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

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

import torch
from functools import partial
from segment_anything.modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer
from RepViTSAM import repvit
from timm.models import create_model

def build_sam_repvit(checkpoint=None):
    prompt_embed_dim = 256
    image_size = 1024
    vit_patch_size = 16
    image_embedding_size = image_size // vit_patch_size
    repvit_sam = Sam(
            image_encoder=create_model('repvit'),
            prompt_encoder=PromptEncoder(
            embed_dim=prompt_embed_dim,
            image_embedding_size=(image_embedding_size, image_embedding_size),
            input_image_size=(image_size, image_size),
            mask_in_chans=16,
            ),
            mask_decoder=MaskDecoder(
                    num_multimask_outputs=3,
                    transformer=TwoWayTransformer(
                    depth=2,
                    embedding_dim=prompt_embed_dim,
                    mlp_dim=2048,
                    num_heads=8,
                ),
                transformer_dim=prompt_embed_dim,
                iou_head_depth=3,
                iou_head_hidden_dim=256,
            ),
            pixel_mean=[123.675, 116.28, 103.53],
            pixel_std=[58.395, 57.12, 57.375],
        )

    repvit_sam.eval()
    if checkpoint is not None:
        with open(checkpoint, "rb") as f:
            state_dict = torch.load(f)
        repvit_sam.load_state_dict(state_dict)
    return repvit_sam

from functools import partial

sam_model_registry = {
    "repvit": partial(build_sam_repvit),
}