# 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. 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 CLIPSeg checkpoints from the original repository. URL: https://github.com/timojl/clipseg."""

import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def get_clipseg_config(model_name):
    text_config = CLIPSegTextConfig()
    vision_config = CLIPSegVisionConfig(patch_size=16)

    use_complex_transposed_convolution = True if "refined" in model_name else False
    reduce_dim = 16 if "rd16" in model_name else 64

    config = CLIPSegConfig.from_text_vision_configs(
        text_config,
        vision_config,
        use_complex_transposed_convolution=use_complex_transposed_convolution,
        reduce_dim=reduce_dim,
    )
    return config


def rename_key(name):
    # update prefixes
    if "clip_model" in name:
        name = name.replace("clip_model", "clip")
    if "transformer" in name:
        if "visual" in name:
            name = name.replace("visual.transformer", "vision_model")
        else:
            name = name.replace("transformer", "text_model")
    if "resblocks" in name:
        name = name.replace("resblocks", "encoder.layers")
    if "ln_1" in name:
        name = name.replace("ln_1", "layer_norm1")
    if "ln_2" in name:
        name = name.replace("ln_2", "layer_norm2")
    if "c_fc" in name:
        name = name.replace("c_fc", "fc1")
    if "c_proj" in name:
        name = name.replace("c_proj", "fc2")
    if "attn" in name and "self" not in name:
        name = name.replace("attn", "self_attn")
    # text encoder
    if "token_embedding" in name:
        name = name.replace("token_embedding", "text_model.embeddings.token_embedding")
    if "positional_embedding" in name and "visual" not in name:
        name = name.replace("positional_embedding", "text_model.embeddings.position_embedding.weight")
    if "ln_final" in name:
        name = name.replace("ln_final", "text_model.final_layer_norm")
    # vision encoder
    if "visual.class_embedding" in name:
        name = name.replace("visual.class_embedding", "vision_model.embeddings.class_embedding")
    if "visual.conv1" in name:
        name = name.replace("visual.conv1", "vision_model.embeddings.patch_embedding")
    if "visual.positional_embedding" in name:
        name = name.replace("visual.positional_embedding", "vision_model.embeddings.position_embedding.weight")
    if "visual.ln_pre" in name:
        name = name.replace("visual.ln_pre", "vision_model.pre_layrnorm")
    if "visual.ln_post" in name:
        name = name.replace("visual.ln_post", "vision_model.post_layernorm")
    # projection layers
    if "visual.proj" in name:
        name = name.replace("visual.proj", "visual_projection.weight")
    if "text_projection" in name:
        name = name.replace("text_projection", "text_projection.weight")
    # decoder
    if "trans_conv" in name:
        name = name.replace("trans_conv", "transposed_convolution")
    if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
        name = "decoder." + name
    if "blocks" in name:
        name = name.replace("blocks", "decoder.layers")
    if "linear1" in name:
        name = name.replace("linear1", "mlp.fc1")
    if "linear2" in name:
        name = name.replace("linear2", "mlp.fc2")
    if "norm1" in name and "layer_" not in name:
        name = name.replace("norm1", "layer_norm1")
    if "norm2" in name and "layer_" not in name:
        name = name.replace("norm2", "layer_norm2")

    return name


def convert_state_dict(orig_state_dict, config):
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)

        if key.startswith("clip_model") and "attn.in_proj" in key:
            key_split = key.split(".")
            if "visual" in key:
                layer_num = int(key_split[4])
                dim = config.vision_config.hidden_size
                prefix = "vision_model"
            else:
                layer_num = int(key_split[3])
                dim = config.text_config.hidden_size
                prefix = "text_model"

            if "weight" in key:
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
                    dim : dim * 2, :
                ]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
            else:
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
        elif "self_attn" in key and "out_proj" not in key:
            key_split = key.split(".")
            layer_num = int(key_split[1])
            dim = config.reduce_dim
            if "weight" in key:
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[dim : dim * 2, :]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
            else:
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
        else:
            new_name = rename_key(key)
            if "visual_projection" in new_name or "text_projection" in new_name:
                val = val.T
            orig_state_dict[new_name] = val

    return orig_state_dict


# We will verify our results on an image of cute cats
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image


def convert_clipseg_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub):
    config = get_clipseg_config(model_name)
    model = CLIPSegForImageSegmentation(config)
    model.eval()

