data

    def refine_with_m2m(self, points, point_labels, low_res_masks, points_per_batch):
        new_masks = []
        new_iou_preds = []
        for (cur_points, cur_point_labels, low_res_mask) in batch_iterator(points_per_batch, points, point_labels, low_res_masks):
            (best_masks, best_iou_preds, _) = self.predictor._predict(cur_points[:, None, :], cur_point_labels[:, None], mask_input=low_res_mask[:, None, :], multimask_output=False, return_logits=True)
            new_masks.append(best_masks)
            new_iou_preds.append(best_iou_preds)
        masks = torch.cat(new_masks, dim=0)
        return (masks, torch.cat(new_iou_preds, dim=0))

# File: segment-anything-2-coreml-conversion/sam2/build_sam.py
import logging
import torch
from hydra import compose
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, **kwargs):
    if apply_postprocessing:
        hydra_overrides_extra = hydra_overrides_extra.copy()
        hydra_overrides_extra += ['++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']
    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, **kwargs):
    hydra_overrides = ['++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictor']
    if apply_postprocessing:
        hydra_overrides_extra = hydra_overrides_extra.copy()
        hydra_overrides_extra += ['++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', '++model.binarize_mask_from_pts_for_mem_enc=true', '++model.fill_hole_area=8']
    hydra_overrides.extend(hydra_overrides_extra)
    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_hf(model_id, **kwargs):
    from huggingface_hub import hf_hub_download
    model_id_to_filenames = {'facebook/sam2-hiera-tiny': ('sam2_hiera_t.yaml', 'sam2_hiera_tiny.pt'), 'facebook/sam2-hiera-small': ('sam2_hiera_s.yaml', 'sam2_hiera_small.pt'), 'facebook/sam2-hiera-base-plus': ('sam2_hiera_b+.yaml', 'sam2_hiera_base_plus.pt'), 'facebook/sam2-hiera-large': ('sam2_hiera_l.yaml', 'sam2_hiera_large.pt')}
    (config_name, checkpoint_name) = model_id_to_filenames[model_id]
    ckpt_path = hf_hub_download(repo_id=model_id, filename=checkpoint_name)
    return build_sam2(config_file=config_name, ckpt_path=ckpt_path, **kwargs)

def build_sam2_video_predictor_hf(model_id, **kwargs):
    from huggingface_hub import hf_hub_download
    model_id_to_filenames = {'facebook/sam2-hiera-tiny': ('sam2_hiera_t.yaml', 'sam2_hiera_tiny.pt'), 'facebook/sam2-hiera-small': ('sam2_hiera_s.yaml', 'sam2_hiera_small.pt'), 'facebook/sam2-hiera-base-plus': ('sam2_hiera_b+.yaml', 'sam2_hiera_base_plus.pt'), 'facebook/sam2-hiera-large': ('sam2_hiera_l.yaml', 'sam2_hiera_large.pt')}
    (config_name, checkpoint_name) = model_id_to_filenames[model_id]
    ckpt_path = hf_hub_download(repo_id=model_id, filename=checkpoint_name)
    return build_sam2_video_predictor(config_file=config_name, ckpt_path=ckpt_path, **kwargs)

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')

# File: segment-anything-2-coreml-conversion/sam2/modeling/backbones/hieradet.py
from functools import partial
from typing import List, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from sam2.modeling.backbones.utils import PatchEmbed, window_partition, window_unpartition
from sam2.modeling.sam2_utils import DropPath, MLP

def do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module=None) -> torch.Tensor:
    if pool is None:
        return x
    x = x.permute(0, 3, 1, 2)
    x = pool(x)
    x = x.permute(0, 2, 3, 1)
    if norm:
        x = norm(x)
    return x

class MultiScaleAttention(nn.Module):

    def __init__(self, dim: int, dim_out: int, num_heads: int, q_pool: nn.Module=None):
        super().__init__()
        self.dim = dim
        self.dim_out = dim_out
        self.num_heads = num_heads
        self.q_pool = q_pool
        self.qkv = nn.Linear(dim, dim_out * 3)
        self.proj = nn.Linear(dim_out, dim_out)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        (B, H, W, _) = x.shape
        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1)
        (q, k, v) = torch.unbind(qkv, 2)
        if self.q_pool:
            q = do_pool(q.reshape(B, H, W, -1), self.q_pool)
            (H, W) = q.shape[1:3]
            q = q.reshape(B, H * W, self.num_heads, -1)
        x = F.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2))
        x = x.transpose(1, 2)
        x = x.reshape(B, H, W, -1)
        x = self.proj(x)
        return x

class MultiScaleBlock(nn.Module):

    def __init__(self, dim: int, dim_out: int, num_heads: int, mlp_ratio: float=4.0, drop_path: float=0.0, norm_layer: Union[nn.Module, str]='LayerNorm', q_stride: Tuple[int, int]=None, act_layer: nn.Module=nn.GELU, window_size: int=0):
        super().__init__()
        if isinstance(norm_layer, str):
            norm_layer = partial(getattr(nn, norm_layer), eps=1e-06)
        self.dim = dim
        self.dim_out = dim_out
        self.norm1 = norm_layer(dim)
        self.window_size = window_size
        (self.pool, self.q_stride) = (None, q_stride)
        if self.q_stride:
            self.pool = nn.MaxPool2d(kernel_size=q_stride, stride=q_stride, ceil_mode=False)
        self.attn = MultiScaleAttention(dim, dim_out, num_heads=num_heads, q_pool=self.pool)
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim_out)
        self.mlp = MLP(dim_out, int(dim_out * mlp_ratio), dim_out, num_layers=2, activation=act_layer)
        if dim != dim_out:
            self.proj = nn.Linear(dim, dim_out)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shortcut = x
        x = self.norm1(x)
        if self.dim != self.dim_out:
            shortcut = do_pool(self.proj(x), self.pool)
        window_size = self.window_size
        if window_size > 0:
            (H, W) = (x.shape[1], x.shape[2])
            (x, pad_hw) = window_partition(x, window_size)
        x = self.attn(x)
        if self.q_stride:
            window_size = self.window_size // self.q_stride[0]
            (H, W) = shortcut.shape[1:3]
            pad_h = (window_size - H % window_size) % window_size
            pad_w = (window_size - W % window_size) % window_size
            pad_hw = (H + pad_h, W + pad_w)
        if self.window_size > 0:
            x = window_unpartition(x, window_size, pad_hw, (H, W))
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x

class Hiera(nn.Module):

    def __init__(self, embed_dim: int=96, num_heads: int=1, drop_path_rate: float=0.0, q_pool: int=3, q_stride: Tuple[int, int]=(2, 2), stages: Tuple[int, ...]=(2, 3, 16, 3), dim_mul: float=2.0, head_mul: float=2.0, window_pos_embed_bkg_spatial_size: Tuple[int, int]=(14, 14), window_spec: Tuple[int, ...]=(8, 4, 14, 7), global_att_blocks: Tuple[int, ...]=(12, 16, 20), return_interm_layers=True):
        super().__init__()
        assert len(stages) == len(window_spec)
        self.window_spec = window_spec
        depth = sum(stages)
        self.q_stride = q_stride
        self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)]
        assert 0 <= q_pool <= len(self.stage_ends[:-1])
        self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool]
        self.return_interm_layers = return_interm_layers
        self.patch_embed = PatchEmbed(embed_dim=embed_dim)
        self.global_att_blocks = global_att_blocks
        self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size
        self.pos_embed = nn.Parameter(torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size))
        self.pos_embed_window = nn.Parameter(torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0]))
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
        cur_stage = 1
        self.blocks = nn.ModuleList()
        for i in range(depth):
            dim_out = embed_dim
            window_size = self.window_spec[cur_stage - 1]
            if self.global_att_blocks is not None:
                window_size = 0 if i in self.global_att_blocks else window_size
            if i - 1 in self.stage_ends:
                dim_out = int(embed_dim * dim_mul)
                num_heads = int(num_heads * head_mul)
                cur_stage += 1
            block = MultiScaleBlock(dim=embed_dim, dim_out=dim_out, num_heads=num_heads, drop_path=dpr[i], q_stride=self.q_stride if i in self.q_pool_blocks else None, window_size=window_size)
            embed_dim = dim_out
            self.blocks.append(block)
        self.channel_list = [self.blocks[i].dim_out for i in self.stage_ends[::-1]] if return_interm_layers else [self.blocks[-1].dim_out]

    def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor:
        (h, w) = hw
        window_embed = self.pos_embed_window
        pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode='bicubic')
        tiles = [x // y for (x, y) in zip(pos_embed.shape, window_embed.shape)]
        pos_embed = pos_embed + window_embed.tile(tiles)
        pos_embed = pos_embed.permute(0, 2, 3, 1)
        return pos_embed

    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
        x = self.patch_embed(x)
        x = x + self._get_pos_embed(x.shape[1:3])
        outputs = []
        for (i, blk) in enumerate(self.blocks):
            x = blk(x)
            if i == self.stage_ends[-1] or (i in self.stage_ends and self.return_interm_layers):
                feats = x.permute(0, 3, 1, 2)
                outputs.append(feats)
        return outputs

# File: segment-anything-2-coreml-conversion/sam2/modeling/backbones/image_encoder.py
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F

class ImageEncoder(nn.Module):

    def __init__(self, trunk: nn.Module, neck: nn.Module, scalp: int=0):
        super().__init__()
        self.trunk = trunk
        self.neck = neck
        self.scalp = scalp
        assert self.trunk.channel_list == self.neck.backbone_channel_list, f'Channel dims of trunk and neck do not match. Trunk: {self.trunk.channel_list}, neck: {self.neck.backbone_channel_list}'

    def forward(self, sample: torch.Tensor):
        (features, pos) = self.neck(self.trunk(sample))
        if self.scalp > 0:
            (features, pos) = (features[:-self.scalp], pos[:-self.scalp])
        src = features[-1]
        output = {'vision_features': src, 'vision_pos_enc': pos, 'backbone_fpn': features}
        return output

class FpnNeck(nn.Module):

    def __init__(self, position_encoding: nn.Module, d_model: int, backbone_channel_list: List[int], kernel_size: int=1, stride: int=1, padding: int=0, fpn_interp_model: str='bilinear', fuse_type: str='sum', fpn_top_down_levels: Optional[List[int]]=None):
        super().__init__()
        self.position_encoding = position_encoding
        self.convs = nn.ModuleList()
        self.backbone_channel_list = backbone_channel_list
        for dim in backbone_channel_list:
            current = nn.Sequential()
            current.add_module('conv', nn.Conv2d(in_channels=dim, out_channels=d_model, kernel_size=kernel_size, stride=stride, padding=padding))
            self.convs.append(current)
        self.fpn_interp_model = fpn_interp_model
        assert fuse_type in ['sum', 'avg']
        self.fuse_type = fuse_type
        if fpn_top_down_levels is None:
            fpn_top_down_levels = range(len(self.convs))
        self.fpn_top_down_levels = list(fpn_top_down_levels)

    def forward(self, xs: List[torch.Tensor]):
        out = [None] * len(self.convs)
        pos = [None] * len(self.convs)
        assert len(xs) == len(self.convs)
        prev_features = None
        n = len(self.convs) - 1
        for i in range(n, -1, -1):
            x = xs[i]
            lateral_features = self.convs[n - i](x)
            if i in self.fpn_top_down_levels and prev_features is not None:
                top_down_features = F.interpolate(prev_features.to(dtype=torch.float32), scale_factor=2.0, mode=self.fpn_interp_model, align_corners=None if self.fpn_interp_model == 'nearest' else False, antialias=False)
                prev_features = lateral_features + top_down_features
                if self.fuse_type == 'avg':
                    prev_features /= 2
            else:
                prev_features = lateral_features
            x_out = prev_features
            out[i] = x_out
            pos[i] = self.position_encoding(x_out).to(x_out.dtype)
        return (out, pos)

# File: segment-anything-2-coreml-conversion/sam2/modeling/backbones/utils.py
""""""
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F

def window_partition(x, window_size):
    (B, H, W, C) = x.shape
    pad_h = (window_size - H % window_size) % window_size
    pad_w = (window_size - W % window_size) % window_size
    if pad_h > 0 or pad_w > 0:
        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
    (Hp, Wp) = (H + pad_h, W + pad_w)
    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return (windows, (Hp, Wp))

def window_unpartition(windows, window_size, pad_hw, hw):
    (Hp, Wp) = pad_hw
    (H, W) = hw
    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
    if Hp > H or Wp > W:
        x = x[:, :H, :W, :].contiguous()
    return x

class PatchEmbed(nn.Module):

    def __init__(self, kernel_size: Tuple[int, ...]=(7, 7), stride: Tuple[int, ...]=(4, 4), padding: Tuple[int, ...]=(3, 3), in_chans: int=3, embed_dim: int=768):
        super().__init__()
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.proj(x)
        x = x.permute(0, 2, 3, 1)
        return x

# File: segment-anything-2-coreml-conversion/sam2/modeling/memory_attention.py
from typing import Optional
import torch
from torch import nn, Tensor
from sam2.modeling.sam.transformer import RoPEAttention
from sam2.modeling.sam2_utils import get_activation_fn, get_clones

class MemoryAttentionLayer(nn.Module):

    def __init__(self, activation: str, cross_attention: nn.Module, d_model: int, dim_feedforward: int, dropout: float, pos_enc_at_attn: bool, pos_enc_at_cross_attn_keys: bool, pos_enc_at_cross_attn_queries: bool, self_attention: nn.Module):
        super().__init__()
        self.d_model = d_model
        self.dim_feedforward = dim_feedforward
        self.dropout_value = dropout
        self.self_attn = self_attention
        self.cross_attn_image = cross_attention
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)
        self.activation_str = activation
        self.activation = get_activation_fn(activation)
        self.pos_enc_at_attn = pos_enc_at_attn
        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys

    def _forward_sa(self, tgt, query_pos):
        tgt2 = self.norm1(tgt)
        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
        tgt2 = self.self_attn(q, k, v=tgt2)
        tgt = tgt + self.dropout1(tgt2)
        return tgt

    def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):
        kwds = {}
        if num_k_exclude_rope > 0:
            assert isinstance(self.cross_attn_image, RoPEAttention)
            kwds = {'num_k_exclude_rope': num_k_exclude_rope}
        tgt2 = self.norm2(tgt)
        tgt2 = self.cross_attn_image(q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2, k=memory + pos if self.pos_enc_at_cross_attn_keys else memory, v=memory, **kwds)
        tgt = tgt + self.dropout2(tgt2)
        return tgt

    def forward(self, tgt, memory, pos: Optional[Tensor]=None, query_pos: Optional[Tensor]=None, num_k_exclude_rope: int=0) -> torch.Tensor:
        tgt = self._forward_sa(tgt, query_pos)
        tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)
        tgt2 = self.norm3(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
        tgt = tgt + self.dropout3(tgt2)
        return tgt

class MemoryAttention(nn.Module):

    def __init__(self, d_model: int, pos_enc_at_input: bool, layer: nn.Module, num_layers: int, batch_first: bool=True):
        super().__init__()
        self.d_model = d_model
        self.layers = get_clones(layer, num_layers)
        self.num_layers = num_layers
        self.norm = nn.LayerNorm(d_model)
        self.pos_enc_at_input = pos_enc_at_input
        self.batch_first = batch_first

    def forward(self, curr: torch.Tensor, memory: torch.Tensor, curr_pos: Optional[Tensor]=None, memory_pos: Optional[Tensor]=None, num_obj_ptr_tokens: int=0):
        if isinstance(curr, list):
            assert isinstance(curr_pos, list)
            assert len(curr) == len(curr_pos) == 1
            (curr, curr_pos) = (curr[0], curr_pos[0])
        assert curr.shape[1] == memory.shape[1], 'Batch size must be the same for curr and memory'
        output = curr
        if self.pos_enc_at_input and curr_pos is not None:
            output = output + 0.1 * curr_pos
        if self.batch_first:
            output = output.transpose(0, 1)
            curr_pos = curr_pos.transpose(0, 1)
            memory = memory.transpose(0, 1)
            memory_pos = memory_pos.transpose(0, 1)
        for layer in self.layers:
            kwds = {}
            if isinstance(layer.cross_attn_image, RoPEAttention):
                kwds = {'num_k_exclude_rope': num_obj_ptr_tokens}
            output = layer(tgt=output, memory=memory, pos=memory_pos, query_pos=curr_pos, **kwds)
        normed_output = self.norm(output)
        if self.batch_first:
            normed_output = normed_output.transpose(0, 1)
            curr_pos = curr_pos.transpose(0, 1)
        return normed_output

