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 typing import Dict

from .base import GenericTensor, Pipeline


# Can't use @add_end_docstrings(PIPELINE_INIT_ARGS) here because this one does not accept `binary_output`
class FeatureExtractionPipeline(Pipeline):
    """
    Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base
    transformer, which can be used as features in downstream tasks.

    Example:

    ```python
    >>> from transformers import pipeline

    >>> extractor = pipeline(model="bert-base-uncased", task="feature-extraction")
    >>> result = extractor("This is a simple test.", return_tensors=True)
    >>> result.shape  # This is a tensor of shape [1, sequence_lenth, hidden_dimension] representing the input string.
    torch.Size([1, 8, 768])
    ```

    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)

    This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier:
    `"feature-extraction"`.

    All models may be used for this pipeline. See a list of all models, including community-contributed models on
    [huggingface.co/models](https://huggingface.co/models).

    Arguments:
        model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
            [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
        tokenizer ([`PreTrainedTokenizer`]):
            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
            [`PreTrainedTokenizer`].
        modelcard (`str` or [`ModelCard`], *optional*):
            Model card attributed to the model for this pipeline.
        framework (`str`, *optional*):
            The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
            installed.

            If no framework is specified, will default to the one currently installed. If no framework is specified and
            both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
            provided.
        return_tensors (`bool`, *optional*):
            If `True`, returns a tensor according to the specified framework, otherwise returns a list.
        task (`str`, defaults to `""`):
            A task-identifier for the pipeline.
        args_parser ([`~pipelines.ArgumentHandler`], *optional*):
            Reference to the object in charge of parsing supplied pipeline parameters.
        device (`int`, *optional*, defaults to -1):
            Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
            the associated CUDA device id.
        tokenize_kwargs (`dict`, *optional*):
            Additional dictionary of keyword arguments passed along to the tokenizer.
    """

    def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, return_tensors=None, **kwargs):
        if tokenize_kwargs is None:
            tokenize_kwargs = {}

        if truncation is not None:
            if "truncation" in tokenize_kwargs:
                raise ValueError(
                    "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"
                )
            tokenize_kwargs["truncation"] = truncation

        preprocess_params = tokenize_kwargs

        postprocess_params = {}
        if return_tensors is not None:
            postprocess_params["return_tensors"] = return_tensors

        return preprocess_params, {}, postprocess_params

    def preprocess(self, inputs, **tokenize_kwargs) -> Dict[str, GenericTensor]:
        return_tensors = self.framework
        model_inputs = self.tokenizer(inputs, return_tensors=return_tensors, **tokenize_kwargs)
        return model_inputs

    def _forward(self, model_inputs):
        model_outputs = self.model(**model_inputs)
        return model_outputs

    def postprocess(self, model_outputs, return_tensors=False):
        # [0] is the first available tensor, logits or last_hidden_state.
        if return_tensors:
            return model_outputs[0]
        if self.framework == "pt":
            return model_outputs[0].tolist()
        elif self.framework == "tf":
            return model_outputs[0].numpy().tolist()

    def __call__(self, *args, **kwargs):
        """
        Extract the features of the input(s).

        Args:
            args (`str` or `List[str]`): One or several texts (or one list of texts) to get the features of.

        Return:
            A nested list of `float`: The features computed by the model.
        """
        return super().__call__(*args, **kwargs)

from typing import Dict

from .base import GenericTensor, Pipeline


# Can't use @add_end_docstrings(PIPELINE_INIT_ARGS) here because this one does not accept `binary_output`
class FeatureExtractionPipeline(Pipeline):
    """
    Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base
    transformer, which can be used as features in downstream tasks.

    Example:

    ```python
    >>> from transformers import pipeline

    >>> extractor = pipeline(model="bert-base-uncased", task="feature-extraction")
    >>> result = extractor("This is a simple test.", return_tensors=True)
    >>> result.shape  # This is a tensor of shape [1, sequence_lenth, hidden_dimension] representing the input string.
    torch.Size([1, 8, 768])
    ```

    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)

    This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier:
    `"feature-extraction"`.

    All models may be used for this pipeline. See a list of all models, including community-contributed models on
    [huggingface.co/models](https://huggingface.co/models).

    Arguments:
        model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
            [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
        tokenizer ([`PreTrainedTokenizer`]):
            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
            [`PreTrainedTokenizer`].
        modelcard (`str` or [`ModelCard`], *optional*):
            Model card attributed to the model for this pipeline.
        framework (`str`, *optional*):
            The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
            installed.

            If no framework is specified, will default to the one currently installed. If no framework is specified and
            both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
            provided.
        return_tensors (`bool`, *optional*):
            If `True`, returns a tensor according to the specified framework, otherwise returns a list.
        task (`str`, defaults to `""`):
            A task-identifier for the pipeline.
        args_parser ([`~pipelines.ArgumentHandler`], *optional*):
            Reference to the object in charge of parsing supplied pipeline parameters.
        device (`int`, *optional*, defaults to -1):
            Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
            the associated CUDA device id.
        tokenize_kwargs (`dict`, *optional*):
            Additional dictionary of keyword arguments passed along to the tokenizer.
    """

    def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, return_tensors=None, **kwargs):
        if tokenize_kwargs is None:
            tokenize_kwargs = {}

        if truncation is not None:
            if "truncation" in tokenize_kwargs:
                raise ValueError(
                    "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"
                )
            tokenize_kwargs["truncation"] = truncation

        preprocess_params = tokenize_kwargs

        postprocess_params = {}
        if return_tensors is not None:
            postprocess_params["return_tensors"] = return_tensors

        return preprocess_params, {}, postprocess_params

    def preprocess(self, inputs, **tokenize_kwargs) -> Dict[str, GenericTensor]:
        return_tensors = self.framework
        model_inputs = self.tokenizer(inputs, return_tensors=return_tensors, **tokenize_kwargs)
        return model_inputs

    def _forward(self, model_inputs):
        model_outputs = self.model(**model_inputs)
        return model_outputs

    def postprocess(self, model_outputs, return_tensors=False):
        # [0] is the first available tensor, logits or last_hidden_state.
        if return_tensors:
            return model_outputs[0]
        if self.framework == "pt":
            return model_outputs[0].tolist()
        elif self.framework == "tf":
            return model_outputs[0].numpy().tolist()

    def __call__(self, *args, **kwargs):
        """
        Extract the features of the input(s).

        Args:
            args (`str` or `List[str]`): One or several texts (or one list of texts) to get the features of.

        Return:
            A nested list of `float`: The features computed by the model.
        """
        return super().__call__(*args, **kwargs)
