# 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

 -->

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 2020-present, AllenAI Authors, University of Illinois Urbana-Champaign,
# Intel Nervana Systems and 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.
"""
Binarizers take a (real value) matrix as input and produce a binary (values in {0,1}) mask of the same shape.
"""

import torch
from torch import autograd


class ThresholdBinarizer(autograd.Function):
    """
    Thresholdd binarizer.
    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j} > \tau`
    where `\tau` is a real value threshold.

    Implementation is inspired from:
        https://github.com/arunmallya/piggyback
        Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
        Arun Mallya, Dillon Davis, Svetlana Lazebnik
    """

    @staticmethod
    def forward(ctx, inputs: torch.tensor, threshold: float, sigmoid: bool):
        """
        Args:
            inputs (`torch.FloatTensor`)
                The input matrix from which the binarizer computes the binary mask.
            threshold (`float`)
                The threshold value (in R).
            sigmoid (`bool`)
                If set to ``True``, we apply the sigmoid function to the `inputs` matrix before comparing to `threshold`.
                In this case, `threshold` should be a value between 0 and 1.
        Returns:
            mask (`torch.FloatTensor`)
                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
                retained, 0 - the associated weight is pruned).
        """
        nb_elems = inputs.numel()
        nb_min = int(0.005 * nb_elems) + 1
        if sigmoid:
            mask = (torch.sigmoid(inputs) > threshold).type(inputs.type())
        else:
            mask = (inputs > threshold).type(inputs.type())
        if mask.sum() < nb_min:
            # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
            k_threshold = inputs.flatten().kthvalue(max(nb_elems - nb_min, 1)).values
            mask = (inputs > k_threshold).type(inputs.type())
        return mask

    @staticmethod
    def backward(ctx, gradOutput):
        return gradOutput, None, None


class TopKBinarizer(autograd.Function):
    """
    Top-k Binarizer.
    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j}`
    is among the k% highest values of S.

    Implementation is inspired from:
        https://github.com/allenai/hidden-networks
        What's hidden in a randomly weighted neural network?
        Vivek Ramanujan*, Mitchell Wortsman*, Aniruddha Kembhavi, Ali Farhadi, Mohammad Rastegari
    """

    @staticmethod
    def forward(ctx, inputs: torch.tensor, threshold: float):
        """
        Args:
            inputs (`torch.FloatTensor`)
                The input matrix from which the binarizer computes the binary mask.
            threshold (`float`)
                The percentage of weights to keep (the rest is pruned).
                `threshold` is a float between 0 and 1.
        Returns:
            mask (`torch.FloatTensor`)
                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
                retained, 0 - the associated weight is pruned).
        """
        # Get the subnetwork by sorting the inputs and using the top threshold %
        mask = inputs.clone()
        _, idx = inputs.flatten().sort(descending=True)
        j = int(threshold * inputs.numel())

        # flat_out and mask access the same memory.
        flat_out = mask.flatten()
        flat_out[idx[j:]] = 0
        flat_out[idx[:j]] = 1
        return mask

    @staticmethod
    def backward(ctx, gradOutput):
        return gradOutput, None


class MagnitudeBinarizer(object):
    """
    Magnitude Binarizer.
    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j}`
    is among the k% highest values of |S| (absolute value).

    Implementation is inspired from https://github.com/NervanaSystems/distiller/blob/2291fdcc2ea642a98d4e20629acb5a9e2e04b4e6/distiller/pruning/automated_gradual_pruner.py#L24
    """

    @staticmethod
    def apply(inputs: torch.tensor, threshold: float):
        """
        Args:
            inputs (`torch.FloatTensor`)
                The input matrix from which the binarizer computes the binary mask.
                This input marix is typically the weight matrix.
            threshold (`float`)
                The percentage of weights to keep (the rest is pruned).
                `threshold` is a float between 0 and 1.
        Returns:
            mask (`torch.FloatTensor`)
                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
                retained, 0 - the associated weight is pruned).
        """
        # Get the subnetwork by sorting the inputs and using the top threshold %
        mask = inputs.clone()
        _, idx = inputs.abs().flatten().sort(descending=True)
        j = int(threshold * inputs.numel())

        # flat_out and mask access the same memory.
        flat_out = mask.flatten()
        flat_out[idx[j:]] = 0
        flat_out[idx[:j]] = 1
        return mask

# coding=utf-8
# Copyright 2020-present, AllenAI Authors, University of Illinois Urbana-Champaign,
# Intel Nervana Systems and 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.
"""
Binarizers take a (real value) matrix as input and produce a binary (values in {0,1}) mask of the same shape.
"""

import torch
from torch import autograd


class ThresholdBinarizer(autograd.Function):
    """
    Thresholdd binarizer.
    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j} > \tau`
    where `\tau` is a real value threshold.