# File: segment-anything-2-coreml-conversion/sam2/modeling/memory_encoder.py
import math
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from sam2.modeling.sam2_utils import DropPath, get_clones, LayerNorm2d

class MaskDownSampler(nn.Module):

    def __init__(self, embed_dim=256, kernel_size=4, stride=4, padding=0, total_stride=16, activation=nn.GELU):
        super().__init__()
        num_layers = int(math.log2(total_stride) // math.log2(stride))
        assert stride ** num_layers == total_stride
        self.encoder = nn.Sequential()
        (mask_in_chans, mask_out_chans) = (1, 1)
        for _ in range(num_layers):
            mask_out_chans = mask_in_chans * stride ** 2
            self.encoder.append(nn.Conv2d(mask_in_chans, mask_out_chans, kernel_size=kernel_size, stride=stride, padding=padding))
            self.encoder.append(LayerNorm2d(mask_out_chans))
            self.encoder.append(activation())
            mask_in_chans = mask_out_chans
        self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1))

    def forward(self, x):
        return self.encoder(x)

class CXBlock(nn.Module):

    def __init__(self, dim, kernel_size=7, padding=3, drop_path=0.0, layer_scale_init_value=1e-06, use_dwconv=True):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=kernel_size, padding=padding, groups=dim if use_dwconv else 1)
        self.norm = LayerNorm2d(dim, eps=1e-06)
        self.pwconv1 = nn.Linear(dim, 4 * dim)
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True) if layer_scale_init_value > 0 else None
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()

    def forward(self, x):
        input = x
        x = self.dwconv(x)
        x = self.norm(x)
        x = x.permute(0, 2, 3, 1)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.gamma is not None:
            x = self.gamma * x
        x = x.permute(0, 3, 1, 2)
        x = input + self.drop_path(x)
        return x

class Fuser(nn.Module):

    def __init__(self, layer, num_layers, dim=None, input_projection=False):
        super().__init__()
        self.proj = nn.Identity()
        self.layers = get_clones(layer, num_layers)
        if input_projection:
            assert dim is not None
            self.proj = nn.Conv2d(dim, dim, kernel_size=1)

    def forward(self, x):
        x = self.proj(x)
        for layer in self.layers:
            x = layer(x)
        return x

class MemoryEncoder(nn.Module):

    def __init__(self, out_dim, mask_downsampler, fuser, position_encoding, in_dim=256):
        super().__init__()
        self.mask_downsampler = mask_downsampler
        self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)
        self.fuser = fuser
        self.position_encoding = position_encoding
        self.out_proj = nn.Identity()
        if out_dim != in_dim:
            self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)

    def forward(self, pix_feat: torch.Tensor, masks: torch.Tensor, skip_mask_sigmoid: bool=False) -> Tuple[torch.Tensor, torch.Tensor]:
        if not skip_mask_sigmoid:
            masks = F.sigmoid(masks)
        masks = self.mask_downsampler(masks)
        pix_feat = pix_feat.to(masks.device)
        x = self.pix_feat_proj(pix_feat)
        x = x + masks
        x = self.fuser(x)
        x = self.out_proj(x)
        pos = self.position_encoding(x).to(x.dtype)
        return {'vision_features': x, 'vision_pos_enc': [pos]}

# File: segment-anything-2-coreml-conversion/sam2/modeling/position_encoding.py
import math
from typing import Any, Optional, Tuple
import numpy as np
import torch
from torch import nn

class PositionEmbeddingSine(nn.Module):

    def __init__(self, num_pos_feats, temperature: int=10000, normalize: bool=True, scale: Optional[float]=None):
        super().__init__()
        assert num_pos_feats % 2 == 0, 'Expecting even model width'
        self.num_pos_feats = num_pos_feats // 2
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError('normalize should be True if scale is passed')
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale
        self.cache = {}

    def _encode_xy(self, x, y):
        assert len(x) == len(y) and x.ndim == y.ndim == 1
        x_embed = x * self.scale
        y_embed = y * self.scale
        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
        pos_x = x_embed[:, None] / dim_t
        pos_y = y_embed[:, None] / dim_t
        pos_x = torch.stack((pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2).flatten(1)
        pos_y = torch.stack((pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2).flatten(1)
        return (pos_x, pos_y)

    @torch.no_grad()
    def encode_boxes(self, x, y, w, h):
        (pos_x, pos_y) = self._encode_xy(x, y)
        pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
        return pos
    encode = encode_boxes

    @torch.no_grad()
    def encode_points(self, x, y, labels):
        ((bx, nx), (by, ny), (bl, nl)) = (x.shape, y.shape, labels.shape)
        assert bx == by and nx == ny and (bx == bl) and (nx == nl)
        (pos_x, pos_y) = self._encode_xy(x.flatten(), y.flatten())
        (pos_x, pos_y) = (pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1))
        pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)
        return pos

    @torch.no_grad()
    def forward(self, x: torch.Tensor):
        cache_key = (x.shape[-2], x.shape[-1])
        if cache_key in self.cache:
            return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1)
        y_embed = torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device).view(1, -1, 1).repeat(x.shape[0], 1, x.shape[-1])
        x_embed = torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device).view(1, 1, -1).repeat(x.shape[0], x.shape[-2], 1)
        if self.normalize:
            eps = 1e-06
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        self.cache[cache_key] = pos[0]
        return pos

class PositionEmbeddingRandom(nn.Module):

    def __init__(self, num_pos_feats: int=64, scale: Optional[float]=None) -> None:
        super().__init__()
        if scale is None or scale <= 0.0:
            scale = 1.0
        self.register_buffer('positional_encoding_gaussian_matrix', scale * torch.randn((2, num_pos_feats)))

    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
        coords = 2 * coords - 1
        coords = coords @ self.positional_encoding_gaussian_matrix
        coords = 2 * np.pi * coords
        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)

    def forward(self, size: Tuple[int, int]) -> torch.Tensor:
        (h, w) = size
        device: Any = self.positional_encoding_gaussian_matrix.device
        grid = torch.ones((h, w), device=device, dtype=torch.float32)
        y_embed = grid.cumsum(dim=0) - 0.5
        x_embed = grid.cumsum(dim=1) - 0.5
        y_embed = y_embed / h
        x_embed = x_embed / w
        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
        return pe.permute(2, 0, 1)

    def forward_with_coords(self, coords_input: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
        coords = coords_input.clone()
        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
        return self._pe_encoding(coords.to(torch.float))

def init_t_xy(end_x: int, end_y: int):
    t = torch.arange(end_x * end_y, dtype=torch.float32)
    t_x = (t % end_x).float()
    t_y = torch.div(t, end_x, rounding_mode='floor').float()
    return (t_x, t_y)

def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float=10000.0):
    freqs_x = 1.0 / theta ** (torch.arange(0, dim, 4)[:dim // 4].float() / dim)
    freqs_y = 1.0 / theta ** (torch.arange(0, dim, 4)[:dim // 4].float() / dim)
    (t_x, t_y) = init_t_xy(end_x, end_y)
    freqs_x = torch.outer(t_x, freqs_x)
    freqs_y = torch.outer(t_y, freqs_y)
    freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)
    freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)
    return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)

def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
    ndim = x.ndim
    assert 0 <= 1 < ndim
    assert freqs_cis.shape == (x.shape[-2], x.shape[-1])
    shape = [d if i >= ndim - 2 else 1 for (i, d) in enumerate(x.shape)]
    return freqs_cis.view(*shape)

def apply_rotary_enc(xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, repeat_freqs_k: bool=False):
    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) if xk.shape[-2] != 0 else None
    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
    if xk_ is None:
        return (xq_out.type_as(xq).to(xq.device), xk)
    if repeat_freqs_k:
        r = xk_.shape[-2] // xq_.shape[-2]
        if freqs_cis.is_cuda:
            freqs_cis = freqs_cis.repeat(*[1] * (freqs_cis.ndim - 2), r, 1)
        else:
            freqs_cis = freqs_cis.unsqueeze(2).expand(-1, -1, r, -1, -1).flatten(2, 3)
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return (xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device))

# File: segment-anything-2-coreml-conversion/sam2/modeling/sam/mask_decoder.py
from typing import List, Optional, Tuple, Type
import torch
from torch import nn
from sam2.modeling.sam2_utils import LayerNorm2d, MLP

class MaskDecoder(nn.Module):

    def __init__(self, *, transformer_dim: int, transformer: nn.Module, num_multimask_outputs: int=3, activation: Type[nn.Module]=nn.GELU, iou_head_depth: int=3, iou_head_hidden_dim: int=256, use_high_res_features: bool=False, iou_prediction_use_sigmoid=False, dynamic_multimask_via_stability=False, dynamic_multimask_stability_delta=0.05, dynamic_multimask_stability_thresh=0.98, pred_obj_scores: bool=False, pred_obj_scores_mlp: bool=False, use_multimask_token_for_obj_ptr: bool=False) -> None:
        super().__init__()
        self.transformer_dim = transformer_dim
        self.transformer = transformer
        self.num_multimask_outputs = num_multimask_outputs
        self.iou_token = nn.Embedding(1, transformer_dim)
        self.num_mask_tokens = num_multimask_outputs + 1
        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
        self.pred_obj_scores = pred_obj_scores
        if self.pred_obj_scores:
            self.obj_score_token = nn.Embedding(1, transformer_dim)
        self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr
        self.output_upscaling = nn.Sequential(nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2), LayerNorm2d(transformer_dim // 4), activation(), nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2), activation())
        self.use_high_res_features = use_high_res_features
        if use_high_res_features:
            self.conv_s0 = nn.Conv2d(transformer_dim, transformer_dim // 8, kernel_size=1, stride=1)
            self.conv_s1 = nn.Conv2d(transformer_dim, transformer_dim // 4, kernel_size=1, stride=1)
        self.output_hypernetworks_mlps = nn.ModuleList([MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for i in range(self.num_mask_tokens)])
        self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth, sigmoid_output=iou_prediction_use_sigmoid)
        if self.pred_obj_scores:
            self.pred_obj_score_head = nn.Linear(transformer_dim, 1)
            if pred_obj_scores_mlp:
                self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)
        self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
        self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
        self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh

    def forward(self, image_embeddings: torch.Tensor, image_pe: torch.Tensor, sparse_prompt_embeddings: torch.Tensor, dense_prompt_embeddings: torch.Tensor, multimask_output: bool, repeat_image: bool, high_res_features: Optional[List[torch.Tensor]]=None) -> Tuple[torch.Tensor, torch.Tensor]:
        (masks, iou_pred, mask_tokens_out, object_score_logits) = self.predict_masks(image_embeddings=image_embeddings, image_pe=image_pe, sparse_prompt_embeddings=sparse_prompt_embeddings, dense_prompt_embeddings=dense_prompt_embeddings, repeat_image=repeat_image, high_res_features=high_res_features)
        if multimask_output:
            masks = masks[:, 1:, :, :]
            iou_pred = iou_pred[:, 1:]
        elif self.dynamic_multimask_via_stability and (not self.training):
            (masks, iou_pred) = self._dynamic_multimask_via_stability(masks, iou_pred)
        else:
            masks = masks[:, 0:1, :, :]
            iou_pred = iou_pred[:, 0:1]
        if multimask_output and self.use_multimask_token_for_obj_ptr:
            sam_tokens_out = mask_tokens_out[:, 1:]
        else:
            sam_tokens_out = mask_tokens_out[:, 0:1]
        return (masks, iou_pred, sam_tokens_out, object_score_logits)

    def predict_masks(self, image_embeddings: torch.Tensor, image_pe: torch.Tensor, sparse_prompt_embeddings: torch.Tensor, dense_prompt_embeddings: torch.Tensor, repeat_image: bool, high_res_features: Optional[List[torch.Tensor]]=None) -> Tuple[torch.Tensor, torch.Tensor]:
        s = 0
        if self.pred_obj_scores:
            output_tokens = torch.cat([self.obj_score_token.weight, self.iou_token.weight, self.mask_tokens.weight], dim=0)
            s = 1
        else:
            output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
        if repeat_image:
            src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
        else:
            assert image_embeddings.shape[0] == tokens.shape[0]
            src = image_embeddings
        src = src + dense_prompt_embeddings
        assert image_pe.size(0) == 1, 'image_pe should have size 1 in batch dim (from `get_dense_pe()`)'
        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
        (b, c, h, w) = src.shape
        (hs, src) = self.transformer(src, pos_src, tokens)
        iou_token_out = hs[:, s, :]
        mask_tokens_out = hs[:, s + 1:s + 1 + self.num_mask_tokens, :]
        src = src.transpose(1, 2).view(b, c, h, w)
        if not self.use_high_res_features:
            upscaled_embedding = self.output_upscaling(src)
        else:
            (dc1, ln1, act1, dc2, act2) = self.output_upscaling
            (feat_s0, feat_s1) = high_res_features
            upscaled_embedding = act1(ln1(dc1(src) + feat_s1))
            upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)
        hyper_in_list: List[torch.Tensor] = []
        for i in range(self.num_mask_tokens):
            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
        hyper_in = torch.stack(hyper_in_list, dim=1)
        (b, c, h, w) = upscaled_embedding.shape
        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
        iou_pred = self.iou_prediction_head(iou_token_out)
        if self.pred_obj_scores:
            assert s == 1
            object_score_logits = self.pred_obj_score_head(hs[:, 0, :])
        else:
            object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1)
        return (masks, iou_pred, mask_tokens_out, object_score_logits)

    def _get_stability_scores(self, mask_logits):
        mask_logits = mask_logits.flatten(-2)
        stability_delta = self.dynamic_multimask_stability_delta
        area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()
        area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()
        stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)
        return stability_scores

    def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
        multimask_logits = all_mask_logits[:, 1:, :, :]
        multimask_iou_scores = all_iou_scores[:, 1:]
        best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)
        batch_inds = torch.arange(multimask_iou_scores.size(0), device=all_iou_scores.device)
        best_multimask_logits = multimask_logits[batch_inds, best_scores_inds]
        best_multimask_logits = best_multimask_logits.unsqueeze(1)
        best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds]
        best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1)
        singlemask_logits = all_mask_logits[:, 0:1, :, :]
        singlemask_iou_scores = all_iou_scores[:, 0:1]
        stability_scores = self._get_stability_scores(singlemask_logits)
        is_stable = stability_scores >= self.dynamic_multimask_stability_thresh
        mask_logits_out = torch.where(is_stable[..., None, None].expand_as(singlemask_logits), singlemask_logits, best_multimask_logits)
        iou_scores_out = torch.where(is_stable.expand_as(singlemask_iou_scores), singlemask_iou_scores, best_multimask_iou_scores)
        return (mask_logits_out, iou_scores_out)

