# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

add VITS model. # What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# 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 CvT checkpoints from the original repository.

URL: https://github.com/microsoft/CvT"""


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def embeddings(idx):
    """
    The function helps in renaming embedding layer weights.

    Args:
        idx: stage number in original model
    """
    embed = []
    embed.append(
        (
            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
            f"stage{idx}.patch_embed.proj.weight",
        )
    )
    embed.append(
        (
            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
            f"stage{idx}.patch_embed.proj.bias",
        )
    )
    embed.append(
        (
            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
            f"stage{idx}.patch_embed.norm.weight",
        )
    )
    embed.append(
        (
            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
            f"stage{idx}.patch_embed.norm.bias",
        )
    )
    return embed


def attention(idx, cnt):
    """
    The function helps in renaming attention block layers weights.

    Args:
        idx: stage number in original model
        cnt: count of blocks in each stage
    """
    attention_weights = []
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
            f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
            f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
            f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
            f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
            f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
            f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
            f"stage{idx}.blocks.{cnt}.attn.proj.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
            f"stage{idx}.blocks.{cnt}.attn.proj.bias",
        )
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias")
    )
    return attention_weights


def cls_token(idx):
    """
    Function helps in renaming cls_token weights
    """
    token = []
    token.append((f"cvt.encoder.stages.{idx}.cls_token", "stage2.cls_token"))
    return token


def final():
    """
    Function helps in renaming final classification layer
    """
    head = []
    head.append(("layernorm.weight", "norm.weight"))
    head.append(("layernorm.bias", "norm.bias"))
    head.append(("classifier.weight", "head.weight"))
    head.append(("classifier.bias", "head.bias"))
    return head


def convert_cvt_checkpoint(cvt_model, image_size, cvt_file_name, pytorch_dump_folder):
    """
    Fucntion to convert the microsoft cvt checkpoint to huggingface checkpoint
    """
    img_labels_file = "imagenet-1k-id2label.json"
    num_labels = 1000

    repo_id = "huggingface/label-files"
    num_labels = num_labels
    id2label = json.load(open(cached_download(hf_hub_url(repo_id, img_labels_file, repo_type="dataset")), "r"))
    id2label = {int(k): v for k, v in id2label.items()}

    id2label = id2label
    label2id = {v: k for k, v in id2label.items()}

    config = config = CvtConfig(num_labels=num_labels, id2label=id2label, label2id=label2id)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit("/", 1)[-1][4:6] == "13":
        config.depth = [1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit("/", 1)[-1][4:6] == "21":
        config.depth = [1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        config.depth = [2, 2, 20]
        config.num_heads = [3, 12, 16]
        config.embed_dim = [192, 768, 1024]

    model = CvtForImageClassification(config)
    image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
    image_processor.size["shortest_edge"] = image_size
    original_weights = torch.load(cvt_file_name, map_location=torch.device("cpu"))

    huggingface_weights = OrderedDict()
    list_of_state_dict = []

    for idx in range(len(config.depth)):
        if config.cls_token[idx]:
            list_of_state_dict = list_of_state_dict + cls_token(idx)
        list_of_state_dict = list_of_state_dict + embeddings(idx)
        for cnt in range(config.depth[idx]):
            list_of_state_dict = list_of_state_dict + attention(idx, cnt)

    list_of_state_dict = list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(gg)
    for i in range(len(list_of_state_dict)):
        huggingface_weights[list_of_state_dict[i][0]] = original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(huggingface_weights)
    model.save_pretrained(pytorch_dump_folder)
    image_processor.save_pretrained(pytorch_dump_folder)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--cvt_model",
        default="cvt-w24",
        type=str,
        help="Name of the cvt model you'd like to convert.",
    )
    parser.add_argument(
        "--image_size",
        default=384,
        type=int,
        help="Input Image Size",
    )
    parser.add_argument(
        "--cvt_file_name",
        default=r"cvtmodels\CvT-w24-384x384-IN-22k.pth",
        type=str,
        help="Input Image Size",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )

    args = parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

# 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 CvT checkpoints from the original repository.

URL: https://github.com/microsoft/CvT"""


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def embeddings(idx):
    """
    The function helps in renaming embedding layer weights.