    Implementation is inspired from:
        https://github.com/arunmallya/piggyback
        Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
        Arun Mallya, Dillon Davis, Svetlana Lazebnik
    """

    @staticmethod
    def forward(ctx, inputs: torch.tensor, threshold: float, sigmoid: bool):
        """
        Args:
            inputs (`torch.FloatTensor`)
                The input matrix from which the binarizer computes the binary mask.
            threshold (`float`)
                The threshold value (in R).
            sigmoid (`bool`)
                If set to ``True``, we apply the sigmoid function to the `inputs` matrix before comparing to `threshold`.
                In this case, `threshold` should be a value between 0 and 1.
        Returns:
            mask (`torch.FloatTensor`)
                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
                retained, 0 - the associated weight is pruned).
        """
        nb_elems = inputs.numel()
        nb_min = int(0.005 * nb_elems) + 1
        if sigmoid:
            mask = (torch.sigmoid(inputs) > threshold).type(inputs.type())
        else:
            mask = (inputs > threshold).type(inputs.type())
        if mask.sum() < nb_min:
            # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
            k_threshold = inputs.flatten().kthvalue(max(nb_elems - nb_min, 1)).values
            mask = (inputs > k_threshold).type(inputs.type())
        return mask

    @staticmethod
    def backward(ctx, gradOutput):
        return gradOutput, None, None


class TopKBinarizer(autograd.Function):
    """
    Top-k Binarizer.
    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j}`
    is among the k% highest values of S.

    Implementation is inspired from:
        https://github.com/allenai/hidden-networks
        What's hidden in a randomly weighted neural network?
        Vivek Ramanujan*, Mitchell Wortsman*, Aniruddha Kembhavi, Ali Farhadi, Mohammad Rastegari
    """

    @staticmethod
    def forward(ctx, inputs: torch.tensor, threshold: float):
        """
        Args:
            inputs (`torch.FloatTensor`)
                The input matrix from which the binarizer computes the binary mask.
            threshold (`float`)
                The percentage of weights to keep (the rest is pruned).
                `threshold` is a float between 0 and 1.
        Returns:
            mask (`torch.FloatTensor`)
                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
                retained, 0 - the associated weight is pruned).
        """
        # Get the subnetwork by sorting the inputs and using the top threshold %
        mask = inputs.clone()
        _, idx = inputs.flatten().sort(descending=True)
        j = int(threshold * inputs.numel())

        # flat_out and mask access the same memory.
        flat_out = mask.flatten()
        flat_out[idx[j:]] = 0
        flat_out[idx[:j]] = 1
        return mask

    @staticmethod
    def backward(ctx, gradOutput):
        return gradOutput, None


class MagnitudeBinarizer(object):
    """
    Magnitude Binarizer.
    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j}`
    is among the k% highest values of |S| (absolute value).

    Implementation is inspired from https://github.com/NervanaSystems/distiller/blob/2291fdcc2ea642a98d4e20629acb5a9e2e04b4e6/distiller/pruning/automated_gradual_pruner.py#L24
    """

    @staticmethod
    def apply(inputs: torch.tensor, threshold: float):
        """
        Args:
            inputs (`torch.FloatTensor`)
                The input matrix from which the binarizer computes the binary mask.
                This input marix is typically the weight matrix.
            threshold (`float`)
                The percentage of weights to keep (the rest is pruned).
                `threshold` is a float between 0 and 1.
        Returns:
            mask (`torch.FloatTensor`)
                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
                retained, 0 - the associated weight is pruned).
        """
        # Get the subnetwork by sorting the inputs and using the top threshold %
        mask = inputs.clone()
        _, idx = inputs.abs().flatten().sort(descending=True)
        j = int(threshold * inputs.numel())

        # flat_out and mask access the same memory.
        flat_out = mask.flatten()
        flat_out[idx[j:]] = 0
        flat_out[idx[:j]] = 1
        return mask