# File: segment-anything-2-coreml-conversion/sam2/modeling/sam/prompt_encoder.py
from typing import Optional, Tuple, Type
import torch
from torch import nn
from sam2.modeling.position_encoding import PositionEmbeddingRandom
from sam2.modeling.sam2_utils import LayerNorm2d

class PromptEncoder(nn.Module):

    def __init__(self, embed_dim: int, image_embedding_size: Tuple[int, int], input_image_size: Tuple[int, int], mask_in_chans: int, activation: Type[nn.Module]=nn.GELU) -> None:
        super().__init__()
        self.embed_dim = embed_dim
        self.input_image_size = input_image_size
        self.image_embedding_size = image_embedding_size
        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
        self.num_point_embeddings: int = 4
        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
        self.point_embeddings = nn.ModuleList(point_embeddings)
        self.not_a_point_embed = nn.Embedding(1, embed_dim)
        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
        self.mask_downscaling = nn.Sequential(nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2), LayerNorm2d(mask_in_chans // 4), activation(), nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2), LayerNorm2d(mask_in_chans), activation(), nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1))
        self.no_mask_embed = nn.Embedding(1, embed_dim)

    def get_dense_pe(self) -> torch.Tensor:
        return self.pe_layer(self.image_embedding_size).unsqueeze(0)

    def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor:
        points = points + 0.5
        if pad:
            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
            points = torch.cat([points, padding_point], dim=1)
            labels = torch.cat([labels, padding_label], dim=1)
        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
        mask_not_a_point = (labels == -1).float().unsqueeze(-1)
        mask_label_0 = (labels == 0).float().unsqueeze(-1)
        mask_label_1 = (labels == 1).float().unsqueeze(-1)
        mask_label_2 = (labels == 2).float().unsqueeze(-1)
        mask_label_3 = (labels == 3).float().unsqueeze(-1)
        point_embedding = point_embedding * (1 - mask_not_a_point) + self.not_a_point_embed.weight * mask_not_a_point + self.point_embeddings[0].weight * mask_label_0 + self.point_embeddings[1].weight * mask_label_1 + self.point_embeddings[2].weight * mask_label_2 + self.point_embeddings[3].weight * mask_label_3
        return point_embedding

    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
        boxes = boxes + 0.5
        coords = boxes.reshape(-1, 2, 2)
        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
        return corner_embedding

    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
        mask_embedding = self.mask_downscaling(masks)
        return mask_embedding

    def _get_batch_size(self, points: Optional[Tuple[torch.Tensor, torch.Tensor]], boxes: Optional[torch.Tensor], masks: Optional[torch.Tensor]) -> int:
        if points is not None:
            return points[0].shape[0]
        elif boxes is not None:
            return boxes.shape[0]
        elif masks is not None:
            return masks.shape[0]
        else:
            return 1

    def _get_device(self) -> torch.device:
        return self.point_embeddings[0].weight.device

    def forward(self, points: Optional[Tuple[torch.Tensor, torch.Tensor]], boxes: Optional[torch.Tensor], masks: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
        bs = self._get_batch_size(points, boxes, masks)
        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
        if points is not None:
            (coords, labels) = points
            point_embeddings = self._embed_points(coords, labels, pad=boxes is None)
            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
        if boxes is not None:
            box_embeddings = self._embed_boxes(boxes)
            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
        if masks is not None:
            dense_embeddings = self._embed_masks(masks)
        else:
            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(bs, -1, self.image_embedding_size[0], self.image_embedding_size[1])
        return (sparse_embeddings, dense_embeddings)

    def points_only(self, points: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
        (coords, labels) = points
        sparse_embeddings = self._embed_points(coords, labels, pad=True)
        bs = points[0].shape[0]
        dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(bs, -1, self.image_embedding_size[0], self.image_embedding_size[1])
        return (sparse_embeddings, dense_embeddings)

# File: segment-anything-2-coreml-conversion/sam2/modeling/sam/transformer.py
import contextlib
import math
import warnings
from functools import partial
from typing import Tuple, Type
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from sam2.modeling.position_encoding import apply_rotary_enc, compute_axial_cis
from sam2.modeling.sam2_utils import MLP
from sam2.utils.misc import get_sdpa_settings
warnings.simplefilter(action='ignore', category=FutureWarning)
(OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON) = get_sdpa_settings()
ALLOW_ALL_KERNELS = False

def sdp_kernel_context(dropout_p):
    if ALLOW_ALL_KERNELS:
        return contextlib.nullcontext()
    return torch.backends.cuda.sdp_kernel(enable_flash=USE_FLASH_ATTN, enable_math=OLD_GPU and dropout_p > 0.0 or MATH_KERNEL_ON, enable_mem_efficient=OLD_GPU)

class TwoWayTransformer(nn.Module):

    def __init__(self, depth: int, embedding_dim: int, num_heads: int, mlp_dim: int, activation: Type[nn.Module]=nn.ReLU, attention_downsample_rate: int=2) -> None:
        super().__init__()
        self.depth = depth
        self.embedding_dim = embedding_dim
        self.num_heads = num_heads
        self.mlp_dim = mlp_dim
        self.layers = nn.ModuleList()
        for i in range(depth):
            self.layers.append(TwoWayAttentionBlock(embedding_dim=embedding_dim, num_heads=num_heads, mlp_dim=mlp_dim, activation=activation, attention_downsample_rate=attention_downsample_rate, skip_first_layer_pe=i == 0))
        self.final_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
        self.norm_final_attn = nn.LayerNorm(embedding_dim)

    def forward(self, image_embedding: Tensor, image_pe: Tensor, point_embedding: Tensor) -> Tuple[Tensor, Tensor]:
        (bs, c, h, w) = image_embedding.shape
        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
        image_pe = image_pe.flatten(2).permute(0, 2, 1)
        queries = point_embedding
        keys = image_embedding
        for layer in self.layers:
            (queries, keys) = layer(queries=queries, keys=keys, query_pe=point_embedding, key_pe=image_pe)
        q = queries + point_embedding
        k = keys + image_pe
        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
        queries = queries + attn_out
        queries = self.norm_final_attn(queries)
        return (queries, keys)

class TwoWayAttentionBlock(nn.Module):

    def __init__(self, embedding_dim: int, num_heads: int, mlp_dim: int=2048, activation: Type[nn.Module]=nn.ReLU, attention_downsample_rate: int=2, skip_first_layer_pe: bool=False) -> None:
        super().__init__()
        self.self_attn = Attention(embedding_dim, num_heads)
        self.norm1 = nn.LayerNorm(embedding_dim)
        self.cross_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
        self.norm2 = nn.LayerNorm(embedding_dim)
        self.mlp = MLP(embedding_dim, mlp_dim, embedding_dim, num_layers=2, activation=activation)
        self.norm3 = nn.LayerNorm(embedding_dim)
        self.norm4 = nn.LayerNorm(embedding_dim)
        self.cross_attn_image_to_token = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
        self.skip_first_layer_pe = skip_first_layer_pe

    def forward(self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor) -> Tuple[Tensor, Tensor]:
        if self.skip_first_layer_pe:
            queries = self.self_attn(q=queries, k=queries, v=queries)
        else:
            q = queries + query_pe
            attn_out = self.self_attn(q=q, k=q, v=queries)
            queries = queries + attn_out
        queries = self.norm1(queries)
        q = queries + query_pe
        k = keys + key_pe
        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
        queries = queries + attn_out
        queries = self.norm2(queries)
        mlp_out = self.mlp(queries)
        queries = queries + mlp_out
        queries = self.norm3(queries)
        q = queries + query_pe
        k = keys + key_pe
        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
        keys = keys + attn_out
        keys = self.norm4(keys)
        return (queries, keys)

class Attention(nn.Module):

    def __init__(self, embedding_dim: int, num_heads: int, downsample_rate: int=1, dropout: float=0.0, kv_in_dim: int=None) -> None:
        super().__init__()
        self.embedding_dim = embedding_dim
        self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
        self.internal_dim = embedding_dim // downsample_rate
        self.num_heads = num_heads
        assert self.internal_dim % num_heads == 0, 'num_heads must divide embedding_dim.'
        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
        self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
        self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
        self.dropout_p = dropout

    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
        (b, n, c) = x.shape
        x = x.reshape(b, n, num_heads, c // num_heads)
        return x.transpose(1, 2)

    def _recombine_heads(self, x: Tensor) -> Tensor:
        (b, n_heads, n_tokens, c_per_head) = x.shape
        x = x.transpose(1, 2)
        return x.reshape(b, n_tokens, n_heads * c_per_head)

    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
        q = self.q_proj(q)
        k = self.k_proj(k)
        v = self.v_proj(v)
        q = self._separate_heads(q, self.num_heads)
        k = self._separate_heads(k, self.num_heads)
        v = self._separate_heads(v, self.num_heads)
        dropout_p = self.dropout_p if self.training else 0.0
        try:
            with sdp_kernel_context(dropout_p):
                out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
        except Exception as e:
            warnings.warn(f'Flash Attention kernel failed due to: {e}\nFalling back to all available kernels for scaled_dot_product_attention (which may have a slower speed).', category=UserWarning, stacklevel=2)
            global ALLOW_ALL_KERNELS
            ALLOW_ALL_KERNELS = True
            out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
        out = self._recombine_heads(out)
        out = self.out_proj(out)
        return out

class RoPEAttention(Attention):

    def __init__(self, *args, rope_theta=10000.0, rope_k_repeat=False, feat_sizes=(32, 32), **kwargs):
        super().__init__(*args, **kwargs)
        self.compute_cis = partial(compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta)
        freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1])
        self.freqs_cis = freqs_cis
        self.rope_k_repeat = rope_k_repeat

    def forward(self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int=0) -> Tensor:
        q = self.q_proj(q)
        k = self.k_proj(k)
        v = self.v_proj(v)
        q = self._separate_heads(q, self.num_heads)
        k = self._separate_heads(k, self.num_heads)
        v = self._separate_heads(v, self.num_heads)
        w = h = math.sqrt(q.shape[-2])
        self.freqs_cis = self.freqs_cis.to(q.device)
        if self.freqs_cis.shape[0] != q.shape[-2]:
            self.freqs_cis = self.compute_cis(end_x=w, end_y=h).to(q.device)
        if q.shape[-2] != k.shape[-2]:
            assert self.rope_k_repeat
        num_k_rope = k.size(-2) - num_k_exclude_rope
        (q, k[:, :, :num_k_rope]) = apply_rotary_enc(q, k[:, :, :num_k_rope], freqs_cis=self.freqs_cis, repeat_freqs_k=self.rope_k_repeat)
        dropout_p = self.dropout_p if self.training else 0.0
        try:
            with sdp_kernel_context(dropout_p):
                out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
        except Exception as e:
            warnings.warn(f'Flash Attention kernel failed due to: {e}\nFalling back to all available kernels for scaled_dot_product_attention (which may have a slower speed).', category=UserWarning, stacklevel=2)
            global ALLOW_ALL_KERNELS
            ALLOW_ALL_KERNELS = True
            out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
        out = self._recombine_heads(out)
        out = self.out_proj(out)
        return out

# File: segment-anything-2-coreml-conversion/sam2/modeling/sam2_base.py
import torch
import torch.distributed
import torch.nn.functional as F
from torch.nn.init import trunc_normal_
from sam2.modeling.sam.mask_decoder import MaskDecoder
from sam2.modeling.sam.prompt_encoder import PromptEncoder
from sam2.modeling.sam.transformer import TwoWayTransformer
from sam2.modeling.sam2_utils import get_1d_sine_pe, MLP, select_closest_cond_frames
NO_OBJ_SCORE = -1024.0

class SAM2Base(torch.nn.Module):

    def __init__(self, image_encoder, memory_attention, memory_encoder, num_maskmem=7, image_size=512, backbone_stride=16, sigmoid_scale_for_mem_enc=1.0, sigmoid_bias_for_mem_enc=0.0, binarize_mask_from_pts_for_mem_enc=False, use_mask_input_as_output_without_sam=False, max_cond_frames_in_attn=-1, directly_add_no_mem_embed=False, use_high_res_features_in_sam=False, multimask_output_in_sam=False, multimask_min_pt_num=1, multimask_max_pt_num=1, multimask_output_for_tracking=False, use_multimask_token_for_obj_ptr: bool=False, iou_prediction_use_sigmoid=False, memory_temporal_stride_for_eval=1, add_all_frames_to_correct_as_cond=False, non_overlap_masks_for_mem_enc=False, use_obj_ptrs_in_encoder=False, max_obj_ptrs_in_encoder=16, add_tpos_enc_to_obj_ptrs=True, proj_tpos_enc_in_obj_ptrs=False, only_obj_ptrs_in_the_past_for_eval=False, pred_obj_scores: bool=False, pred_obj_scores_mlp: bool=False, fixed_no_obj_ptr: bool=False, soft_no_obj_ptr: bool=False, use_mlp_for_obj_ptr_proj: bool=False, sam_mask_decoder_extra_args=None, compile_image_encoder: bool=False):
        super().__init__()
        self.image_encoder = image_encoder
        self.use_high_res_features_in_sam = use_high_res_features_in_sam
        self.num_feature_levels = 3 if use_high_res_features_in_sam else 1
        self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder
        self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder
        if use_obj_ptrs_in_encoder:
            self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4)
        self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs
        if proj_tpos_enc_in_obj_ptrs:
            assert add_tpos_enc_to_obj_ptrs
        self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs
        self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval
        self.memory_attention = memory_attention
        self.hidden_dim = memory_attention.d_model
        self.memory_encoder = memory_encoder
        self.mem_dim = self.hidden_dim
        if hasattr(self.memory_encoder, 'out_proj') and hasattr(self.memory_encoder.out_proj, 'weight'):
            self.mem_dim = self.memory_encoder.out_proj.weight.shape[0]
        self.num_maskmem = num_maskmem
        self.maskmem_tpos_enc = torch.nn.Parameter(torch.zeros(num_maskmem, 1, 1, self.mem_dim))
        trunc_normal_(self.maskmem_tpos_enc, std=0.02)
        self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
        self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
        trunc_normal_(self.no_mem_embed, std=0.02)
        trunc_normal_(self.no_mem_pos_enc, std=0.02)
        self.directly_add_no_mem_embed = directly_add_no_mem_embed
        self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc
        self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc
        self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc
        self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc
        self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval
        self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam
        self.multimask_output_in_sam = multimask_output_in_sam
        self.multimask_min_pt_num = multimask_min_pt_num
        self.multimask_max_pt_num = multimask_max_pt_num
        self.multimask_output_for_tracking = multimask_output_for_tracking
        self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr
        self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid
        self.image_size = image_size
        self.backbone_stride = backbone_stride
        self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args
        self.pred_obj_scores = pred_obj_scores
        self.pred_obj_scores_mlp = pred_obj_scores_mlp
        self.fixed_no_obj_ptr = fixed_no_obj_ptr
        self.soft_no_obj_ptr = soft_no_obj_ptr
        if self.fixed_no_obj_ptr:
            assert self.pred_obj_scores
            assert self.use_obj_ptrs_in_encoder
        if self.pred_obj_scores and self.use_obj_ptrs_in_encoder:
            self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim))
            trunc_normal_(self.no_obj_ptr, std=0.02)
        self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj
        self._build_sam_heads()
        self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond
        self.max_cond_frames_in_attn = max_cond_frames_in_attn
        if compile_image_encoder:
            print('Image encoder compilation is enabled. First forward pass will be slow.')
            self.image_encoder.forward = torch.compile(self.image_encoder.forward, mode='max-autotune', fullgraph=True, dynamic=False)

