# What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
Migrate Trainer from `Repository` to `upload_folder`. # What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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 Swin Transformer + UperNet checkpoints from mmsegmentation.

URL: https://github.com/open-mmlab/mmsegmentation/tree/master/configs/swin
"""

import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def get_upernet_config(model_name):
    auxiliary_in_channels = 384
    window_size = 7
    if "tiny" in model_name:
        embed_dim = 96
        depths = (2, 2, 6, 2)
        num_heads = (3, 6, 12, 24)
    elif "small" in model_name:
        embed_dim = 96
        depths = (2, 2, 18, 2)
        num_heads = (3, 6, 12, 24)
    elif "base" in model_name:
        embed_dim = 128
        depths = (2, 2, 18, 2)
        num_heads = (4, 8, 16, 32)
        window_size = 12
        auxiliary_in_channels = 512
    elif "large" in model_name:
        embed_dim = 192
        depths = (2, 2, 18, 2)
        num_heads = (6, 12, 24, 48)
        window_size = 12
        auxiliary_in_channels = 768

    # set label information
    num_labels = 150
    repo_id = "huggingface/label-files"
    filename = "ade20k-id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    label2id = {v: k for k, v in id2label.items()}

    backbone_config = SwinConfig(
        embed_dim=embed_dim,
        depths=depths,
        num_heads=num_heads,
        window_size=window_size,
        out_features=["stage1", "stage2", "stage3", "stage4"],
    )
    config = UperNetConfig(
        backbone_config=backbone_config,
        auxiliary_in_channels=auxiliary_in_channels,
        num_labels=num_labels,
        id2label=id2label,
        label2id=label2id,
    )

    return config


# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config):
    rename_keys = []

    # fmt: off
    # stem
    rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
    rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
    rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight"))
    rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias"))
    # stages
    for i in range(len(config.backbone_config.depths)):
        for j in range(config.backbone_config.depths[i]):
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias"))

        if i < 3:
            rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight"))
            rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight"))
            rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias"))
        rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight"))
        rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias"))

    # decode head
    rename_keys.extend(
        [
            ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
            ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
            ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
            ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
        ]
    )
    # fmt: on

    return rename_keys


def rename_key(dct, old, new):
    val = dct.pop(old)
    dct[new] = val


# we split up the matrix of each encoder layer into queries, keys and values
def read_in_q_k_v(state_dict, backbone_config):
    num_features = [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
    for i in range(len(backbone_config.depths)):
        dim = num_features[i]
        for j in range(backbone_config.depths[i]):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            in_proj_weight = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight")
            in_proj_bias = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias")
            # next, add query, keys and values (in that order) to the state dict
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"] = in_proj_bias[: dim]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[
                dim : dim * 2, :
            ]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.bias"] = in_proj_bias[
                dim : dim * 2
            ]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[
                -dim :, :
            ]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"] = in_proj_bias[-dim :]
            # fmt: on


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 reverse_correct_unfold_reduction_order(x):
    out_channel, in_channel = x.shape
    x = x.reshape(out_channel, in_channel // 4, 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


# there was an incompatibility with this version, due to a new implementation of their downsampling operation using nn.Unfold.
# was resolved as seen here:
# https://github.com/open-mmlab/mmdetection/blob/31c84958f54287a8be2b99cbf87a6dcf12e57753/mmdet/models/utils/ckpt_convert.py#L96.
def reverse_correct_unfold_norm_order(x):
    in_channel = x.shape[0]
    x = x.reshape(in_channel // 4, 4)
    x = x[:, [0, 2, 1, 3]].transpose(0, 1).reshape(in_channel)
    return x


def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
    model_name_to_url = {
        "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
        "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
        "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
        "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
    }
    checkpoint_url = model_name_to_url[model_name]
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu", file_name=model_name)[
        "state_dict"
    ]

    for name, param in state_dict.items():
        print(name, param.shape)

    config = get_upernet_config(model_name)
    model = UperNetForSemanticSegmentation(config)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        if "bn" in key:
            key = key.replace("bn", "batch_norm")
        state_dict[key] = val

    # rename keys
    rename_keys = create_rename_keys(config)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config.backbone_config)

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                state_dict[key] = reverse_correct_unfold_reduction_order(value)
            if "norm" in key:
                state_dict[key] = reverse_correct_unfold_norm_order(value)

    model.load_state_dict(state_dict)

    # verify on image
    url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

    processor = SegformerImageProcessor()
    pixel_values = processor(image, return_tensors="pt").pixel_values

    with torch.no_grad():
        outputs = model(pixel_values)
        logits = outputs.logits

    print(logits.shape)
    print("First values of logits:", logits[0, 0, :3, :3])
    # assert values
    if model_name == "upernet-swin-tiny":
        expected_slice = torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]
        )
    elif model_name == "upernet-swin-small":
        expected_slice = torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]
        )
    elif model_name == "upernet-swin-base":
        expected_slice = torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]
        )
    elif model_name == "upernet-swin-large":
        expected_slice = torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]
        )
    print("Logits:", outputs.logits[0, 0, :3, :3])
    assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4)
    print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        print(f"Saving processor to {pytorch_dump_folder_path}")
        processor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        print(f"Pushing model and processor for {model_name} to hub")
        model.push_to_hub(f"openmmlab/{model_name}")
        processor.push_to_hub(f"openmmlab/{model_name}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="upernet-swin-tiny",
        type=str,
        choices=[f"upernet-swin-{size}" for size in ["tiny", "small", "base", "large"]],
        help="Name of the Swin + UperNet model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )

    args = parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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 Swin Transformer + UperNet checkpoints from mmsegmentation.

