# 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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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.
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      [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).

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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 The Microsoft Authors 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.
""" ProphetNet model configuration"""

from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}


class ProphetNetConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ProphetNetModel`]. It is used to instantiate a
    ProphetNet 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 ProphetNet
    [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) architecture.

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

    Args:
        activation_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for activations inside the fully connected layer.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`ProphetNetModel`].
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the layers and the pooler layer.
        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        num_encoder_layers (`int`, *optional*, defaults to 12):
            Number of encoder layers.
        num_encoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the `intermediate` (often named feed-forward) layer in decoder.
        num_decoder_layers (`int`, *optional*, defaults to 12):
            Number of decoder layers.
        num_decoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        attention_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        add_cross_attention (`bool`, *optional*, defaults to `True`):
            Whether cross-attention layers should be added to the model.
        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
            Whether this is an encoder/decoder model.
        pad_token_id (`int`, *optional*, defaults to 1)
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 0)
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2)
            End of stream token id.
        ngram (`int`, *optional*, defaults to 2)
            Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
            token.
        num_buckets (`int`, *optional*, defaults to 32)
            The number of buckets to use for each attention layer. This is for relative position calculation. See the
            [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
        relative_max_distance (`int`, *optional*, defaults to 128)
            Relative distances greater than this number will be put into the last same bucket. This is for relative
            position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
        disable_ngram_loss (`bool`, *optional*, defaults to `False`):
            Whether be trained predicting only the next first token.
        eps (`float`, *optional*, defaults to 0.0):
            Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label
            smoothing is performed.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
    """
    model_type = "prophetnet"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_attention_heads": "num_encoder_attention_heads",
    }

    def __init__(
        self,
        activation_dropout: Optional[float] = 0.1,
        activation_function: Optional[Union[str, Callable]] = "gelu",
        vocab_size: Optional[int] = 30522,
        hidden_size: Optional[int] = 1024,
        encoder_ffn_dim: Optional[int] = 4096,
        num_encoder_layers: Optional[int] = 12,
        num_encoder_attention_heads: Optional[int] = 16,
        decoder_ffn_dim: Optional[int] = 4096,
        num_decoder_layers: Optional[int] = 12,
        num_decoder_attention_heads: Optional[int] = 16,
        attention_dropout: Optional[float] = 0.1,
        dropout: Optional[float] = 0.1,
        max_position_embeddings: Optional[int] = 512,
        init_std: Optional[float] = 0.02,
        is_encoder_decoder: Optional[bool] = True,
        add_cross_attention: Optional[bool] = True,
        decoder_start_token_id: Optional[int] = 0,
        ngram: Optional[int] = 2,
        num_buckets: Optional[int] = 32,
        relative_max_distance: Optional[int] = 128,
        disable_ngram_loss: Optional[bool] = False,
        eps: Optional[float] = 0.0,
        use_cache: Optional[bool] = True,
        pad_token_id: Optional[int] = 0,
        bos_token_id: Optional[int] = 1,
        eos_token_id: Optional[int] = 2,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.encoder_ffn_dim = encoder_ffn_dim
        self.num_encoder_layers = num_encoder_layers
        self.num_encoder_attention_heads = num_encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.num_decoder_layers = num_decoder_layers
        self.num_decoder_attention_heads = num_decoder_attention_heads
        self.max_position_embeddings = max_position_embeddings
        self.init_std = init_std  # Normal(0, this parameter)
        self.activation_function = activation_function

        # parameters for prophetnet
        self.ngram = ngram
        self.num_buckets = num_buckets
        self.relative_max_distance = relative_max_distance
        self.disable_ngram_loss = disable_ngram_loss
        self.eps = eps

        # 3 Types of Dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.dropout = dropout

        self.use_cache = use_cache

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            add_cross_attention=add_cross_attention,
            decoder_start_token_id=decoder_start_token_id,
            **kwargs,
        )

    @property
    def num_hidden_layers(self) -> int:
        return self.num_encoder_layers + self.num_decoder_layers

    @num_hidden_layers.setter
    def num_hidden_layers(self, value):
        raise NotImplementedError(
            "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
            " `num_decoder_layers`."
        )

# coding=utf-8
# Copyright 2020 The Microsoft Authors 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.
""" ProphetNet model configuration"""

from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}


class ProphetNetConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ProphetNetModel`]. It is used to instantiate a
    ProphetNet 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 ProphetNet
    [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) architecture.