    @property
    def device(self):
        return next(self.parameters()).device

    def forward(self, *args, **kwargs):
        raise NotImplementedError('Please use the corresponding methods in SAM2VideoPredictor for inference.See notebooks/video_predictor_example.ipynb for an example.')

    def _build_sam_heads(self):
        self.sam_prompt_embed_dim = self.hidden_dim
        self.sam_image_embedding_size = self.image_size // self.backbone_stride
        self.sam_prompt_encoder = PromptEncoder(embed_dim=self.sam_prompt_embed_dim, image_embedding_size=(self.sam_image_embedding_size, self.sam_image_embedding_size), input_image_size=(self.image_size, self.image_size), mask_in_chans=16)
        self.sam_mask_decoder = MaskDecoder(num_multimask_outputs=3, transformer=TwoWayTransformer(depth=2, embedding_dim=self.sam_prompt_embed_dim, mlp_dim=2048, num_heads=8), transformer_dim=self.sam_prompt_embed_dim, iou_head_depth=3, iou_head_hidden_dim=256, use_high_res_features=self.use_high_res_features_in_sam, iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid, pred_obj_scores=self.pred_obj_scores, pred_obj_scores_mlp=self.pred_obj_scores_mlp, use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr, **self.sam_mask_decoder_extra_args or {})
        if self.use_obj_ptrs_in_encoder:
            self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim)
            if self.use_mlp_for_obj_ptr_proj:
                self.obj_ptr_proj = MLP(self.hidden_dim, self.hidden_dim, self.hidden_dim, 3)
        else:
            self.obj_ptr_proj = torch.nn.Identity()
        if self.proj_tpos_enc_in_obj_ptrs:
            self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim)
        else:
            self.obj_ptr_tpos_proj = torch.nn.Identity()

    def _forward_sam_heads(self, backbone_features, point_inputs=None, mask_inputs=None, high_res_features=None, multimask_output=False):
        B = backbone_features.size(0)
        device = backbone_features.device
        assert backbone_features.size(1) == self.sam_prompt_embed_dim
        assert backbone_features.size(2) == self.sam_image_embedding_size
        assert backbone_features.size(3) == self.sam_image_embedding_size
        if point_inputs is not None:
            sam_point_coords = point_inputs['point_coords']
            sam_point_labels = point_inputs['point_labels']
            assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B
        else:
            sam_point_coords = torch.zeros(B, 1, 2, device=device)
            sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device)
        if mask_inputs is not None:
            assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1)
            if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size:
                sam_mask_prompt = F.interpolate(mask_inputs.float(), size=self.sam_prompt_encoder.mask_input_size, align_corners=False, mode='bilinear', antialias=True)
            else:
                sam_mask_prompt = mask_inputs
        else:
            sam_mask_prompt = None
        (sparse_embeddings, dense_embeddings) = self.sam_prompt_encoder(points=(sam_point_coords, sam_point_labels), boxes=None, masks=sam_mask_prompt)
        (low_res_multimasks, ious, sam_output_tokens, object_score_logits) = self.sam_mask_decoder(image_embeddings=backbone_features, image_pe=self.sam_prompt_encoder.get_dense_pe(), sparse_prompt_embeddings=sparse_embeddings, dense_prompt_embeddings=dense_embeddings, multimask_output=multimask_output, repeat_image=False, high_res_features=high_res_features)
        if self.pred_obj_scores:
            is_obj_appearing = object_score_logits > 0
            low_res_multimasks = torch.where(is_obj_appearing[:, None, None], low_res_multimasks, NO_OBJ_SCORE)
        low_res_multimasks = low_res_multimasks.float()
        high_res_multimasks = F.interpolate(low_res_multimasks, size=(self.image_size, self.image_size), mode='bilinear', align_corners=False)
        sam_output_token = sam_output_tokens[:, 0]
        if multimask_output:
            best_iou_inds = torch.argmax(ious, dim=-1)
            batch_inds = torch.arange(B, device=device)
            low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)
            high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)
            if sam_output_tokens.size(1) > 1:
                sam_output_token = sam_output_tokens[batch_inds, best_iou_inds]
        else:
            (low_res_masks, high_res_masks) = (low_res_multimasks, high_res_multimasks)
        obj_ptr = self.obj_ptr_proj(sam_output_token)
        if self.pred_obj_scores:
            if self.soft_no_obj_ptr:
                assert not self.teacher_force_obj_scores_for_mem
                lambda_is_obj_appearing = object_score_logits.sigmoid()
            else:
                lambda_is_obj_appearing = is_obj_appearing.float()
            if self.fixed_no_obj_ptr:
                obj_ptr = lambda_is_obj_appearing * obj_ptr
            obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
        return (low_res_multimasks, high_res_multimasks, ious, low_res_masks, high_res_masks, obj_ptr, object_score_logits)

    def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs):
        (out_scale, out_bias) = (20.0, -10.0)
        mask_inputs_float = mask_inputs.float()
        high_res_masks = mask_inputs_float * out_scale + out_bias
        low_res_masks = F.interpolate(high_res_masks, size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4), align_corners=False, mode='bilinear', antialias=True)
        ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float()
        if not self.use_obj_ptrs_in_encoder:
            obj_ptr = torch.zeros(mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device)
        else:
            (_, _, _, _, _, obj_ptr, _) = self._forward_sam_heads(backbone_features=backbone_features, mask_inputs=self.mask_downsample(mask_inputs_float), high_res_features=high_res_features)
        is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1)
        is_obj_appearing = is_obj_appearing[..., None]
        lambda_is_obj_appearing = is_obj_appearing.float()
        object_score_logits = out_scale * lambda_is_obj_appearing + out_bias
        if self.pred_obj_scores:
            if self.fixed_no_obj_ptr:
                obj_ptr = lambda_is_obj_appearing * obj_ptr
            obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
        return (low_res_masks, high_res_masks, ious, low_res_masks, high_res_masks, obj_ptr, object_score_logits)

    def forward_image(self, img_batch: torch.Tensor):
        backbone_out = self.image_encoder(img_batch)
        if self.use_high_res_features_in_sam:
            backbone_out['backbone_fpn'][0] = self.sam_mask_decoder.conv_s0(backbone_out['backbone_fpn'][0])
            backbone_out['backbone_fpn'][1] = self.sam_mask_decoder.conv_s1(backbone_out['backbone_fpn'][1])
        return backbone_out

    def _prepare_backbone_features(self, backbone_out):
        backbone_out = backbone_out.copy()
        assert len(backbone_out['backbone_fpn']) == len(backbone_out['vision_pos_enc'])
        assert len(backbone_out['backbone_fpn']) >= self.num_feature_levels
        feature_maps = backbone_out['backbone_fpn'][-self.num_feature_levels:]
        vision_pos_embeds = backbone_out['vision_pos_enc'][-self.num_feature_levels:]
        feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds]
        vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps]
        vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds]
        return (backbone_out, vision_feats, vision_pos_embeds, feat_sizes)

    def _prepare_memory_conditioned_features(self, frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, feat_sizes, output_dict, num_frames, track_in_reverse=False):
        B = current_vision_feats[-1].size(1)
        C = self.hidden_dim
        (H, W) = feat_sizes[-1]
        device = current_vision_feats[-1].device
        if self.num_maskmem == 0:
            pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
            return pix_feat
        num_obj_ptr_tokens = 0
        if not is_init_cond_frame:
            (to_cat_memory, to_cat_memory_pos_embed) = ([], [])
            assert len(output_dict['cond_frame_outputs']) > 0
            cond_outputs = output_dict['cond_frame_outputs']
            (selected_cond_outputs, unselected_cond_outputs) = select_closest_cond_frames(frame_idx, cond_outputs, self.max_cond_frames_in_attn)
            t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()]
            r = self.memory_temporal_stride_for_eval
            for t_pos in range(1, self.num_maskmem):
                t_rel = self.num_maskmem - t_pos
                if t_rel == 1:
                    if not track_in_reverse:
                        prev_frame_idx = frame_idx - t_rel
                    else:
                        prev_frame_idx = frame_idx + t_rel
                elif not track_in_reverse:
                    prev_frame_idx = (frame_idx - 2) // r * r
                    prev_frame_idx = prev_frame_idx - (t_rel - 2) * r
                else:
                    prev_frame_idx = -(-(frame_idx + 2) // r) * r
                    prev_frame_idx = prev_frame_idx + (t_rel - 2) * r
                out = output_dict['non_cond_frame_outputs'].get(prev_frame_idx, None)
                if out is None:
                    out = unselected_cond_outputs.get(prev_frame_idx, None)
                t_pos_and_prevs.append((t_pos, out))
            for (t_pos, prev) in t_pos_and_prevs:
                if prev is None:
                    continue
                feats = prev['maskmem_features'].to(device, non_blocking=True)
                to_cat_memory.append(feats.flatten(2).permute(2, 0, 1))
                maskmem_enc = prev['maskmem_pos_enc'][-1].to(device)
                maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1)
                maskmem_enc = maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1]
                to_cat_memory_pos_embed.append(maskmem_enc)
            if self.use_obj_ptrs_in_encoder:
                max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder)
                if not self.training and self.only_obj_ptrs_in_the_past_for_eval:
                    ptr_cond_outputs = {t: out for (t, out) in selected_cond_outputs.items() if (t >= frame_idx if track_in_reverse else t <= frame_idx)}
                else:
                    ptr_cond_outputs = selected_cond_outputs
                pos_and_ptrs = [(abs(frame_idx - t), out['obj_ptr']) for (t, out) in ptr_cond_outputs.items()]
                for t_diff in range(1, max_obj_ptrs_in_encoder):
                    t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff
                    if t < 0 or (num_frames is not None and t >= num_frames):
                        break
                    out = output_dict['non_cond_frame_outputs'].get(t, unselected_cond_outputs.get(t, None))
                    if out is not None:
                        pos_and_ptrs.append((t_diff, out['obj_ptr']))
                if len(pos_and_ptrs) > 0:
                    (pos_list, ptrs_list) = zip(*pos_and_ptrs)
                    obj_ptrs = torch.stack(ptrs_list, dim=0)
                    if self.add_tpos_enc_to_obj_ptrs:
                        t_diff_max = max_obj_ptrs_in_encoder - 1
                        tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim
                        obj_pos = torch.tensor(pos_list, device=device)
                        obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim)
                        obj_pos = self.obj_ptr_tpos_proj(obj_pos)
                        obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim)
                    else:
                        obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim)
                    if self.mem_dim < C:
                        obj_ptrs = obj_ptrs.reshape(-1, B, C // self.mem_dim, self.mem_dim)
                        obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1)
                        obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0)
                    to_cat_memory.append(obj_ptrs)
                    to_cat_memory_pos_embed.append(obj_pos)
                    num_obj_ptr_tokens = obj_ptrs.shape[0]
                else:
                    num_obj_ptr_tokens = 0
        else:
            if self.directly_add_no_mem_embed:
                pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed
                pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)
                return pix_feat_with_mem
            to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)]
            to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)]
        memory = torch.cat(to_cat_memory, dim=0)
        memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0)
        pix_feat_with_mem = self.memory_attention(curr=current_vision_feats, curr_pos=current_vision_pos_embeds, memory=memory, memory_pos=memory_pos_embed, num_obj_ptr_tokens=num_obj_ptr_tokens)
        pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)
        return pix_feat_with_mem

    def _encode_new_memory(self, current_vision_feats, feat_sizes, pred_masks_high_res, is_mask_from_pts):
        B = current_vision_feats[-1].size(1)
        C = self.hidden_dim
        (H, W) = feat_sizes[-1]
        pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
        if self.non_overlap_masks_for_mem_enc and (not self.training):
            pred_masks_high_res = self._apply_non_overlapping_constraints(pred_masks_high_res)
        binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts
        if binarize and (not self.training):
            mask_for_mem = (pred_masks_high_res > 0).float()
        else:
            mask_for_mem = torch.sigmoid(pred_masks_high_res)
        if self.sigmoid_scale_for_mem_enc != 1.0:
            mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc
        if self.sigmoid_bias_for_mem_enc != 0.0:
            mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc
        maskmem_out = self.memory_encoder(pix_feat, mask_for_mem, skip_mask_sigmoid=True)
        maskmem_features = maskmem_out['vision_features']
        maskmem_pos_enc = maskmem_out['vision_pos_enc']
        return (maskmem_features, maskmem_pos_enc)