URL: https://github.com/open-mmlab/mmsegmentation/tree/master/configs/swin
"""

import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def get_upernet_config(model_name):
    auxiliary_in_channels = 384
    window_size = 7
    if "tiny" in model_name:
        embed_dim = 96
        depths = (2, 2, 6, 2)
        num_heads = (3, 6, 12, 24)
    elif "small" in model_name:
        embed_dim = 96
        depths = (2, 2, 18, 2)
        num_heads = (3, 6, 12, 24)
    elif "base" in model_name:
        embed_dim = 128
        depths = (2, 2, 18, 2)
        num_heads = (4, 8, 16, 32)
        window_size = 12
        auxiliary_in_channels = 512
    elif "large" in model_name:
        embed_dim = 192
        depths = (2, 2, 18, 2)
        num_heads = (6, 12, 24, 48)
        window_size = 12
        auxiliary_in_channels = 768

    # set label information
    num_labels = 150
    repo_id = "huggingface/label-files"
    filename = "ade20k-id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    label2id = {v: k for k, v in id2label.items()}

    backbone_config = SwinConfig(
        embed_dim=embed_dim,
        depths=depths,
        num_heads=num_heads,
        window_size=window_size,
        out_features=["stage1", "stage2", "stage3", "stage4"],
    )
    config = UperNetConfig(
        backbone_config=backbone_config,
        auxiliary_in_channels=auxiliary_in_channels,
        num_labels=num_labels,
        id2label=id2label,
        label2id=label2id,
    )

    return config


# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config):
    rename_keys = []

    # fmt: off
    # stem
    rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
    rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
    rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight"))
    rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias"))
    # stages
    for i in range(len(config.backbone_config.depths)):
        for j in range(config.backbone_config.depths[i]):
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias"))

        if i < 3:
            rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight"))
            rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight"))
            rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias"))
        rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight"))
        rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias"))

    # decode head
    rename_keys.extend(
        [
            ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
            ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
            ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
            ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
        ]
    )
    # fmt: on

    return rename_keys


def rename_key(dct, old, new):
    val = dct.pop(old)
    dct[new] = val


# we split up the matrix of each encoder layer into queries, keys and values
def read_in_q_k_v(state_dict, backbone_config):
    num_features = [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
    for i in range(len(backbone_config.depths)):
        dim = num_features[i]
        for j in range(backbone_config.depths[i]):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            in_proj_weight = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight")
            in_proj_bias = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias")
            # next, add query, keys and values (in that order) to the state dict
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"] = in_proj_bias[: dim]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[
                dim : dim * 2, :
            ]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.bias"] = in_proj_bias[
                dim : dim * 2
            ]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[
                -dim :, :
            ]
            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"] = in_proj_bias[-dim :]
            # fmt: on


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 reverse_correct_unfold_reduction_order(x):
    out_channel, in_channel = x.shape
    x = x.reshape(out_channel, in_channel // 4, 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


# there was an incompatibility with this version, due to a new implementation of their downsampling operation using nn.Unfold.
# was resolved as seen here:
# https://github.com/open-mmlab/mmdetection/blob/31c84958f54287a8be2b99cbf87a6dcf12e57753/mmdet/models/utils/ckpt_convert.py#L96.
def reverse_correct_unfold_norm_order(x):
    in_channel = x.shape[0]
    x = x.reshape(in_channel // 4, 4)
    x = x[:, [0, 2, 1, 3]].transpose(0, 1).reshape(in_channel)
    return x


def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
    model_name_to_url = {
        "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
        "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
        "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
        "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
    }
    checkpoint_url = model_name_to_url[model_name]
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu", file_name=model_name)[
        "state_dict"
    ]

    for name, param in state_dict.items():
        print(name, param.shape)

    config = get_upernet_config(model_name)
    model = UperNetForSemanticSegmentation(config)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        if "bn" in key:
            key = key.replace("bn", "batch_norm")
        state_dict[key] = val

    # rename keys
    rename_keys = create_rename_keys(config)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config.backbone_config)

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                state_dict[key] = reverse_correct_unfold_reduction_order(value)
            if "norm" in key:
                state_dict[key] = reverse_correct_unfold_norm_order(value)

    model.load_state_dict(state_dict)

    # verify on image
    url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

    processor = SegformerImageProcessor()
    pixel_values = processor(image, return_tensors="pt").pixel_values

    with torch.no_grad():
        outputs = model(pixel_values)
        logits = outputs.logits

    print(logits.shape)
    print("First values of logits:", logits[0, 0, :3, :3])
    # assert values
    if model_name == "upernet-swin-tiny":
        expected_slice = torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]
        )
    elif model_name == "upernet-swin-small":
        expected_slice = torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]
        )
    elif model_name == "upernet-swin-base":
        expected_slice = torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]
        )
    elif model_name == "upernet-swin-large":
        expected_slice = torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]
        )
    print("Logits:", outputs.logits[0, 0, :3, :3])
    assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4)
    print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        print(f"Saving processor to {pytorch_dump_folder_path}")
        processor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        print(f"Pushing model and processor for {model_name} to hub")
        model.push_to_hub(f"openmmlab/{model_name}")
        processor.push_to_hub(f"openmmlab/{model_name}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="upernet-swin-tiny",
        type=str,
        choices=[f"upernet-swin-{size}" for size in ["tiny", "small", "base", "large"]],
        help="Name of the Swin + UperNet model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )

    args = parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
