# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# This implementation refers to: https://github.com/facebookresearch/segment-anything
import paddle

from functools import partial

from paddleseg.utils import load_entire_model

from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer, TinyViT


def build_sam_vit_h(checkpoint=None):
    return _build_sam(
        encoder_embed_dim=1280,
        encoder_depth=32,
        encoder_num_heads=16,
        encoder_global_attn_indexes=[7, 15, 23, 31],
        checkpoint=checkpoint, )


build_sam = build_sam_vit_h


def build_sam_vit_l(checkpoint=None):
    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):
    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_sam_vit_t(checkpoint=None):
    prompt_embed_dim = 256
    image_size = 1024
    vit_patch_size = 16
    image_embedding_size = image_size // vit_patch_size
    mobile_sam = Sam(
            image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,
                embed_dims=[64, 128, 160, 320],
                depths=[2, 2, 6, 2],
                num_heads=[2, 4, 5, 10],
                window_sizes=[7, 7, 14, 7],
                mlp_ratio=4.,
                drop_rate=0.,
                drop_path_rate=0.0,
                mbconv_expand_ratio=4.0,
                local_conv_size=3,
                layer_lr_decay=0.8
            ),
            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],
        )

    mobile_sam.eval()
    if checkpoint is not None:
        load_entire_model(mobile_sam, checkpoint)
    mobile_sam.image_encoder.build_abs()
    return mobile_sam

sam_model_registry = {
    "default": build_sam,
    "vit_h": build_sam,
    "vit_l": build_sam_vit_l,
    "vit_b": build_sam_vit_b,
    "vit_t": build_sam_vit_t,
}

def _build_sam(
        encoder_embed_dim,
        encoder_depth,
        encoder_num_heads,
        encoder_global_attn_indexes,
        checkpoint=None, ):
    prompt_embed_dim = 256
    image_size = 1024
    vit_patch_size = 16
    image_embedding_size = image_size // vit_patch_size
    sam = Sam(
        image_encoder=ImageEncoderViT(
            depth=encoder_depth,
            embed_dim=encoder_embed_dim,
            img_size=image_size,
            mlp_ratio=4,
            norm_layer=partial(
                paddle.nn.LayerNorm, epsilon=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, ),
        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], )
    sam.eval()
    if checkpoint is not None:
        load_entire_model(sam, checkpoint)
    return sam