    def track_step(self, frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, feat_sizes, point_inputs, mask_inputs, output_dict, num_frames, track_in_reverse=False, run_mem_encoder=True, prev_sam_mask_logits=None):
        current_out = {'point_inputs': point_inputs, 'mask_inputs': mask_inputs}
        if len(current_vision_feats) > 1:
            high_res_features = [x.permute(1, 2, 0).view(x.size(1), x.size(2), *s) for (x, s) in zip(current_vision_feats[:-1], feat_sizes[:-1])]
        else:
            high_res_features = None
        if mask_inputs is not None and self.use_mask_input_as_output_without_sam:
            pix_feat = current_vision_feats[-1].permute(1, 2, 0)
            pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1])
            sam_outputs = self._use_mask_as_output(pix_feat, high_res_features, mask_inputs)
        else:
            pix_feat_with_mem = self._prepare_memory_conditioned_features(frame_idx=frame_idx, is_init_cond_frame=is_init_cond_frame, current_vision_feats=current_vision_feats[-1:], current_vision_pos_embeds=current_vision_pos_embeds[-1:], feat_sizes=feat_sizes[-1:], output_dict=output_dict, num_frames=num_frames, track_in_reverse=track_in_reverse)
            if prev_sam_mask_logits is not None:
                assert point_inputs is not None and mask_inputs is None
                mask_inputs = prev_sam_mask_logits
            multimask_output = self._use_multimask(is_init_cond_frame, point_inputs)
            sam_outputs = self._forward_sam_heads(backbone_features=pix_feat_with_mem, point_inputs=point_inputs, mask_inputs=mask_inputs, high_res_features=high_res_features, multimask_output=multimask_output)
        (_, _, _, low_res_masks, high_res_masks, obj_ptr, _) = sam_outputs
        current_out['pred_masks'] = low_res_masks
        current_out['pred_masks_high_res'] = high_res_masks
        current_out['obj_ptr'] = obj_ptr
        if run_mem_encoder and self.num_maskmem > 0:
            high_res_masks_for_mem_enc = high_res_masks
            (maskmem_features, maskmem_pos_enc) = self._encode_new_memory(current_vision_feats=current_vision_feats, feat_sizes=feat_sizes, pred_masks_high_res=high_res_masks_for_mem_enc, is_mask_from_pts=point_inputs is not None)
            current_out['maskmem_features'] = maskmem_features
            current_out['maskmem_pos_enc'] = maskmem_pos_enc
        else:
            current_out['maskmem_features'] = None
            current_out['maskmem_pos_enc'] = None
        return current_out

    def _use_multimask(self, is_init_cond_frame, point_inputs):
        num_pts = 0 if point_inputs is None else point_inputs['point_labels'].size(1)
        multimask_output = self.multimask_output_in_sam and (is_init_cond_frame or self.multimask_output_for_tracking) and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num)
        return multimask_output

    def _apply_non_overlapping_constraints(self, pred_masks):
        batch_size = pred_masks.size(0)
        if batch_size == 1:
            return pred_masks
        device = pred_masks.device
        max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True)
        batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None]
        keep = max_obj_inds == batch_obj_inds
        pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0))
        return pred_masks

# File: segment-anything-2-coreml-conversion/sam2/modeling/sam2_utils.py
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F

def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num):
    if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:
        selected_outputs = cond_frame_outputs
        unselected_outputs = {}
    else:
        assert max_cond_frame_num >= 2, 'we should allow using 2+ conditioning frames'
        selected_outputs = {}
        idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)
        if idx_before is not None:
            selected_outputs[idx_before] = cond_frame_outputs[idx_before]
        idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)
        if idx_after is not None:
            selected_outputs[idx_after] = cond_frame_outputs[idx_after]
        num_remain = max_cond_frame_num - len(selected_outputs)
        inds_remain = sorted((t for t in cond_frame_outputs if t not in selected_outputs), key=lambda x: abs(x - frame_idx))[:num_remain]
        selected_outputs.update(((t, cond_frame_outputs[t]) for t in inds_remain))
        unselected_outputs = {t: v for (t, v) in cond_frame_outputs.items() if t not in selected_outputs}
    return (selected_outputs, unselected_outputs)

def get_1d_sine_pe(pos_inds, dim, temperature=10000):
    pe_dim = dim // 2
    dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device)
    dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)
    pos_embed = pos_inds.unsqueeze(-1) / dim_t
    pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)
    return pos_embed

def get_activation_fn(activation):
    if activation == 'relu':
        return F.relu
    if activation == 'gelu':
        return F.gelu
    if activation == 'glu':
        return F.glu
    raise RuntimeError(f'activation should be relu/gelu, not {activation}.')

def get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])

class DropPath(nn.Module):

    def __init__(self, drop_prob=0.0, scale_by_keep=True):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob
        self.scale_by_keep = scale_by_keep

    def forward(self, x):
        if self.drop_prob == 0.0 or not self.training:
            return x
        keep_prob = 1 - self.drop_prob
        shape = (x.shape[0],) + (1,) * (x.ndim - 1)
        random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
        if keep_prob > 0.0 and self.scale_by_keep:
            random_tensor.div_(keep_prob)
        return x * random_tensor

class MLP(nn.Module):

    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int, activation: nn.Module=nn.ReLU, sigmoid_output: bool=False) -> None:
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList((nn.Linear(n, k) for (n, k) in zip([input_dim] + h, h + [output_dim])))
        self.sigmoid_output = sigmoid_output
        self.act = activation()

    def forward(self, x):
        for (i, layer) in enumerate(self.layers):
            x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)
        if self.sigmoid_output:
            x = F.sigmoid(x)
        return x

class LayerNorm2d(nn.Module):

    def __init__(self, num_channels: int, eps: float=1e-06) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(num_channels))
        self.bias = nn.Parameter(torch.zeros(num_channels))
        self.eps = eps

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        u = x.mean(1, keepdim=True)
        s = (x - u).pow(2).mean(1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.eps)
        x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x

# File: segment-anything-2-coreml-conversion/sam2/sam2_image_predictor.py
import os
import logging
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from PIL.Image import Image
from sam2.modeling.sam2_base import SAM2Base
from sam2.utils.transforms import SAM2Transforms

class SAM2ImagePredictor:

    def __init__(self, sam_model: SAM2Base, mask_threshold=0.0, max_hole_area=0.0, max_sprinkle_area=0.0, **kwargs) -> None:
        super().__init__()
        self.model = sam_model
        self._transforms = SAM2Transforms(resolution=self.model.image_size, mask_threshold=mask_threshold, max_hole_area=max_hole_area, max_sprinkle_area=max_sprinkle_area)
        self._is_image_set = False
        self._features = None
        self._orig_hw = None
        self._is_batch = False
        self.mask_threshold = mask_threshold
        self._bb_feat_sizes = [(256, 256), (128, 128), (64, 64)]

    @classmethod
    def from_pretrained(cls, model_id: str, **kwargs) -> 'SAM2ImagePredictor':
        from sam2.build_sam import build_sam2_hf
        sam_model = build_sam2_hf(model_id, **kwargs)
        return cls(sam_model, **kwargs)

    @torch.no_grad()
    def set_image(self, image: Union[np.ndarray, Image]) -> None:
        self.reset_predictor()
        if isinstance(image, np.ndarray):
            logging.info('For numpy array image, we assume (HxWxC) format')
            self._orig_hw = [image.shape[:2]]
        elif isinstance(image, Image):
            (w, h) = image.size
            self._orig_hw = [(h, w)]
        else:
            raise NotImplementedError('Image format not supported')
        input_image = self._transforms(image)
        input_image = input_image[None, ...].to(self.device)
        assert len(input_image.shape) == 4 and input_image.shape[1] == 3, f'input_image must be of size 1x3xHxW, got {input_image.shape}'
        logging.info('Computing image embeddings for the provided image...')
        backbone_out = self.model.forward_image(input_image)
        (_, vision_feats, _, _) = self.model._prepare_backbone_features(backbone_out)
        if self.model.directly_add_no_mem_embed:
            vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
        feats = [feat.permute(1, 2, 0).view(1, -1, *feat_size) for (feat, feat_size) in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])][::-1]
        self._features = {'image_embed': feats[-1], 'high_res_feats': feats[:-1]}
        self._is_image_set = True
        serialize_ground = os.environ.get('SERIALIZE_GROUND', False)
        if serialize_ground:
            image_embed = self._features['image_embed'].cpu().numpy()
            high_res_feats = self._features['high_res_feats']
            feats_s0 = high_res_feats[0].cpu().numpy()
            feats_s1 = high_res_feats[1].cpu().numpy()
            np.save('image_embed.npy', image_embed)
            np.save('feats_s0.npy', feats_s0)
            np.save('feats_s1.npy', feats_s1)
        logging.info('Image embeddings computed.')

    @torch.no_grad()
    def encode_image_raw(self, prepared_image: torch.Tensor):
        self.model.eval()
        with torch.no_grad():
            for (_, param) in self.model.named_parameters():
                if param.requires_grad:
                    param.requires_grad = False
            backbone_out = self.model.forward_image(prepared_image)
            (_, vision_feats, _, _) = self.model._prepare_backbone_features(backbone_out)
            if self.model.directly_add_no_mem_embed:
                vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
            feats = [feat.permute(1, 2, 0).view(1, -1, *feat_size) for (feat, feat_size) in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])][::-1]
            image_embed = feats[-1]
            high_res_feats = feats[:-1]
            assert len(high_res_feats) == 2
            (feats_s0, feats_s1) = (high_res_feats[0], high_res_feats[1])
            return (image_embed, feats_s0, feats_s1)

    @torch.no_grad()
    def encode_points_raw(self, unnorm_coords: torch.Tensor, labels: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        concat_points = (unnorm_coords, labels)
        with torch.no_grad():
            for (_, param) in self.model.named_parameters():
                if param.requires_grad:
                    param.requires_grad = False
            (sparse_embeddings, dense_embeddings) = self.model.sam_prompt_encoder.points_only(points=concat_points)
            return (sparse_embeddings, dense_embeddings)

    @torch.no_grad()
    def decode_masks_raw(self, image_embeddings: torch.Tensor, sparse_embedding: torch.Tensor, dense_embedding: torch.Tensor, high_res_features: List[torch.Tensor], multimask_output: bool=True, batched_mode: bool=False):
        with torch.no_grad():
            for (_, param) in self.model.sam_mask_decoder.named_parameters():
                if param.requires_grad:
                    param.requires_grad = False
            (low_res_masks, iou_scores, _, _) = self.model.sam_mask_decoder(image_embeddings=image_embeddings, image_pe=self.model.sam_prompt_encoder.get_dense_pe(), sparse_prompt_embeddings=sparse_embedding, dense_prompt_embeddings=dense_embedding, multimask_output=multimask_output, repeat_image=batched_mode, high_res_features=high_res_features)
            return (low_res_masks, iou_scores)

    @torch.no_grad()
    def set_image_batch(self, image_list: List[Union[np.ndarray]]) -> None:
        self.reset_predictor()
        assert isinstance(image_list, list)
        self._orig_hw = []
        for image in image_list:
            assert isinstance(image, np.ndarray), 'Images are expected to be an np.ndarray in RGB format, and of shape  HWC'
            self._orig_hw.append(image.shape[:2])
        img_batch = self._transforms.forward_batch(image_list)
        img_batch = img_batch.to(self.device)
        batch_size = img_batch.shape[0]
        assert len(img_batch.shape) == 4 and img_batch.shape[1] == 3, f'img_batch must be of size Bx3xHxW, got {img_batch.shape}'
        logging.info('Computing image embeddings for the provided images...')
        backbone_out = self.model.forward_image(img_batch)
        (_, vision_feats, _, _) = self.model._prepare_backbone_features(backbone_out)
        if self.model.directly_add_no_mem_embed:
            vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
        feats = [feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) for (feat, feat_size) in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])][::-1]
        self._features = {'image_embed': feats[-1], 'high_res_feats': feats[:-1]}
        self._is_image_set = True
        self._is_batch = True
        logging.info('Image embeddings computed.')

    def predict_batch(self, point_coords_batch: List[np.ndarray]=None, point_labels_batch: List[np.ndarray]=None, box_batch: List[np.ndarray]=None, mask_input_batch: List[np.ndarray]=None, multimask_output: bool=True, return_logits: bool=False, normalize_coords=True) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
        assert self._is_batch, 'This function should only be used when in batched mode'
        if not self._is_image_set:
            raise RuntimeError('An image must be set with .set_image_batch(...) before mask prediction.')
        num_images = len(self._features['image_embed'])
        all_masks = []
        all_ious = []
        all_low_res_masks = []
        for img_idx in range(num_images):
            point_coords = point_coords_batch[img_idx] if point_coords_batch is not None else None
            point_labels = point_labels_batch[img_idx] if point_labels_batch is not None else None
            box = box_batch[img_idx] if box_batch is not None else None
            mask_input = mask_input_batch[img_idx] if mask_input_batch is not None else None
            (mask_input, unnorm_coords, labels, unnorm_box) = self._prep_prompts(point_coords, point_labels, box, mask_input, normalize_coords, img_idx=img_idx)
            (masks, iou_predictions, low_res_masks) = self._predict(unnorm_coords, labels, unnorm_box, mask_input, multimask_output, return_logits=return_logits, img_idx=img_idx)
            masks_np = masks.squeeze(0).float().detach().cpu().numpy()
            iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy()
            low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()
            all_masks.append(masks_np)
            all_ious.append(iou_predictions_np)
            all_low_res_masks.append(low_res_masks_np)
        return (all_masks, all_ious, all_low_res_masks)

    def predict(self, point_coords: Optional[np.ndarray]=None, point_labels: Optional[np.ndarray]=None, box: Optional[np.ndarray]=None, mask_input: Optional[np.ndarray]=None, multimask_output: bool=True, return_logits: bool=False, normalize_coords=True) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        if not self._is_image_set:
            raise RuntimeError('An image must be set with .set_image(...) before mask prediction.')
        (mask_input, unnorm_coords, labels, unnorm_box) = self._prep_prompts(point_coords, point_labels, box, mask_input, normalize_coords)
        (masks, iou_predictions, low_res_masks) = self._predict(unnorm_coords, labels, unnorm_box, mask_input, multimask_output, return_logits=return_logits)
        masks_np = masks.squeeze(0).float().detach().cpu().numpy()
        iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy()
        low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()
        return (masks_np, iou_predictions_np, low_res_masks_np)

    def _prep_prompts(self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1):
        (unnorm_coords, labels, unnorm_box, mask_input) = (None, None, None, None)
        if point_coords is not None:
            assert point_labels is not None, 'point_labels must be supplied if point_coords is supplied.'
            point_coords = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
            unnorm_coords = self._transforms.transform_coords(point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx])
            labels = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
            if len(unnorm_coords.shape) == 2:
                (unnorm_coords, labels) = (unnorm_coords[None, ...], labels[None, ...])
        if box is not None:
            box = torch.as_tensor(box, dtype=torch.float, device=self.device)
            unnorm_box = self._transforms.transform_boxes(box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx])
        if mask_logits is not None:
            mask_input = torch.as_tensor(mask_logits, dtype=torch.float, device=self.device)
            if len(mask_input.shape) == 3:
                mask_input = mask_input[None, :, :, :]
        return (mask_input, unnorm_coords, labels, unnorm_box)

