# Ultralytics YOLO 🚀, AGPL-3.0 license

# 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.

from functools import partial

import torch

from ultralytics.utils.downloads import attempt_download_asset
from .modules.decoders import MaskDecoder
from .modules.encoders import ImageEncoderViT, PromptEncoder
from .modules.sam import Sam
from .modules.tiny_encoder import TinyViT
from .modules.transformer import TwoWayTransformer


def build_sam_vit_h(checkpoint=None):
    """Build and return a Segment Anything Model (SAM) h-size model."""
    return _build_sam(
        encoder_embed_dim=1280,
        encoder_depth=32,
        encoder_num_heads=16,
        encoder_global_attn_indexes=[7, 15, 23, 31],
        checkpoint=checkpoint,
    )


def build_sam_vit_l(checkpoint=None):
    """Build and return a Segment Anything Model (SAM) l-size model."""
    return _build_sam(
        encoder_embed_dim=1024,
        encoder_depth=24,
        encoder_num_heads=16,
        encoder_global_attn_indexes=[5, 11, 17, 23],
        checkpoint=checkpoint,
    )


def build_sam_vit_b(checkpoint=None):
    """Build and return a Segment Anything Model (SAM) b-size model."""
    return _build_sam(
        encoder_embed_dim=768,
        encoder_depth=12,
        encoder_num_heads=12,
        encoder_global_attn_indexes=[2, 5, 8, 11],
        checkpoint=checkpoint,
    )


def build_mobile_sam(checkpoint=None):
    """Build and return Mobile Segment Anything Model (Mobile-SAM)."""
    return _build_sam(
        encoder_embed_dim=[64, 128, 160, 320],
        encoder_depth=[2, 2, 6, 2],
        encoder_num_heads=[2, 4, 5, 10],
        encoder_global_attn_indexes=None,
        mobile_sam=True,
        checkpoint=checkpoint,
    )


def _build_sam(
    encoder_embed_dim, encoder_depth, encoder_num_heads, encoder_global_attn_indexes, checkpoint=None, mobile_sam=False
):
    """Builds the selected SAM model architecture."""
    prompt_embed_dim = 256
    image_size = 1024
    vit_patch_size = 16
    image_embedding_size = image_size // vit_patch_size
    image_encoder = (
        TinyViT(
            img_size=1024,
            in_chans=3,
            num_classes=1000,
            embed_dims=encoder_embed_dim,
            depths=encoder_depth,
            num_heads=encoder_num_heads,
            window_sizes=[7, 7, 14, 7],
            mlp_ratio=4.0,
            drop_rate=0.0,
            drop_path_rate=0.0,
            use_checkpoint=False,
            mbconv_expand_ratio=4.0,
            local_conv_size=3,
            layer_lr_decay=0.8,
        )
        if mobile_sam
        else ImageEncoderViT(
            depth=encoder_depth,
            embed_dim=encoder_embed_dim,
            img_size=image_size,
            mlp_ratio=4,
            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
            num_heads=encoder_num_heads,
            patch_size=vit_patch_size,
            qkv_bias=True,
            use_rel_pos=True,
            global_attn_indexes=encoder_global_attn_indexes,
            window_size=14,
            out_chans=prompt_embed_dim,
        )
    )
    sam = Sam(
        image_encoder=image_encoder,
        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],
    )
    if checkpoint is not None:
        checkpoint = attempt_download_asset(checkpoint)
        with open(checkpoint, "rb") as f:
            state_dict = torch.load(f)
        sam.load_state_dict(state_dict)
    sam.eval()
    # sam.load_state_dict(torch.load(checkpoint), strict=True)
    # sam.eval()
    return sam


sam_model_map = {
    "sam_h.pt": build_sam_vit_h,
    "sam_l.pt": build_sam_vit_l,
    "sam_b.pt": build_sam_vit_b,
    "mobile_sam.pt": build_mobile_sam,
}


def build_sam(ckpt="sam_b.pt"):
    """Build a SAM model specified by ckpt."""
    model_builder = None
    ckpt = str(ckpt)  # to allow Path ckpt types
    for k in sam_model_map.keys():
        if ckpt.endswith(k):
            model_builder = sam_model_map.get(k)

    if not model_builder:
        raise FileNotFoundError(f"{ckpt} is not a supported SAM model. Available models are: \n {sam_model_map.keys()}")

    return model_builder(ckpt)