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

    # remove some keys
    for key in state_dict.copy().keys():
        if key.startswith("model"):
            state_dict.pop(key, None)

    # rename some keys
    state_dict = convert_state_dict(state_dict, config)
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)

    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
        raise ValueError("Missing keys that are not expected: {}".format(missing_keys))
    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
        raise ValueError(f"Unexpected keys: {unexpected_keys}")

    image_processor = ViTImageProcessor(size=352)
    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
    processor = CLIPSegProcessor(image_processor=image_processor, tokenizer=tokenizer)

    image = prepare_img()
    text = ["a glass", "something to fill", "wood", "a jar"]

    inputs = processor(text=text, images=[image] * len(text), padding="max_length", return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)

    # verify values
    expected_conditional = torch.tensor([0.1110, -0.1882, 0.1645])
    expected_pooled_output = torch.tensor([0.2692, -0.7197, -0.1328])
    if model_name == "clipseg-rd64-refined":
        expected_masks_slice = torch.tensor(
            [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]
        )
    elif model_name == "clipseg-rd64":
        expected_masks_slice = torch.tensor(
            [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]
        )
    elif model_name == "clipseg-rd16":
        expected_masks_slice = torch.tensor(
            [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]
        )
    else:
        raise ValueError(f"Model name {model_name} not supported.")

    assert torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3)
    assert torch.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-3)
    assert torch.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-3)
    print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        print(f"Saving model and processor to {pytorch_dump_folder_path}")
        model.save_pretrained(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 the hub")
        model.push_to_hub(f"CIDAS/{model_name}")
        processor.push_to_hub(f"CIDAS/{model_name}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="clipseg-rd64",
        type=str,
        choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
        help=(
            "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
            " reduce dimension)"
        ),
    )
    parser.add_argument(
        "--checkpoint_path",
        default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
        type=str,
        help=(
            "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
            " the decoder weights."
        ),
    )
    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_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

# coding=utf-8
# Copyright 2022 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 CLIPSeg checkpoints from the original repository. URL: https://github.com/timojl/clipseg."""

import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def get_clipseg_config(model_name):
    text_config = CLIPSegTextConfig()
    vision_config = CLIPSegVisionConfig(patch_size=16)

    use_complex_transposed_convolution = True if "refined" in model_name else False
    reduce_dim = 16 if "rd16" in model_name else 64

    config = CLIPSegConfig.from_text_vision_configs(
        text_config,
        vision_config,
        use_complex_transposed_convolution=use_complex_transposed_convolution,
        reduce_dim=reduce_dim,
    )
    return config


def rename_key(name):
    # update prefixes
    if "clip_model" in name:
        name = name.replace("clip_model", "clip")
    if "transformer" in name:
        if "visual" in name:
            name = name.replace("visual.transformer", "vision_model")
        else:
            name = name.replace("transformer", "text_model")
    if "resblocks" in name:
        name = name.replace("resblocks", "encoder.layers")
    if "ln_1" in name:
        name = name.replace("ln_1", "layer_norm1")
    if "ln_2" in name:
        name = name.replace("ln_2", "layer_norm2")
    if "c_fc" in name:
        name = name.replace("c_fc", "fc1")
    if "c_proj" in name:
        name = name.replace("c_proj", "fc2")
    if "attn" in name and "self" not in name:
        name = name.replace("attn", "self_attn")
    # text encoder
    if "token_embedding" in name:
        name = name.replace("token_embedding", "text_model.embeddings.token_embedding")
    if "positional_embedding" in name and "visual" not in name:
        name = name.replace("positional_embedding", "text_model.embeddings.position_embedding.weight")
    if "ln_final" in name:
        name = name.replace("ln_final", "text_model.final_layer_norm")
    # vision encoder
    if "visual.class_embedding" in name:
        name = name.replace("visual.class_embedding", "vision_model.embeddings.class_embedding")
    if "visual.conv1" in name:
        name = name.replace("visual.conv1", "vision_model.embeddings.patch_embedding")
    if "visual.positional_embedding" in name:
        name = name.replace("visual.positional_embedding", "vision_model.embeddings.position_embedding.weight")
    if "visual.ln_pre" in name:
        name = name.replace("visual.ln_pre", "vision_model.pre_layrnorm")
    if "visual.ln_post" in name:
        name = name.replace("visual.ln_post", "vision_model.post_layernorm")
    # projection layers
    if "visual.proj" in name:
        name = name.replace("visual.proj", "visual_projection.weight")
    if "text_projection" in name:
        name = name.replace("text_projection", "text_projection.weight")
    # decoder
    if "trans_conv" in name:
        name = name.replace("trans_conv", "transposed_convolution")
    if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
        name = "decoder." + name
    if "blocks" in name:
        name = name.replace("blocks", "decoder.layers")
    if "linear1" in name:
        name = name.replace("linear1", "mlp.fc1")
    if "linear2" in name:
        name = name.replace("linear2", "mlp.fc2")
    if "norm1" in name and "layer_" not in name:
        name = name.replace("norm1", "layer_norm1")
    if "norm2" in name and "layer_" not in name:
        name = name.replace("norm2", "layer_norm2")