    @torch.no_grad()
    def _predict(self, point_coords: Optional[torch.Tensor], point_labels: Optional[torch.Tensor], boxes: Optional[torch.Tensor]=None, mask_input: Optional[torch.Tensor]=None, multimask_output: bool=True, return_logits: bool=False, img_idx: int=-1) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        if not self._is_image_set:
            raise RuntimeError('An image must be set with .set_image(...) before mask prediction.')
        if point_coords is not None:
            concat_points = (point_coords, point_labels)
        else:
            concat_points = None
        if boxes is not None:
            box_coords = boxes.reshape(-1, 2, 2)
            box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device)
            box_labels = box_labels.repeat(boxes.size(0), 1)
            if concat_points is not None:
                concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)
                concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)
                concat_points = (concat_coords, concat_labels)
            else:
                concat_points = (box_coords, box_labels)
        (sparse_embeddings, dense_embeddings) = self.model.sam_prompt_encoder(points=concat_points, boxes=None, masks=mask_input)
        batched_mode = concat_points is not None and concat_points[0].shape[0] > 1
        high_res_features = [feat_level[img_idx].unsqueeze(0) for feat_level in self._features['high_res_feats']]
        (low_res_masks, iou_predictions, _, _) = self.model.sam_mask_decoder(image_embeddings=self._features['image_embed'][img_idx].unsqueeze(0), image_pe=self.model.sam_prompt_encoder.get_dense_pe(), sparse_prompt_embeddings=sparse_embeddings, dense_prompt_embeddings=dense_embeddings, multimask_output=multimask_output, repeat_image=batched_mode, high_res_features=high_res_features)
        if os.environ.get('SERIALIZE_GROUND', False):
            low_res_masks_np = low_res_masks.cpu().numpy()
            np.save('low_res_masks.npy', low_res_masks_np)
        masks = self._transforms.postprocess_masks(low_res_masks, self._orig_hw[img_idx])
        low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0)
        if not return_logits:
            masks = masks > self.mask_threshold
        return (masks, iou_predictions, low_res_masks)

    def get_image_embedding(self) -> torch.Tensor:
        if not self._is_image_set:
            raise RuntimeError('An image must be set with .set_image(...) to generate an embedding.')
        assert self._features is not None, 'Features must exist if an image has been set.'
        return self._features['image_embed']

    @property
    def device(self) -> torch.device:
        return self.model.device

    def reset_predictor(self) -> None:
        self._is_image_set = False
        self._features = None
        self._orig_hw = None
        self._is_batch = False

# File: segment-anything-2-coreml-conversion/sam2/sam2_video_predictor.py
import warnings
from collections import OrderedDict
import torch
from tqdm import tqdm
from sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base
from sam2.utils.misc import concat_points, fill_holes_in_mask_scores, load_video_frames

class SAM2VideoPredictor(SAM2Base):

    def __init__(self, fill_hole_area=0, non_overlap_masks=False, clear_non_cond_mem_around_input=False, clear_non_cond_mem_for_multi_obj=False, **kwargs):
        super().__init__(**kwargs)
        self.fill_hole_area = fill_hole_area
        self.non_overlap_masks = non_overlap_masks
        self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input
        self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj

    @torch.inference_mode()
    def init_state(self, video_path, offload_video_to_cpu=False, offload_state_to_cpu=False, async_loading_frames=False):
        compute_device = self.device
        (images, video_height, video_width) = load_video_frames(video_path=video_path, image_size=self.image_size, offload_video_to_cpu=offload_video_to_cpu, async_loading_frames=async_loading_frames, compute_device=compute_device)
        inference_state = {}
        inference_state['images'] = images
        inference_state['num_frames'] = len(images)
        inference_state['offload_video_to_cpu'] = offload_video_to_cpu
        inference_state['offload_state_to_cpu'] = offload_state_to_cpu
        inference_state['video_height'] = video_height
        inference_state['video_width'] = video_width
        inference_state['device'] = compute_device
        if offload_state_to_cpu:
            inference_state['storage_device'] = torch.device('cpu')
        else:
            inference_state['storage_device'] = compute_device
        inference_state['point_inputs_per_obj'] = {}
        inference_state['mask_inputs_per_obj'] = {}
        inference_state['cached_features'] = {}
        inference_state['constants'] = {}
        inference_state['obj_id_to_idx'] = OrderedDict()
        inference_state['obj_idx_to_id'] = OrderedDict()
        inference_state['obj_ids'] = []
        inference_state['output_dict'] = {'cond_frame_outputs': {}, 'non_cond_frame_outputs': {}}
        inference_state['output_dict_per_obj'] = {}
        inference_state['temp_output_dict_per_obj'] = {}
        inference_state['consolidated_frame_inds'] = {'cond_frame_outputs': set(), 'non_cond_frame_outputs': set()}
        inference_state['tracking_has_started'] = False
        inference_state['frames_already_tracked'] = {}
        self._get_image_feature(inference_state, frame_idx=0, batch_size=1)
        return inference_state

    @classmethod
    def from_pretrained(cls, model_id: str, **kwargs) -> 'SAM2VideoPredictor':
        from sam2.build_sam import build_sam2_video_predictor_hf
        sam_model = build_sam2_video_predictor_hf(model_id, **kwargs)
        return sam_model

    def _obj_id_to_idx(self, inference_state, obj_id):
        obj_idx = inference_state['obj_id_to_idx'].get(obj_id, None)
        if obj_idx is not None:
            return obj_idx
        allow_new_object = not inference_state['tracking_has_started']
        if allow_new_object:
            obj_idx = len(inference_state['obj_id_to_idx'])
            inference_state['obj_id_to_idx'][obj_id] = obj_idx
            inference_state['obj_idx_to_id'][obj_idx] = obj_id
            inference_state['obj_ids'] = list(inference_state['obj_id_to_idx'])
            inference_state['point_inputs_per_obj'][obj_idx] = {}
            inference_state['mask_inputs_per_obj'][obj_idx] = {}
            inference_state['output_dict_per_obj'][obj_idx] = {'cond_frame_outputs': {}, 'non_cond_frame_outputs': {}}
            inference_state['temp_output_dict_per_obj'][obj_idx] = {'cond_frame_outputs': {}, 'non_cond_frame_outputs': {}}
            return obj_idx
        else:
            raise RuntimeError(f"Cannot add new object id {obj_id} after tracking starts. All existing object ids: {inference_state['obj_ids']}. Please call 'reset_state' to restart from scratch.")

    def _obj_idx_to_id(self, inference_state, obj_idx):
        return inference_state['obj_idx_to_id'][obj_idx]

    def _get_obj_num(self, inference_state):
        return len(inference_state['obj_idx_to_id'])

    @torch.inference_mode()
    def add_new_points_or_box(self, inference_state, frame_idx, obj_id, points=None, labels=None, clear_old_points=True, normalize_coords=True, box=None):
        obj_idx = self._obj_id_to_idx(inference_state, obj_id)
        point_inputs_per_frame = inference_state['point_inputs_per_obj'][obj_idx]
        mask_inputs_per_frame = inference_state['mask_inputs_per_obj'][obj_idx]
        if (points is not None) != (labels is not None):
            raise ValueError('points and labels must be provided together')
        if points is None and box is None:
            raise ValueError('at least one of points or box must be provided as input')
        if points is None:
            points = torch.zeros(0, 2, dtype=torch.float32)
        elif not isinstance(points, torch.Tensor):
            points = torch.tensor(points, dtype=torch.float32)
        if labels is None:
            labels = torch.zeros(0, dtype=torch.int32)
        elif not isinstance(labels, torch.Tensor):
            labels = torch.tensor(labels, dtype=torch.int32)
        if points.dim() == 2:
            points = points.unsqueeze(0)
        if labels.dim() == 1:
            labels = labels.unsqueeze(0)
        if box is not None:
            if not clear_old_points:
                raise ValueError('cannot add box without clearing old points, since box prompt must be provided before any point prompt (please use clear_old_points=True instead)')
            if inference_state['tracking_has_started']:
                warnings.warn("You are adding a box after tracking starts. SAM 2 may not always be able to incorporate a box prompt for *refinement*. If you intend to use box prompt as an *initial* input before tracking, please call 'reset_state' on the inference state to restart from scratch.", category=UserWarning, stacklevel=2)
            if not isinstance(box, torch.Tensor):
                box = torch.tensor(box, dtype=torch.float32, device=points.device)
            box_coords = box.reshape(1, 2, 2)
            box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device)
            box_labels = box_labels.reshape(1, 2)
            points = torch.cat([box_coords, points], dim=1)
            labels = torch.cat([box_labels, labels], dim=1)
        if normalize_coords:
            video_H = inference_state['video_height']
            video_W = inference_state['video_width']
            points = points / torch.tensor([video_W, video_H]).to(points.device)
        points = points * self.image_size
        points = points.to(inference_state['device'])
        labels = labels.to(inference_state['device'])
        if not clear_old_points:
            point_inputs = point_inputs_per_frame.get(frame_idx, None)
        else:
            point_inputs = None
        point_inputs = concat_points(point_inputs, points, labels)
        point_inputs_per_frame[frame_idx] = point_inputs
        mask_inputs_per_frame.pop(frame_idx, None)
        is_init_cond_frame = frame_idx not in inference_state['frames_already_tracked']
        if is_init_cond_frame:
            reverse = False
        else:
            reverse = inference_state['frames_already_tracked'][frame_idx]['reverse']
        obj_output_dict = inference_state['output_dict_per_obj'][obj_idx]
        obj_temp_output_dict = inference_state['temp_output_dict_per_obj'][obj_idx]
        is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond
        storage_key = 'cond_frame_outputs' if is_cond else 'non_cond_frame_outputs'
        prev_sam_mask_logits = None
        prev_out = obj_temp_output_dict[storage_key].get(frame_idx)
        if prev_out is None:
            prev_out = obj_output_dict['cond_frame_outputs'].get(frame_idx)
            if prev_out is None:
                prev_out = obj_output_dict['non_cond_frame_outputs'].get(frame_idx)
        if prev_out is not None and prev_out['pred_masks'] is not None:
            device = inference_state['device']
            prev_sam_mask_logits = prev_out['pred_masks'].to(device, non_blocking=True)
            prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0)
        (current_out, _) = self._run_single_frame_inference(inference_state=inference_state, output_dict=obj_output_dict, frame_idx=frame_idx, batch_size=1, is_init_cond_frame=is_init_cond_frame, point_inputs=point_inputs, mask_inputs=None, reverse=reverse, run_mem_encoder=False, prev_sam_mask_logits=prev_sam_mask_logits)
        obj_temp_output_dict[storage_key][frame_idx] = current_out
        obj_ids = inference_state['obj_ids']
        consolidated_out = self._consolidate_temp_output_across_obj(inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=False, consolidate_at_video_res=True)
        (_, video_res_masks) = self._get_orig_video_res_output(inference_state, consolidated_out['pred_masks_video_res'])
        return (frame_idx, obj_ids, video_res_masks)

    def add_new_points(self, *args, **kwargs):
        return self.add_new_points_or_box(*args, **kwargs)

    @torch.inference_mode()
    def add_new_mask(self, inference_state, frame_idx, obj_id, mask):
        obj_idx = self._obj_id_to_idx(inference_state, obj_id)
        point_inputs_per_frame = inference_state['point_inputs_per_obj'][obj_idx]
        mask_inputs_per_frame = inference_state['mask_inputs_per_obj'][obj_idx]
        if not isinstance(mask, torch.Tensor):
            mask = torch.tensor(mask, dtype=torch.bool)
        assert mask.dim() == 2
        (mask_H, mask_W) = mask.shape
        mask_inputs_orig = mask[None, None]
        mask_inputs_orig = mask_inputs_orig.float().to(inference_state['device'])
        if mask_H != self.image_size or mask_W != self.image_size:
            mask_inputs = torch.nn.functional.interpolate(mask_inputs_orig, size=(self.image_size, self.image_size), align_corners=False, mode='bilinear', antialias=True)
            mask_inputs = (mask_inputs >= 0.5).float()
        else:
            mask_inputs = mask_inputs_orig
        mask_inputs_per_frame[frame_idx] = mask_inputs
        point_inputs_per_frame.pop(frame_idx, None)
        is_init_cond_frame = frame_idx not in inference_state['frames_already_tracked']
        if is_init_cond_frame:
            reverse = False
        else:
            reverse = inference_state['frames_already_tracked'][frame_idx]['reverse']
        obj_output_dict = inference_state['output_dict_per_obj'][obj_idx]
        obj_temp_output_dict = inference_state['temp_output_dict_per_obj'][obj_idx]
        is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond
        storage_key = 'cond_frame_outputs' if is_cond else 'non_cond_frame_outputs'
        (current_out, _) = self._run_single_frame_inference(inference_state=inference_state, output_dict=obj_output_dict, frame_idx=frame_idx, batch_size=1, is_init_cond_frame=is_init_cond_frame, point_inputs=None, mask_inputs=mask_inputs, reverse=reverse, run_mem_encoder=False)
        obj_temp_output_dict[storage_key][frame_idx] = current_out
        obj_ids = inference_state['obj_ids']
        consolidated_out = self._consolidate_temp_output_across_obj(inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=False, consolidate_at_video_res=True)
        (_, video_res_masks) = self._get_orig_video_res_output(inference_state, consolidated_out['pred_masks_video_res'])
        return (frame_idx, obj_ids, video_res_masks)

    def _get_orig_video_res_output(self, inference_state, any_res_masks):
        device = inference_state['device']
        video_H = inference_state['video_height']
        video_W = inference_state['video_width']
        any_res_masks = any_res_masks.to(device, non_blocking=True)
        if any_res_masks.shape[-2:] == (video_H, video_W):
            video_res_masks = any_res_masks
        else:
            video_res_masks = torch.nn.functional.interpolate(any_res_masks, size=(video_H, video_W), mode='bilinear', align_corners=False)
        if self.non_overlap_masks:
            video_res_masks = self._apply_non_overlapping_constraints(video_res_masks)
        return (any_res_masks, video_res_masks)