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

    Args:
        activation_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for activations inside the fully connected layer.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`ProphetNetModel`].
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the layers and the pooler layer.
        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        num_encoder_layers (`int`, *optional*, defaults to 12):
            Number of encoder layers.
        num_encoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the `intermediate` (often named feed-forward) layer in decoder.
        num_decoder_layers (`int`, *optional*, defaults to 12):
            Number of decoder layers.
        num_decoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        attention_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        add_cross_attention (`bool`, *optional*, defaults to `True`):
            Whether cross-attention layers should be added to the model.
        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
            Whether this is an encoder/decoder model.
        pad_token_id (`int`, *optional*, defaults to 1)
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 0)
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2)
            End of stream token id.
        ngram (`int`, *optional*, defaults to 2)
            Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
            token.
        num_buckets (`int`, *optional*, defaults to 32)
            The number of buckets to use for each attention layer. This is for relative position calculation. See the
            [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
        relative_max_distance (`int`, *optional*, defaults to 128)
            Relative distances greater than this number will be put into the last same bucket. This is for relative
            position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
        disable_ngram_loss (`bool`, *optional*, defaults to `False`):
            Whether be trained predicting only the next first token.
        eps (`float`, *optional*, defaults to 0.0):
            Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label
            smoothing is performed.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
    """
    model_type = "prophetnet"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_attention_heads": "num_encoder_attention_heads",
    }

    def __init__(
        self,
        activation_dropout: Optional[float] = 0.1,
        activation_function: Optional[Union[str, Callable]] = "gelu",
        vocab_size: Optional[int] = 30522,
        hidden_size: Optional[int] = 1024,
        encoder_ffn_dim: Optional[int] = 4096,
        num_encoder_layers: Optional[int] = 12,
        num_encoder_attention_heads: Optional[int] = 16,
        decoder_ffn_dim: Optional[int] = 4096,
        num_decoder_layers: Optional[int] = 12,
        num_decoder_attention_heads: Optional[int] = 16,
        attention_dropout: Optional[float] = 0.1,
        dropout: Optional[float] = 0.1,
        max_position_embeddings: Optional[int] = 512,
        init_std: Optional[float] = 0.02,
        is_encoder_decoder: Optional[bool] = True,
        add_cross_attention: Optional[bool] = True,
        decoder_start_token_id: Optional[int] = 0,
        ngram: Optional[int] = 2,
        num_buckets: Optional[int] = 32,
        relative_max_distance: Optional[int] = 128,
        disable_ngram_loss: Optional[bool] = False,
        eps: Optional[float] = 0.0,
        use_cache: Optional[bool] = True,
        pad_token_id: Optional[int] = 0,
        bos_token_id: Optional[int] = 1,
        eos_token_id: Optional[int] = 2,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.encoder_ffn_dim = encoder_ffn_dim
        self.num_encoder_layers = num_encoder_layers
        self.num_encoder_attention_heads = num_encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.num_decoder_layers = num_decoder_layers
        self.num_decoder_attention_heads = num_decoder_attention_heads
        self.max_position_embeddings = max_position_embeddings
        self.init_std = init_std  # Normal(0, this parameter)
        self.activation_function = activation_function

        # parameters for prophetnet
        self.ngram = ngram
        self.num_buckets = num_buckets
        self.relative_max_distance = relative_max_distance
        self.disable_ngram_loss = disable_ngram_loss
        self.eps = eps

        # 3 Types of Dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.dropout = dropout

        self.use_cache = use_cache

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            add_cross_attention=add_cross_attention,
            decoder_start_token_id=decoder_start_token_id,
            **kwargs,
        )

    @property
    def num_hidden_layers(self) -> int:
        return self.num_encoder_layers + self.num_decoder_layers

    @num_hidden_layers.setter
    def num_hidden_layers(self, value):
        raise NotImplementedError(
            "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
            " `num_decoder_layers`."
        )
