# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. 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.
"""
Convert SAM checkpoints from the original repository.

URL: https://github.com/facebookresearch/segment-anything.

Also supports converting the SlimSAM checkpoints from https://github.com/czg1225/SlimSAM/tree/master.
"""
import sys
sys.path.append("../")

import argparse
import re
import torch
from safetensors.torch import save_model
from huggingface_hub import hf_hub_download
from transformers import SamVisionConfig
from sam_hq_vit_huge.modeling_sam_hq import SamHQModel
from sam_hq_vit_huge.configuration_sam_hq import SamHQConfig


def get_config(model_name):
    if "sam_hq_vit_b" in model_name:
        vision_config = SamVisionConfig()
    elif "sam_hq_vit_l" in model_name:
        vision_config = SamVisionConfig(
            hidden_size=1024,
            num_hidden_layers=24,
            num_attention_heads=16,
            global_attn_indexes=[5, 11, 17, 23],
        )
    elif "sam_hq_vit_h" in model_name:
        vision_config = SamVisionConfig(
            hidden_size=1280,
            num_hidden_layers=32,
            num_attention_heads=16,
            global_attn_indexes=[7, 15, 23, 31],
        )

    config = SamHQConfig(
        vision_config=vision_config,
    )

    return config


KEYS_TO_MODIFY_MAPPING = {
    # Vision Encoder
    "image_encoder": "vision_encoder",
    "patch_embed.proj": "patch_embed.projection",
    "blocks.": "layers.",
    "neck.0": "neck.conv1",
    "neck.1": "neck.layer_norm1",
    "neck.2": "neck.conv2",
    "neck.3": "neck.layer_norm2",

    # Prompt Encoder
    "mask_downscaling.0": "mask_embed.conv1",
    "mask_downscaling.1": "mask_embed.layer_norm1",
    "mask_downscaling.3": "mask_embed.conv2",
    "mask_downscaling.4": "mask_embed.layer_norm2",
    "mask_downscaling.6": "mask_embed.conv3",
    "point_embeddings": "point_embed",
    "pe_layer.positional_encoding_gaussian_matrix": "shared_embedding.positional_embedding",

    # Mask Decoder
    "iou_prediction_head.layers.0": "iou_prediction_head.proj_in",
    "iou_prediction_head.layers.1": "iou_prediction_head.layers.0",
    "iou_prediction_head.layers.2": "iou_prediction_head.proj_out",
    "mask_decoder.output_upscaling.0": "mask_decoder.upscale_conv1",
    "mask_decoder.output_upscaling.1": "mask_decoder.upscale_layer_norm",
    "mask_decoder.output_upscaling.3": "mask_decoder.upscale_conv2",
    ".norm": ".layer_norm",

    # SAM HQ Extra (in Mask Decoder)
    "hf_mlp.layers.0": "hf_mlp.proj_in",
    "hf_mlp.layers.1": "hf_mlp.layers.0",
    "hf_mlp.layers.2": "hf_mlp.proj_out",  
}


def replace_keys(state_dict):
    model_state_dict = {}
    state_dict.pop("pixel_mean", None)
    state_dict.pop("pixel_std", None)

    output_hypernetworks_mlps_pattern = r".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

    for key, value in state_dict.items():
        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
            if key_to_modify in key:
                key = key.replace(key_to_modify, new_key)

            if re.match(output_hypernetworks_mlps_pattern, key):
                layer_nb = int(re.match(output_hypernetworks_mlps_pattern, key).group(2))
                if layer_nb == 0:
                    key = key.replace("layers.0", "proj_in")
                elif layer_nb == 1:
                    key = key.replace("layers.1", "layers.0")
                elif layer_nb == 2:
                    key = key.replace("layers.2", "proj_out")
                break

        model_state_dict[key] = value.cpu()

    model_state_dict["shared_image_embedding.positional_embedding"] = model_state_dict[
        "prompt_encoder.shared_embedding.positional_embedding"
    ].cpu().clone()

    return model_state_dict


def convert_sam_checkpoint(model_name, checkpoint_path, output_dir):
    config = get_config(model_name)

    state_dict = torch.load(checkpoint_path, map_location="cpu")
    state_dict = replace_keys(state_dict)

    hf_model = SamHQModel(config)
    hf_model.eval()

    hf_model.load_state_dict(state_dict)

    if output_dir is not None:
        save_model(hf_model, f"{output_dir}/{model_name}.safetensors", metadata={"format": "pt"})


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    choices = ["sam_hq_vit_b", "sam_hq_vit_l", "sam_hq_vit_h"]
    parser.add_argument(
        "--model_name",
        default="sam_hq_vit_h",
        choices=choices,
        type=str,
        help="Name of the original model to convert",
    )
    parser.add_argument(
        "--checkpoint_path",
        type=str,
        required=False,
        help="Path to the original checkpoint",
    )
    parser.add_argument("--output_dir", default=".", type=str, help="Path to the output PyTorch model.")

    args = parser.parse_args()

    if args.checkpoint_path is not None:
        checkpoint_path = args.checkpoint_path
    else:
        checkpoint_path = hf_hub_download("lkeab/hq-sam", f"{args.model_name}.pth")

    convert_sam_checkpoint(args.model_name, checkpoint_path, args.output_dir)