    def _consolidate_temp_output_across_obj(self, inference_state, frame_idx, is_cond, run_mem_encoder, consolidate_at_video_res=False):
        batch_size = self._get_obj_num(inference_state)
        storage_key = 'cond_frame_outputs' if is_cond else 'non_cond_frame_outputs'
        if consolidate_at_video_res:
            assert not run_mem_encoder, 'memory encoder cannot run at video resolution'
            consolidated_H = inference_state['video_height']
            consolidated_W = inference_state['video_width']
            consolidated_mask_key = 'pred_masks_video_res'
        else:
            consolidated_H = consolidated_W = self.image_size // 4
            consolidated_mask_key = 'pred_masks'
        consolidated_out = {'maskmem_features': None, 'maskmem_pos_enc': None, consolidated_mask_key: torch.full(size=(batch_size, 1, consolidated_H, consolidated_W), fill_value=NO_OBJ_SCORE, dtype=torch.float32, device=inference_state['storage_device']), 'obj_ptr': torch.full(size=(batch_size, self.hidden_dim), fill_value=NO_OBJ_SCORE, dtype=torch.float32, device=inference_state['device'])}
        empty_mask_ptr = None
        for obj_idx in range(batch_size):
            obj_temp_output_dict = inference_state['temp_output_dict_per_obj'][obj_idx]
            obj_output_dict = inference_state['output_dict_per_obj'][obj_idx]
            out = obj_temp_output_dict[storage_key].get(frame_idx, None)
            if out is None:
                out = obj_output_dict['cond_frame_outputs'].get(frame_idx, None)
            if out is None:
                out = obj_output_dict['non_cond_frame_outputs'].get(frame_idx, None)
            if out is None:
                if run_mem_encoder:
                    if empty_mask_ptr is None:
                        empty_mask_ptr = self._get_empty_mask_ptr(inference_state, frame_idx)
                    consolidated_out['obj_ptr'][obj_idx:obj_idx + 1] = empty_mask_ptr
                continue
            obj_mask = out['pred_masks']
            consolidated_pred_masks = consolidated_out[consolidated_mask_key]
            if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]:
                consolidated_pred_masks[obj_idx:obj_idx + 1] = obj_mask
            else:
                resized_obj_mask = torch.nn.functional.interpolate(obj_mask, size=consolidated_pred_masks.shape[-2:], mode='bilinear', align_corners=False)
                consolidated_pred_masks[obj_idx:obj_idx + 1] = resized_obj_mask
            consolidated_out['obj_ptr'][obj_idx:obj_idx + 1] = out['obj_ptr']
        if run_mem_encoder:
            device = inference_state['device']
            high_res_masks = torch.nn.functional.interpolate(consolidated_out['pred_masks'].to(device, non_blocking=True), size=(self.image_size, self.image_size), mode='bilinear', align_corners=False)
            if self.non_overlap_masks_for_mem_enc:
                high_res_masks = self._apply_non_overlapping_constraints(high_res_masks)
            (maskmem_features, maskmem_pos_enc) = self._run_memory_encoder(inference_state=inference_state, frame_idx=frame_idx, batch_size=batch_size, high_res_masks=high_res_masks, is_mask_from_pts=True)
            consolidated_out['maskmem_features'] = maskmem_features
            consolidated_out['maskmem_pos_enc'] = maskmem_pos_enc
        return consolidated_out

    def _get_empty_mask_ptr(self, inference_state, frame_idx):
        batch_size = 1
        mask_inputs = torch.zeros((batch_size, 1, self.image_size, self.image_size), dtype=torch.float32, device=inference_state['device'])
        (_, _, current_vision_feats, current_vision_pos_embeds, feat_sizes) = self._get_image_feature(inference_state, frame_idx, batch_size)
        current_out = self.track_step(frame_idx=frame_idx, is_init_cond_frame=True, current_vision_feats=current_vision_feats, current_vision_pos_embeds=current_vision_pos_embeds, feat_sizes=feat_sizes, point_inputs=None, mask_inputs=mask_inputs, output_dict={}, num_frames=inference_state['num_frames'], track_in_reverse=False, run_mem_encoder=False, prev_sam_mask_logits=None)
        return current_out['obj_ptr']

    @torch.inference_mode()
    def propagate_in_video_preflight(self, inference_state):
        inference_state['tracking_has_started'] = True
        batch_size = self._get_obj_num(inference_state)
        temp_output_dict_per_obj = inference_state['temp_output_dict_per_obj']
        output_dict = inference_state['output_dict']
        consolidated_frame_inds = inference_state['consolidated_frame_inds']
        for is_cond in [False, True]:
            storage_key = 'cond_frame_outputs' if is_cond else 'non_cond_frame_outputs'
            temp_frame_inds = set()
            for obj_temp_output_dict in temp_output_dict_per_obj.values():
                temp_frame_inds.update(obj_temp_output_dict[storage_key].keys())
            consolidated_frame_inds[storage_key].update(temp_frame_inds)
            for frame_idx in temp_frame_inds:
                consolidated_out = self._consolidate_temp_output_across_obj(inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=True)
                output_dict[storage_key][frame_idx] = consolidated_out
                self._add_output_per_object(inference_state, frame_idx, consolidated_out, storage_key)
                clear_non_cond_mem = self.clear_non_cond_mem_around_input and (self.clear_non_cond_mem_for_multi_obj or batch_size <= 1)
                if clear_non_cond_mem:
                    self._clear_non_cond_mem_around_input(inference_state, frame_idx)
            for obj_temp_output_dict in temp_output_dict_per_obj.values():
                obj_temp_output_dict[storage_key].clear()
        for frame_idx in output_dict['cond_frame_outputs']:
            output_dict['non_cond_frame_outputs'].pop(frame_idx, None)
        for obj_output_dict in inference_state['output_dict_per_obj'].values():
            for frame_idx in obj_output_dict['cond_frame_outputs']:
                obj_output_dict['non_cond_frame_outputs'].pop(frame_idx, None)
        for frame_idx in consolidated_frame_inds['cond_frame_outputs']:
            assert frame_idx in output_dict['cond_frame_outputs']
            consolidated_frame_inds['non_cond_frame_outputs'].discard(frame_idx)
        all_consolidated_frame_inds = consolidated_frame_inds['cond_frame_outputs'] | consolidated_frame_inds['non_cond_frame_outputs']
        input_frames_inds = set()
        for point_inputs_per_frame in inference_state['point_inputs_per_obj'].values():
            input_frames_inds.update(point_inputs_per_frame.keys())
        for mask_inputs_per_frame in inference_state['mask_inputs_per_obj'].values():
            input_frames_inds.update(mask_inputs_per_frame.keys())
        assert all_consolidated_frame_inds == input_frames_inds

    @torch.inference_mode()
    def propagate_in_video(self, inference_state, start_frame_idx=None, max_frame_num_to_track=None, reverse=False):
        self.propagate_in_video_preflight(inference_state)
        output_dict = inference_state['output_dict']
        consolidated_frame_inds = inference_state['consolidated_frame_inds']
        obj_ids = inference_state['obj_ids']
        num_frames = inference_state['num_frames']
        batch_size = self._get_obj_num(inference_state)
        if len(output_dict['cond_frame_outputs']) == 0:
            raise RuntimeError('No points are provided; please add points first')
        clear_non_cond_mem = self.clear_non_cond_mem_around_input and (self.clear_non_cond_mem_for_multi_obj or batch_size <= 1)
        if start_frame_idx is None:
            start_frame_idx = min(output_dict['cond_frame_outputs'])
        if max_frame_num_to_track is None:
            max_frame_num_to_track = num_frames
        if reverse:
            end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0)
            if start_frame_idx > 0:
                processing_order = range(start_frame_idx, end_frame_idx - 1, -1)
            else:
                processing_order = []
        else:
            end_frame_idx = min(start_frame_idx + max_frame_num_to_track, num_frames - 1)
            processing_order = range(start_frame_idx, end_frame_idx + 1)
        for frame_idx in tqdm(processing_order, desc='propagate in video'):
            if frame_idx in consolidated_frame_inds['cond_frame_outputs']:
                storage_key = 'cond_frame_outputs'
                current_out = output_dict[storage_key][frame_idx]
                pred_masks = current_out['pred_masks']
                if clear_non_cond_mem:
                    self._clear_non_cond_mem_around_input(inference_state, frame_idx)
            elif frame_idx in consolidated_frame_inds['non_cond_frame_outputs']:
                storage_key = 'non_cond_frame_outputs'
                current_out = output_dict[storage_key][frame_idx]
                pred_masks = current_out['pred_masks']
            else:
                storage_key = 'non_cond_frame_outputs'
                (current_out, pred_masks) = self._run_single_frame_inference(inference_state=inference_state, output_dict=output_dict, frame_idx=frame_idx, batch_size=batch_size, is_init_cond_frame=False, point_inputs=None, mask_inputs=None, reverse=reverse, run_mem_encoder=True)
                output_dict[storage_key][frame_idx] = current_out
            self._add_output_per_object(inference_state, frame_idx, current_out, storage_key)
            inference_state['frames_already_tracked'][frame_idx] = {'reverse': reverse}
            (_, video_res_masks) = self._get_orig_video_res_output(inference_state, pred_masks)
            yield (frame_idx, obj_ids, video_res_masks)

    def _add_output_per_object(self, inference_state, frame_idx, current_out, storage_key):
        maskmem_features = current_out['maskmem_features']
        assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor)
        maskmem_pos_enc = current_out['maskmem_pos_enc']
        assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list)
        output_dict_per_obj = inference_state['output_dict_per_obj']
        for (obj_idx, obj_output_dict) in output_dict_per_obj.items():
            obj_slice = slice(obj_idx, obj_idx + 1)
            obj_out = {'maskmem_features': None, 'maskmem_pos_enc': None, 'pred_masks': current_out['pred_masks'][obj_slice], 'obj_ptr': current_out['obj_ptr'][obj_slice]}
            if maskmem_features is not None:
                obj_out['maskmem_features'] = maskmem_features[obj_slice]
            if maskmem_pos_enc is not None:
                obj_out['maskmem_pos_enc'] = [x[obj_slice] for x in maskmem_pos_enc]
            obj_output_dict[storage_key][frame_idx] = obj_out

    @torch.inference_mode()
    def reset_state(self, inference_state):
        self._reset_tracking_results(inference_state)
        inference_state['obj_id_to_idx'].clear()
        inference_state['obj_idx_to_id'].clear()
        inference_state['obj_ids'].clear()
        inference_state['point_inputs_per_obj'].clear()
        inference_state['mask_inputs_per_obj'].clear()
        inference_state['output_dict_per_obj'].clear()
        inference_state['temp_output_dict_per_obj'].clear()

    def _reset_tracking_results(self, inference_state):
        for v in inference_state['point_inputs_per_obj'].values():
            v.clear()
        for v in inference_state['mask_inputs_per_obj'].values():
            v.clear()
        for v in inference_state['output_dict_per_obj'].values():
            v['cond_frame_outputs'].clear()
            v['non_cond_frame_outputs'].clear()
        for v in inference_state['temp_output_dict_per_obj'].values():
            v['cond_frame_outputs'].clear()
            v['non_cond_frame_outputs'].clear()
        inference_state['output_dict']['cond_frame_outputs'].clear()
        inference_state['output_dict']['non_cond_frame_outputs'].clear()
        inference_state['consolidated_frame_inds']['cond_frame_outputs'].clear()
        inference_state['consolidated_frame_inds']['non_cond_frame_outputs'].clear()
        inference_state['tracking_has_started'] = False
        inference_state['frames_already_tracked'].clear()

    def _get_image_feature(self, inference_state, frame_idx, batch_size):
        (image, backbone_out) = inference_state['cached_features'].get(frame_idx, (None, None))
        if backbone_out is None:
            device = inference_state['device']
            image = inference_state['images'][frame_idx].to(device).float().unsqueeze(0)
            backbone_out = self.forward_image(image)
            inference_state['cached_features'] = {frame_idx: (image, backbone_out)}
        expanded_image = image.expand(batch_size, -1, -1, -1)
        expanded_backbone_out = {'backbone_fpn': backbone_out['backbone_fpn'].copy(), 'vision_pos_enc': backbone_out['vision_pos_enc'].copy()}
        for (i, feat) in enumerate(expanded_backbone_out['backbone_fpn']):
            expanded_backbone_out['backbone_fpn'][i] = feat.expand(batch_size, -1, -1, -1)
        for (i, pos) in enumerate(expanded_backbone_out['vision_pos_enc']):
            pos = pos.expand(batch_size, -1, -1, -1)
            expanded_backbone_out['vision_pos_enc'][i] = pos
        features = self._prepare_backbone_features(expanded_backbone_out)
        features = (expanded_image,) + features
        return features

    def _run_single_frame_inference(self, inference_state, output_dict, frame_idx, batch_size, is_init_cond_frame, point_inputs, mask_inputs, reverse, run_mem_encoder, prev_sam_mask_logits=None):
        (_, _, current_vision_feats, current_vision_pos_embeds, feat_sizes) = self._get_image_feature(inference_state, frame_idx, batch_size)
        assert point_inputs is None or mask_inputs is None
        current_out = self.track_step(frame_idx=frame_idx, is_init_cond_frame=is_init_cond_frame, current_vision_feats=current_vision_feats, current_vision_pos_embeds=current_vision_pos_embeds, feat_sizes=feat_sizes, point_inputs=point_inputs, mask_inputs=mask_inputs, output_dict=output_dict, num_frames=inference_state['num_frames'], track_in_reverse=reverse, run_mem_encoder=run_mem_encoder, prev_sam_mask_logits=prev_sam_mask_logits)
        storage_device = inference_state['storage_device']
        maskmem_features = current_out['maskmem_features']
        if maskmem_features is not None:
            maskmem_features = maskmem_features.to(torch.bfloat16)
            maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
        pred_masks_gpu = current_out['pred_masks']
        if self.fill_hole_area > 0:
            pred_masks_gpu = fill_holes_in_mask_scores(pred_masks_gpu, self.fill_hole_area)
        pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True)
        maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)
        obj_ptr = current_out['obj_ptr']
        compact_current_out = {'maskmem_features': maskmem_features, 'maskmem_pos_enc': maskmem_pos_enc, 'pred_masks': pred_masks, 'obj_ptr': obj_ptr}
        return (compact_current_out, pred_masks_gpu)

    def _run_memory_encoder(self, inference_state, frame_idx, batch_size, high_res_masks, is_mask_from_pts):
        (_, _, current_vision_feats, _, feat_sizes) = self._get_image_feature(inference_state, frame_idx, batch_size)
        (maskmem_features, maskmem_pos_enc) = self._encode_new_memory(current_vision_feats=current_vision_feats, feat_sizes=feat_sizes, pred_masks_high_res=high_res_masks, is_mask_from_pts=is_mask_from_pts)
        storage_device = inference_state['storage_device']
        maskmem_features = maskmem_features.to(torch.bfloat16)
        maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
        maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, {'maskmem_pos_enc': maskmem_pos_enc})
        return (maskmem_features, maskmem_pos_enc)

    def _get_maskmem_pos_enc(self, inference_state, current_out):
        model_constants = inference_state['constants']
        out_maskmem_pos_enc = current_out['maskmem_pos_enc']
        if out_maskmem_pos_enc is not None:
            if 'maskmem_pos_enc' not in model_constants:
                assert isinstance(out_maskmem_pos_enc, list)
                maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]
                model_constants['maskmem_pos_enc'] = maskmem_pos_enc
            else:
                maskmem_pos_enc = model_constants['maskmem_pos_enc']
            batch_size = out_maskmem_pos_enc[0].size(0)
            expanded_maskmem_pos_enc = [x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc]
        else:
            expanded_maskmem_pos_enc = None
        return expanded_maskmem_pos_enc

    def _clear_non_cond_mem_around_input(self, inference_state, frame_idx):
        r = self.memory_temporal_stride_for_eval
        frame_idx_begin = frame_idx - r * self.num_maskmem
        frame_idx_end = frame_idx + r * self.num_maskmem
        output_dict = inference_state['output_dict']
        non_cond_frame_outputs = output_dict['non_cond_frame_outputs']
        for t in range(frame_idx_begin, frame_idx_end + 1):
            non_cond_frame_outputs.pop(t, None)
            for obj_output_dict in inference_state['output_dict_per_obj'].values():
                obj_output_dict['non_cond_frame_outputs'].pop(t, None)

