# 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 2021 NVIDIA and 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.
""" SegFormer model configuration"""

import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}


class SegformerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SegformerModel`]. It is used to instantiate an
    SegFormer model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the SegFormer
    [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        num_encoder_blocks (`int`, *optional*, defaults to 4):
            The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
        depths (`List[int]`, *optional*, defaults to [2, 2, 2, 2]):
            The number of layers in each encoder block.
        sr_ratios (`List[int]`, *optional*, defaults to [8, 4, 2, 1]):
            Sequence reduction ratios in each encoder block.
        hidden_sizes (`List[int]`, *optional*, defaults to [32, 64, 160, 256]):
            Dimension of each of the encoder blocks.
        patch_sizes (`List[int]`, *optional*, defaults to [7, 3, 3, 3]):
            Patch size before each encoder block.
        strides (`List[int]`, *optional*, defaults to [4, 2, 2, 2]):
            Stride before each encoder block.
        num_attention_heads (`List[int]`, *optional*, defaults to [1, 2, 5, 8]):
            Number of attention heads for each attention layer in each block of the Transformer encoder.
        mlp_ratios (`List[int]`, *optional*, defaults to [4, 4, 4, 4]):
            Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
            encoder blocks.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability before the classification head.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
            The epsilon used by the layer normalization layers.
        decoder_hidden_size (`int`, *optional*, defaults to 256):
            The dimension of the all-MLP decode head.
        semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
            The index that is ignored by the loss function of the semantic segmentation model.

    Example:

    ```python
    >>> from transformers import SegformerModel, SegformerConfig

    >>> # Initializing a SegFormer nvidia/segformer-b0-finetuned-ade-512-512 style configuration
    >>> configuration = SegformerConfig()

    >>> # Initializing a model from the nvidia/segformer-b0-finetuned-ade-512-512 style configuration
    >>> model = SegformerModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "segformer"

    def __init__(
        self,
        num_channels=3,
        num_encoder_blocks=4,
        depths=[2, 2, 2, 2],
        sr_ratios=[8, 4, 2, 1],
        hidden_sizes=[32, 64, 160, 256],
        patch_sizes=[7, 3, 3, 3],
        strides=[4, 2, 2, 2],
        num_attention_heads=[1, 2, 5, 8],
        mlp_ratios=[4, 4, 4, 4],
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        classifier_dropout_prob=0.1,
        initializer_range=0.02,
        drop_path_rate=0.1,
        layer_norm_eps=1e-6,
        decoder_hidden_size=256,
        semantic_loss_ignore_index=255,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
            warnings.warn(
                "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                " removed, as the behaviour will default to that of reshape_last_stage = True.",
                FutureWarning,
            )

        self.num_channels = num_channels
        self.num_encoder_blocks = num_encoder_blocks
        self.depths = depths
        self.sr_ratios = sr_ratios
        self.hidden_sizes = hidden_sizes
        self.patch_sizes = patch_sizes
        self.strides = strides
        self.mlp_ratios = mlp_ratios
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.classifier_dropout_prob = classifier_dropout_prob
        self.initializer_range = initializer_range
        self.drop_path_rate = drop_path_rate
        self.layer_norm_eps = layer_norm_eps
        self.decoder_hidden_size = decoder_hidden_size
        self.reshape_last_stage = kwargs.get("reshape_last_stage", True)
        self.semantic_loss_ignore_index = semantic_loss_ignore_index


class SegformerOnnxConfig(OnnxConfig):
    torch_onnx_minimum_version = version.parse("1.11")

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]
        )

    @property
    def atol_for_validation(self) -> float:
        return 1e-4

    @property
    def default_onnx_opset(self) -> int:
        return 12

# coding=utf-8
# Copyright 2021 NVIDIA and 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.
""" SegFormer model configuration"""

import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}


class SegformerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SegformerModel`]. It is used to instantiate an
    SegFormer model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the SegFormer
    [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        num_encoder_blocks (`int`, *optional*, defaults to 4):
            The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
        depths (`List[int]`, *optional*, defaults to [2, 2, 2, 2]):
            The number of layers in each encoder block.
        sr_ratios (`List[int]`, *optional*, defaults to [8, 4, 2, 1]):
            Sequence reduction ratios in each encoder block.
        hidden_sizes (`List[int]`, *optional*, defaults to [32, 64, 160, 256]):
            Dimension of each of the encoder blocks.
        patch_sizes (`List[int]`, *optional*, defaults to [7, 3, 3, 3]):
            Patch size before each encoder block.
        strides (`List[int]`, *optional*, defaults to [4, 2, 2, 2]):
            Stride before each encoder block.
        num_attention_heads (`List[int]`, *optional*, defaults to [1, 2, 5, 8]):
            Number of attention heads for each attention layer in each block of the Transformer encoder.
        mlp_ratios (`List[int]`, *optional*, defaults to [4, 4, 4, 4]):
            Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
            encoder blocks.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability before the classification head.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
            The epsilon used by the layer normalization layers.
        decoder_hidden_size (`int`, *optional*, defaults to 256):
            The dimension of the all-MLP decode head.
        semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
            The index that is ignored by the loss function of the semantic segmentation model.

    Example:

    ```python
    >>> from transformers import SegformerModel, SegformerConfig

    >>> # Initializing a SegFormer nvidia/segformer-b0-finetuned-ade-512-512 style configuration
    >>> configuration = SegformerConfig()

    >>> # Initializing a model from the nvidia/segformer-b0-finetuned-ade-512-512 style configuration
    >>> model = SegformerModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "segformer"

    def __init__(
        self,
        num_channels=3,
        num_encoder_blocks=4,
        depths=[2, 2, 2, 2],
        sr_ratios=[8, 4, 2, 1],
        hidden_sizes=[32, 64, 160, 256],
        patch_sizes=[7, 3, 3, 3],
        strides=[4, 2, 2, 2],
        num_attention_heads=[1, 2, 5, 8],
        mlp_ratios=[4, 4, 4, 4],
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        classifier_dropout_prob=0.1,
        initializer_range=0.02,
        drop_path_rate=0.1,
        layer_norm_eps=1e-6,
        decoder_hidden_size=256,
        semantic_loss_ignore_index=255,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
            warnings.warn(
                "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                " removed, as the behaviour will default to that of reshape_last_stage = True.",
                FutureWarning,
            )

        self.num_channels = num_channels
        self.num_encoder_blocks = num_encoder_blocks
        self.depths = depths
        self.sr_ratios = sr_ratios
        self.hidden_sizes = hidden_sizes
        self.patch_sizes = patch_sizes
        self.strides = strides
        self.mlp_ratios = mlp_ratios
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.classifier_dropout_prob = classifier_dropout_prob
        self.initializer_range = initializer_range
        self.drop_path_rate = drop_path_rate
        self.layer_norm_eps = layer_norm_eps
        self.decoder_hidden_size = decoder_hidden_size
        self.reshape_last_stage = kwargs.get("reshape_last_stage", True)
        self.semantic_loss_ignore_index = semantic_loss_ignore_index


class SegformerOnnxConfig(OnnxConfig):
    torch_onnx_minimum_version = version.parse("1.11")

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]
        )

    @property
    def atol_for_validation(self) -> float:
        return 1e-4

    @property
    def default_onnx_opset(self) -> int:
        return 12
