# Copyright (c) OpenMMLab. All rights reserved.

import argparse

import os.path as osp

from collections import OrderedDict

import mmcv

import torch

from mmcv.runner import CheckpointLoader

def convert_swin(ckpt):

    new_ckpt = OrderedDict()

    def correct_unfold_reduction_order(x):

        out_channel, in_channel = x.shape

        x = x.reshape(out_channel, 4, in_channel // 4)

        x = x[:, [0, 2, 1, 3], :].transpose(1,

                                            2).reshape(out_channel, in_channel)

        return x

    def correct_unfold_norm_order(x):

        in_channel = x.shape[0]

        x = x.reshape(4, in_channel // 4)

        x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel)

        return x

    for k, v in ckpt.items():

        if k.startswith('head'):

            continue

        elif k.startswith('layers'):

            new_v = v

            if 'attn.' in k:

                new_k = k.replace('attn.', 'attn.w_msa.')

            elif 'mlp.' in k:

                if 'mlp.fc1.' in k:

                    new_k = k.replace('mlp.fc1.', 'ffn.layers.0.0.')

                elif 'mlp.fc2.' in k:

                    new_k = k.replace('mlp.fc2.', 'ffn.layers.1.')

                else:

                    new_k = k.replace('mlp.', 'ffn.')

            elif 'downsample' in k:

                new_k = k

                if 'reduction.' in k:

                    new_v = correct_unfold_reduction_order(v)

                elif 'norm.' in k:

                    new_v = correct_unfold_norm_order(v)

            else:

                new_k = k

            new_k = new_k.replace('layers', 'stages', 1)

        elif k.startswith('patch_embed'):

            new_v = v

            if 'proj' in k:

                new_k = k.replace('proj', 'projection')

            else:

                new_k = k

        else:

            new_v = v

            new_k = k

        new_ckpt[new_k] = new_v

    return new_ckpt

def main():

    parser = argparse.ArgumentParser(

        description='Convert keys in official pretrained swin models to'

        'MMSegmentation style.')

    parser.add_argument('src', help='src model path or url')

    # The dst path must be a full path of the new checkpoint.

    parser.add_argument('dst', help='save path')

    args = parser.parse_args()

    checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')

    if 'state_dict' in checkpoint:

        state_dict = checkpoint['state_dict']

    elif 'model' in checkpoint:

        state_dict = checkpoint['model']

    else:

        state_dict = checkpoint

    weight = convert_swin(state_dict)

    mmcv.mkdir_or_exist(osp.dirname(args.dst))

    torch.save(weight, args.dst)

if __name__ == '__main__':

    main()