# File: segment-anything-2-coreml-conversion/sav_dataset/sav_evaluator.py
from argparse import ArgumentParser
from utils.sav_benchmark import benchmark
''
parser = ArgumentParser()
parser.add_argument('--gt_root', required=True, help="Path to the GT folder. For SA-V, it's sav_val/Annotations_6fps or sav_test/Annotations_6fps")
parser.add_argument('--pred_root', required=True, help='Path to a folder containing folders of masks to be evaluated, with exactly the same structure as gt_root')
parser.add_argument('-n', '--num_processes', default=16, type=int, help='Number of concurrent processes')
parser.add_argument('-s', '--strict', help='Make sure every video in the gt_root folder has a corresponding video in the prediction', action='store_true')
parser.add_argument('-q', '--quiet', help='Quietly run evaluation without printing the information out', action='store_true')
parser.add_argument('--do_not_skip_first_and_last_frame', help="In SA-V val and test, we skip the first and the last annotated frames in evaluation. Set this to true for evaluation on settings that doesn't skip first and last frames", action='store_true')
if __name__ == '__main__':
    args = parser.parse_args()
    benchmark([args.gt_root], [args.pred_root], args.strict, args.num_processes, verbose=not args.quiet, skip_first_and_last=not args.do_not_skip_first_and_last_frame)

# File: segment-anything-2-coreml-conversion/tools/vos_inference.py
import argparse
import os
import numpy as np
import torch
from PIL import Image
from sam2.build_sam import build_sam2_video_predictor
DAVIS_PALETTE = b'\x00\x00\x00\x80\x00\x00\x00\x80\x00\x80\x80\x00\x00\x00\x80\x80\x00\x80\x00\x80\x80\x80\x80\x80@\x00\x00\xc0\x00\x00@\x80\x00\xc0\x80\x00@\x00\x80\xc0\x00\x80@\x80\x80\xc0\x80\x80\x00@\x00\x80@\x00\x00\xc0\x00\x80\xc0\x00\x00@\x80\x80@\x80\x00\xc0\x80\x80\xc0\x80@@\x00\xc0@\x00@\xc0\x00\xc0\xc0\x00@@\x80\xc0@\x80@\xc0\x80\xc0\xc0\x80\x00\x00@\x80\x00@\x00\x80@\x80\x80@\x00\x00\xc0\x80\x00\xc0\x00\x80\xc0\x80\x80\xc0@\x00@\xc0\x00@@\x80@\xc0\x80@@\x00\xc0\xc0\x00\xc0@\x80\xc0\xc0\x80\xc0\x00@@\x80@@\x00\xc0@\x80\xc0@\x00@\xc0\x80@\xc0\x00\xc0\xc0\x80\xc0\xc0@@@\xc0@@@\xc0@\xc0\xc0@@@\xc0\xc0@\xc0@\xc0\xc0\xc0\xc0\xc0 \x00\x00\xa0\x00\x00 \x80\x00\xa0\x80\x00 \x00\x80\xa0\x00\x80 \x80\x80\xa0\x80\x80`\x00\x00\xe0\x00\x00`\x80\x00\xe0\x80\x00`\x00\x80\xe0\x00\x80`\x80\x80\xe0\x80\x80 @\x00\xa0@\x00 \xc0\x00\xa0\xc0\x00 @\x80\xa0@\x80 \xc0\x80\xa0\xc0\x80`@\x00\xe0@\x00`\xc0\x00\xe0\xc0\x00`@\x80\xe0@\x80`\xc0\x80\xe0\xc0\x80 \x00@\xa0\x00@ \x80@\xa0\x80@ \x00\xc0\xa0\x00\xc0 \x80\xc0\xa0\x80\xc0`\x00@\xe0\x00@`\x80@\xe0\x80@`\x00\xc0\xe0\x00\xc0`\x80\xc0\xe0\x80\xc0 @@\xa0@@ \xc0@\xa0\xc0@ @\xc0\xa0@\xc0 \xc0\xc0\xa0\xc0\xc0`@@\xe0@@`\xc0@\xe0\xc0@`@\xc0\xe0@\xc0`\xc0\xc0\xe0\xc0\xc0\x00 \x00\x80 \x00\x00\xa0\x00\x80\xa0\x00\x00 \x80\x80 \x80\x00\xa0\x80\x80\xa0\x80@ \x00\xc0 \x00@\xa0\x00\xc0\xa0\x00@ \x80\xc0 \x80@\xa0\x80\xc0\xa0\x80\x00`\x00\x80`\x00\x00\xe0\x00\x80\xe0\x00\x00`\x80\x80`\x80\x00\xe0\x80\x80\xe0\x80@`\x00\xc0`\x00@\xe0\x00\xc0\xe0\x00@`\x80\xc0`\x80@\xe0\x80\xc0\xe0\x80\x00 @\x80 @\x00\xa0@\x80\xa0@\x00 \xc0\x80 \xc0\x00\xa0\xc0\x80\xa0\xc0@ @\xc0 @@\xa0@\xc0\xa0@@ \xc0\xc0 \xc0@\xa0\xc0\xc0\xa0\xc0\x00`@\x80`@\x00\xe0@\x80\xe0@\x00`\xc0\x80`\xc0\x00\xe0\xc0\x80\xe0\xc0@`@\xc0`@@\xe0@\xc0\xe0@@`\xc0\xc0`\xc0@\xe0\xc0\xc0\xe0\xc0  \x00\xa0 \x00 \xa0\x00\xa0\xa0\x00  \x80\xa0 \x80 \xa0\x80\xa0\xa0\x80` \x00\xe0 \x00`\xa0\x00\xe0\xa0\x00` \x80\xe0 \x80`\xa0\x80\xe0\xa0\x80 `\x00\xa0`\x00 \xe0\x00\xa0\xe0\x00 `\x80\xa0`\x80 \xe0\x80\xa0\xe0\x80``\x00\xe0`\x00`\xe0\x00\xe0\xe0\x00``\x80\xe0`\x80`\xe0\x80\xe0\xe0\x80  @\xa0 @ \xa0@\xa0\xa0@  \xc0\xa0 \xc0 \xa0\xc0\xa0\xa0\xc0` @\xe0 @`\xa0@\xe0\xa0@` \xc0\xe0 \xc0`\xa0\xc0\xe0\xa0\xc0 `@\xa0`@ \xe0@\xa0\xe0@ `\xc0\xa0`\xc0 \xe0\xc0\xa0\xe0\xc0``@\xe0`@`\xe0@\xe0\xe0@``\xc0\xe0`\xc0`\xe0\xc0\xe0\xe0\xc0'

def load_ann_png(path):
    mask = Image.open(path)
    palette = mask.getpalette()
    mask = np.array(mask).astype(np.uint8)
    return (mask, palette)

def save_ann_png(path, mask, palette):
    assert mask.dtype == np.uint8
    assert mask.ndim == 2
    output_mask = Image.fromarray(mask)
    output_mask.putpalette(palette)
    output_mask.save(path)

def get_per_obj_mask(mask):
    object_ids = np.unique(mask)
    object_ids = object_ids[object_ids > 0].tolist()
    per_obj_mask = {object_id: mask == object_id for object_id in object_ids}
    return per_obj_mask

def put_per_obj_mask(per_obj_mask, height, width):
    mask = np.zeros((height, width), dtype=np.uint8)
    object_ids = sorted(per_obj_mask)[::-1]
    for object_id in object_ids:
        object_mask = per_obj_mask[object_id]
        object_mask = object_mask.reshape(height, width)
        mask[object_mask] = object_id
    return mask

def load_masks_from_dir(input_mask_dir, video_name, frame_name, per_obj_png_file):
    if not per_obj_png_file:
        input_mask_path = os.path.join(input_mask_dir, video_name, f'{frame_name}.png')
        (input_mask, input_palette) = load_ann_png(input_mask_path)
        per_obj_input_mask = get_per_obj_mask(input_mask)
    else:
        per_obj_input_mask = {}
        for object_name in os.listdir(os.path.join(input_mask_dir, video_name)):
            object_id = int(object_name)
            input_mask_path = os.path.join(input_mask_dir, video_name, object_name, f'{frame_name}.png')
            (input_mask, input_palette) = load_ann_png(input_mask_path)
            per_obj_input_mask[object_id] = input_mask > 0
    return (per_obj_input_mask, input_palette)

def save_masks_to_dir(output_mask_dir, video_name, frame_name, per_obj_output_mask, height, width, per_obj_png_file, output_palette):
    os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)
    if not per_obj_png_file:
        output_mask = put_per_obj_mask(per_obj_output_mask, height, width)
        output_mask_path = os.path.join(output_mask_dir, video_name, f'{frame_name}.png')
        save_ann_png(output_mask_path, output_mask, output_palette)
    else:
        for (object_id, object_mask) in per_obj_output_mask.items():
            object_name = f'{object_id:03d}'
            os.makedirs(os.path.join(output_mask_dir, video_name, object_name), exist_ok=True)
            output_mask = object_mask.reshape(height, width).astype(np.uint8)
            output_mask_path = os.path.join(output_mask_dir, video_name, object_name, f'{frame_name}.png')
            save_ann_png(output_mask_path, output_mask, output_palette)

@torch.inference_mode()
@torch.autocast(device_type='cuda', dtype=torch.bfloat16)
def vos_inference(predictor, base_video_dir, input_mask_dir, output_mask_dir, video_name, score_thresh=0.0, use_all_masks=False, per_obj_png_file=False):
    video_dir = os.path.join(base_video_dir, video_name)
    frame_names = [os.path.splitext(p)[0] for p in os.listdir(video_dir) if os.path.splitext(p)[-1] in ['.jpg', '.jpeg', '.JPG', '.JPEG']]
    frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
    inference_state = predictor.init_state(video_path=video_dir, async_loading_frames=False)
    height = inference_state['video_height']
    width = inference_state['video_width']
    input_palette = None
    if not use_all_masks:
        input_frame_inds = [0]
    else:
        if not per_obj_png_file:
            input_frame_inds = [idx for (idx, name) in enumerate(frame_names) if os.path.exists(os.path.join(input_mask_dir, video_name, f'{name}.png'))]
        else:
            input_frame_inds = [idx for object_name in os.listdir(os.path.join(input_mask_dir, video_name)) for (idx, name) in enumerate(frame_names) if os.path.exists(os.path.join(input_mask_dir, video_name, object_name, f'{name}.png'))]
        input_frame_inds = sorted(set(input_frame_inds))
    for input_frame_idx in input_frame_inds:
        (per_obj_input_mask, input_palette) = load_masks_from_dir(input_mask_dir=input_mask_dir, video_name=video_name, frame_name=frame_names[input_frame_idx], per_obj_png_file=per_obj_png_file)
        for (object_id, object_mask) in per_obj_input_mask.items():
            predictor.add_new_mask(inference_state=inference_state, frame_idx=input_frame_idx, obj_id=object_id, mask=object_mask)
    os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)
    output_palette = input_palette or DAVIS_PALETTE
    video_segments = {}
    for (out_frame_idx, out_obj_ids, out_mask_logits) in predictor.propagate_in_video(inference_state):
        per_obj_output_mask = {out_obj_id: (out_mask_logits[i] > score_thresh).cpu().numpy() for (i, out_obj_id) in enumerate(out_obj_ids)}
        video_segments[out_frame_idx] = per_obj_output_mask
    for (out_frame_idx, per_obj_output_mask) in video_segments.items():
        save_masks_to_dir(output_mask_dir=output_mask_dir, video_name=video_name, frame_name=frame_names[out_frame_idx], per_obj_output_mask=per_obj_output_mask, height=height, width=width, per_obj_png_file=per_obj_png_file, output_palette=output_palette)

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--sam2_cfg', type=str, default='sam2_hiera_b+.yaml', help='SAM 2 model configuration file')
    parser.add_argument('--sam2_checkpoint', type=str, default='./checkpoints/sam2_hiera_b+.pt', help='path to the SAM 2 model checkpoint')
    parser.add_argument('--base_video_dir', type=str, required=True, help='directory containing videos (as JPEG files) to run VOS prediction on')
    parser.add_argument('--input_mask_dir', type=str, required=True, help='directory containing input masks (as PNG files) of each video')
    parser.add_argument('--video_list_file', type=str, default=None, help='text file containing the list of video names to run VOS prediction on')
    parser.add_argument('--output_mask_dir', type=str, required=True, help='directory to save the output masks (as PNG files)')
    parser.add_argument('--score_thresh', type=float, default=0.0, help='threshold for the output mask logits (default: 0.0)')
    parser.add_argument('--use_all_masks', action='store_true', help="whether to use all available PNG files in input_mask_dir (default without this flag: just the first PNG file as input to the SAM 2 model; usually we don't need this flag, since semi-supervised VOS evaluation usually takes input from the first frame only)")
    parser.add_argument('--per_obj_png_file', action='store_true', help='whether use separate per-object PNG files for input and output masks (default without this flag: all object masks are packed into a single PNG file on each frame following DAVIS format; note that the SA-V dataset stores each object mask as an individual PNG file and requires this flag)')
    parser.add_argument('--apply_postprocessing', action='store_true', help="whether to apply postprocessing (e.g. hole-filling) to the output masks (we don't apply such post-processing in the SAM 2 model evaluation)")
    args = parser.parse_args()
    hydra_overrides_extra = ['++model.non_overlap_masks=' + ('false' if args.per_obj_png_file else 'true')]
    predictor = build_sam2_video_predictor(config_file=args.sam2_cfg, ckpt_path=args.sam2_checkpoint, apply_postprocessing=args.apply_postprocessing, hydra_overrides_extra=hydra_overrides_extra)
    if args.use_all_masks:
        print('using all available masks in input_mask_dir as input to the SAM 2 model')
    else:
        print("using only the first frame's mask in input_mask_dir as input to the SAM 2 model")
    if args.video_list_file is not None:
        with open(args.video_list_file, 'r') as f:
            video_names = [v.strip() for v in f.readlines()]
    else:
        video_names = [p for p in os.listdir(args.base_video_dir) if os.path.isdir(os.path.join(args.base_video_dir, p))]
    print(f'running VOS prediction on {len(video_names)} videos:\n{video_names}')
    for (n_video, video_name) in enumerate(video_names):
        print(f'\n{n_video + 1}/{len(video_names)} - running on {video_name}')
        vos_inference(predictor=predictor, base_video_dir=args.base_video_dir, input_mask_dir=args.input_mask_dir, output_mask_dir=args.output_mask_dir, video_name=video_name, score_thresh=args.score_thresh, use_all_masks=args.use_all_masks, per_obj_png_file=args.per_obj_png_file)
    print(f'completed VOS prediction on {len(video_names)} videos -- output masks saved to {args.output_mask_dir}')
if __name__ == '__main__':
    main()