    Args:
        idx: stage number in original model
    """
    embed = []
    embed.append(
        (
            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
            f"stage{idx}.patch_embed.proj.weight",
        )
    )
    embed.append(
        (
            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
            f"stage{idx}.patch_embed.proj.bias",
        )
    )
    embed.append(
        (
            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
            f"stage{idx}.patch_embed.norm.weight",
        )
    )
    embed.append(
        (
            f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
            f"stage{idx}.patch_embed.norm.bias",
        )
    )
    return embed


def attention(idx, cnt):
    """
    The function helps in renaming attention block layers weights.

    Args:
        idx: stage number in original model
        cnt: count of blocks in each stage
    """
    attention_weights = []
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
            f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
            f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
            f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
            f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
            f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
            f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
            f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
            f"stage{idx}.blocks.{cnt}.attn.proj.weight",
        )
    )
    attention_weights.append(
        (
            f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
            f"stage{idx}.blocks.{cnt}.attn.proj.bias",
        )
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight")
    )
    attention_weights.append(
        (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias")
    )
    return attention_weights


def cls_token(idx):
    """
    Function helps in renaming cls_token weights
    """
    token = []
    token.append((f"cvt.encoder.stages.{idx}.cls_token", "stage2.cls_token"))
    return token


def final():
    """
    Function helps in renaming final classification layer
    """
    head = []
    head.append(("layernorm.weight", "norm.weight"))
    head.append(("layernorm.bias", "norm.bias"))
    head.append(("classifier.weight", "head.weight"))
    head.append(("classifier.bias", "head.bias"))
    return head


def convert_cvt_checkpoint(cvt_model, image_size, cvt_file_name, pytorch_dump_folder):
    """
    Fucntion to convert the microsoft cvt checkpoint to huggingface checkpoint
    """
    img_labels_file = "imagenet-1k-id2label.json"
    num_labels = 1000

    repo_id = "huggingface/label-files"
    num_labels = num_labels
    id2label = json.load(open(cached_download(hf_hub_url(repo_id, img_labels_file, repo_type="dataset")), "r"))
    id2label = {int(k): v for k, v in id2label.items()}

    id2label = id2label
    label2id = {v: k for k, v in id2label.items()}

    config = config = CvtConfig(num_labels=num_labels, id2label=id2label, label2id=label2id)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit("/", 1)[-1][4:6] == "13":
        config.depth = [1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit("/", 1)[-1][4:6] == "21":
        config.depth = [1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        config.depth = [2, 2, 20]
        config.num_heads = [3, 12, 16]
        config.embed_dim = [192, 768, 1024]

    model = CvtForImageClassification(config)
    image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
    image_processor.size["shortest_edge"] = image_size
    original_weights = torch.load(cvt_file_name, map_location=torch.device("cpu"))

    huggingface_weights = OrderedDict()
    list_of_state_dict = []

    for idx in range(len(config.depth)):
        if config.cls_token[idx]:
            list_of_state_dict = list_of_state_dict + cls_token(idx)
        list_of_state_dict = list_of_state_dict + embeddings(idx)
        for cnt in range(config.depth[idx]):
            list_of_state_dict = list_of_state_dict + attention(idx, cnt)

    list_of_state_dict = list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(gg)
    for i in range(len(list_of_state_dict)):
        huggingface_weights[list_of_state_dict[i][0]] = original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(huggingface_weights)
    model.save_pretrained(pytorch_dump_folder)
    image_processor.save_pretrained(pytorch_dump_folder)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--cvt_model",
        default="cvt-w24",
        type=str,
        help="Name of the cvt model you'd like to convert.",
    )
    parser.add_argument(
        "--image_size",
        default=384,
        type=int,
        help="Input Image Size",
    )
    parser.add_argument(
        "--cvt_file_name",
        default=r"cvtmodels\CvT-w24-384x384-IN-22k.pth",
        type=str,
        help="Input Image Size",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )

    args = parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