    return name


def convert_state_dict(orig_state_dict, config):
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)

        if key.startswith("clip_model") and "attn.in_proj" in key:
            key_split = key.split(".")
            if "visual" in key:
                layer_num = int(key_split[4])
                dim = config.vision_config.hidden_size
                prefix = "vision_model"
            else:
                layer_num = int(key_split[3])
                dim = config.text_config.hidden_size
                prefix = "text_model"

            if "weight" in key:
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
                    dim : dim * 2, :
                ]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
            else:
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
        elif "self_attn" in key and "out_proj" not in key:
            key_split = key.split(".")
            layer_num = int(key_split[1])
            dim = config.reduce_dim
            if "weight" in key:
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[dim : dim * 2, :]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
            else:
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
        else:
            new_name = rename_key(key)
            if "visual_projection" in new_name or "text_projection" in new_name:
                val = val.T
            orig_state_dict[new_name] = val

    return orig_state_dict


# We will verify our results on an image of cute cats
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image


def convert_clipseg_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub):
    config = get_clipseg_config(model_name)
    model = CLIPSegForImageSegmentation(config)
    model.eval()

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

    # remove some keys
    for key in state_dict.copy().keys():
        if key.startswith("model"):
            state_dict.pop(key, None)

    # rename some keys
    state_dict = convert_state_dict(state_dict, config)
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)

    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
        raise ValueError("Missing keys that are not expected: {}".format(missing_keys))
    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
        raise ValueError(f"Unexpected keys: {unexpected_keys}")

    image_processor = ViTImageProcessor(size=352)
    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
    processor = CLIPSegProcessor(image_processor=image_processor, tokenizer=tokenizer)

    image = prepare_img()
    text = ["a glass", "something to fill", "wood", "a jar"]

    inputs = processor(text=text, images=[image] * len(text), padding="max_length", return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)

    # verify values
    expected_conditional = torch.tensor([0.1110, -0.1882, 0.1645])
    expected_pooled_output = torch.tensor([0.2692, -0.7197, -0.1328])
    if model_name == "clipseg-rd64-refined":
        expected_masks_slice = torch.tensor(
            [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]
        )
    elif model_name == "clipseg-rd64":
        expected_masks_slice = torch.tensor(
            [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]
        )
    elif model_name == "clipseg-rd16":
        expected_masks_slice = torch.tensor(
            [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]
        )
    else:
        raise ValueError(f"Model name {model_name} not supported.")

    assert torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3)
    assert torch.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-3)
    assert torch.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-3)
    print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        print(f"Saving model and processor to {pytorch_dump_folder_path}")
        model.save_pretrained(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 the hub")
        model.push_to_hub(f"CIDAS/{model_name}")
        processor.push_to_hub(f"CIDAS/{model_name}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="clipseg-rd64",
        type=str,
        choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
        help=(
            "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
            " reduce dimension)"
        ),
    )
    parser.add_argument(
        "--checkpoint_path",
        default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
        type=str,
        help=(
            "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
            " the decoder weights."
        ),
    )
    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_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
