This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
Add bark. This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",
    ROBERTA_START_DOCSTRING,
)
class DeeRobertaModel(DeeBertModel):
    config_class = RobertaConfig
    base_model_prefix = "roberta"

    def __init__(self, config):
        super().__init__(config)

        self.embeddings = RobertaEmbeddings(config)
        self.init_weights()


@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """,
    ROBERTA_START_DOCSTRING,
)
class DeeRobertaForSequenceClassification(BertPreTrainedModel):
    config_class = RobertaConfig
    base_model_prefix = "roberta"

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.num_layers = config.num_hidden_layers

        self.roberta = DeeRobertaModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)

    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
        output_layer=-1,
        train_highway=False,
    ):
        r"""
            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                Labels for computing the sequence classification/regression loss.
                Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
                If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
                If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:
            :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
            loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
                Classification (or regression if config.num_labels==1) loss.
            logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
                Classification (or regression if config.num_labels==1) scores (before SoftMax).
            hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
                Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
                of shape :obj:`(batch_size, sequence_length, hidden_size)`.

                Hidden-states of the model at the output of each layer plus the initial embedding outputs.
            attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
                Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
                :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

                Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
                heads.
            highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
                Tuple of each early exit's results (total length: number of layers)
                Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
        """

        exit_layer = self.num_layers
        try:
            outputs = self.roberta(
                input_ids,
                attention_mask=attention_mask,
                token_type_ids=token_type_ids,
                position_ids=position_ids,
                head_mask=head_mask,
                inputs_embeds=inputs_embeds,
            )

            pooled_output = outputs[1]

            pooled_output = self.dropout(pooled_output)
            logits = self.classifier(pooled_output)
            outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
            outputs = e.message
            exit_layer = e.exit_layer
            logits = outputs[0]

        if not self.training:
            original_entropy = entropy(logits)
            highway_entropy = []
            highway_logits_all = []
        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1), labels.view(-1))
            else:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

            # work with highway exits
            highway_losses = []
            for highway_exit in outputs[-1]:
                highway_logits = highway_exit[0]
                if not self.training:
                    highway_logits_all.append(highway_logits)
                    highway_entropy.append(highway_exit[2])
                if self.num_labels == 1:
                    #  We are doing regression
                    loss_fct = MSELoss()
                    highway_loss = loss_fct(highway_logits.view(-1), labels.view(-1))
                else:
                    loss_fct = CrossEntropyLoss()
                    highway_loss = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
                highway_losses.append(highway_loss)

            if train_highway:
                outputs = (sum(highway_losses[:-1]),) + outputs
                # exclude the final highway, of course
            else:
                outputs = (loss,) + outputs
        if not self.training:
            outputs = outputs + ((original_entropy, highway_entropy), exit_layer)
            if output_layer >= 0:
                outputs = (
                    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                )  # use the highway of the last layer

        return outputs  # (loss), logits, (hidden_states), (attentions), entropy

from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",
    ROBERTA_START_DOCSTRING,
)
class DeeRobertaModel(DeeBertModel):
    config_class = RobertaConfig
    base_model_prefix = "roberta"

    def __init__(self, config):
        super().__init__(config)

        self.embeddings = RobertaEmbeddings(config)
        self.init_weights()


@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """,
    ROBERTA_START_DOCSTRING,
)
class DeeRobertaForSequenceClassification(BertPreTrainedModel):
    config_class = RobertaConfig
    base_model_prefix = "roberta"

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.num_layers = config.num_hidden_layers

        self.roberta = DeeRobertaModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)

    @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
        output_layer=-1,
        train_highway=False,
    ):
        r"""
            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
                Labels for computing the sequence classification/regression loss.
                Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
                If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
                If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:
            :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
            loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
                Classification (or regression if config.num_labels==1) loss.
            logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
                Classification (or regression if config.num_labels==1) scores (before SoftMax).
            hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
                Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
                of shape :obj:`(batch_size, sequence_length, hidden_size)`.

                Hidden-states of the model at the output of each layer plus the initial embedding outputs.
            attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
                Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
                :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

                Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
                heads.
            highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
                Tuple of each early exit's results (total length: number of layers)
                Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
        """

        exit_layer = self.num_layers
        try:
            outputs = self.roberta(
                input_ids,
                attention_mask=attention_mask,
                token_type_ids=token_type_ids,
                position_ids=position_ids,
                head_mask=head_mask,
                inputs_embeds=inputs_embeds,
            )

            pooled_output = outputs[1]

            pooled_output = self.dropout(pooled_output)
            logits = self.classifier(pooled_output)
            outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
            outputs = e.message
            exit_layer = e.exit_layer
            logits = outputs[0]

        if not self.training:
            original_entropy = entropy(logits)
            highway_entropy = []
            highway_logits_all = []
        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1), labels.view(-1))
            else:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

            # work with highway exits
            highway_losses = []
            for highway_exit in outputs[-1]:
                highway_logits = highway_exit[0]
                if not self.training:
                    highway_logits_all.append(highway_logits)
                    highway_entropy.append(highway_exit[2])
                if self.num_labels == 1:
                    #  We are doing regression
                    loss_fct = MSELoss()
                    highway_loss = loss_fct(highway_logits.view(-1), labels.view(-1))
                else:
                    loss_fct = CrossEntropyLoss()
                    highway_loss = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
                highway_losses.append(highway_loss)

            if train_highway:
                outputs = (sum(highway_losses[:-1]),) + outputs
                # exclude the final highway, of course
            else:
                outputs = (loss,) + outputs
        if not self.training:
            outputs = outputs + ((original_entropy, highway_entropy), exit_layer)
            if output_layer >= 0:
                outputs = (
                    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                )  # use the highway of the last layer

        return outputs  # (loss), logits, (hidden_states), (attentions), entropy
