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.
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" Transformer XL configuration"""

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


logger = logging.get_logger(__name__)

TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}


class TransfoXLConfig(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. It is
    used to instantiate a Transformer-XL 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 TransfoXL
    [transfo-xl-wt103](https://huggingface.co/transfo-xl-wt103) architecture.

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

    Args:
        vocab_size (`int`, *optional*, defaults to 267735):
            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`TransfoXLModel`] or [`TFTransfoXLModel`].
        cutoffs (`List[int]`, *optional*, defaults to `[20000, 40000, 200000]`):
            Cutoffs for the adaptive softmax.
        d_model (`int`, *optional*, defaults to 1024):
            Dimensionality of the model's hidden states.
        d_embed (`int`, *optional*, defaults to 1024):
            Dimensionality of the embeddings
        n_head (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        d_head (`int`, *optional*, defaults to 64):
            Dimensionality of the model's heads.
        d_inner (`int`, *optional*, defaults to 4096):
            Inner dimension in FF
        div_val (`int`, *optional*, defaults to 4):
            Divident value for adapative input and softmax
        pre_lnorm (`boolean`, *optional*, defaults to `False`):
            Whether or not to apply LayerNorm to the input instead of the output in the blocks.
        n_layer (`int`, *optional*, defaults to 18):
            Number of hidden layers in the Transformer encoder.
        mem_len (`int`, *optional*, defaults to 1600):
            Length of the retained previous heads.
        clamp_len (`int`, *optional*, defaults to 1000):
            Use the same pos embeddings after clamp_len.
        same_length (`boolean`, *optional*, defaults to `True`):
            Whether or not to use the same attn length for all tokens
        proj_share_all_but_first (`boolean`, *optional*, defaults to `True`):
            True to share all but first projs, False not to share.
        attn_type (`int`, *optional*, defaults to 0):
            Attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.
        sample_softmax (`int`, *optional*, defaults to -1):
            Number of samples in the sampled softmax.
        adaptive (`boolean`, *optional*, defaults to `True`):
            Whether or not to use adaptive softmax.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        dropatt (`float`, *optional*, defaults to 0):
            The dropout ratio for the attention probabilities.
        untie_r (`boolean`, *optional*, defaults to `True`):
            Whether ot not to untie relative position biases.
        init (`str`, *optional*, defaults to `"normal"`):
            Parameter initializer to use.
        init_range (`float`, *optional*, defaults to 0.01):
            Parameters initialized by U(-init_range, init_range).
        proj_init_std (`float`, *optional*, defaults to 0.01):
            Parameters initialized by N(0, init_std)
        init_std (`float`, *optional*, defaults to 0.02):
            Parameters initialized by N(0, init_std)
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
            The epsilon to use in the layer normalization layers

    Examples:

    ```python
    >>> from transformers import TransfoXLConfig, TransfoXLModel

    >>> # Initializing a Transformer XL configuration
    >>> configuration = TransfoXLConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = TransfoXLModel(configuration)

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

    model_type = "transfo-xl"
    keys_to_ignore_at_inference = ["mems"]
    attribute_map = {
        "n_token": "vocab_size",
        "hidden_size": "d_model",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(
        self,
        vocab_size=267735,
        cutoffs=[20000, 40000, 200000],
        d_model=1024,
        d_embed=1024,
        n_head=16,
        d_head=64,
        d_inner=4096,
        div_val=4,
        pre_lnorm=False,
        n_layer=18,
        mem_len=1600,
        clamp_len=1000,
        same_length=True,
        proj_share_all_but_first=True,
        attn_type=0,
        sample_softmax=-1,
        adaptive=True,
        dropout=0.1,
        dropatt=0.0,
        untie_r=True,
        init="normal",
        init_range=0.01,
        proj_init_std=0.01,
        init_std=0.02,
        layer_norm_epsilon=1e-5,
        eos_token_id=0,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.cutoffs = []
        self.cutoffs.extend(cutoffs)
        if proj_share_all_but_first:
            self.tie_projs = [False] + [True] * len(self.cutoffs)
        else:
            self.tie_projs = [False] + [False] * len(self.cutoffs)
        self.d_model = d_model
        self.d_embed = d_embed
        self.d_head = d_head
        self.d_inner = d_inner
        self.div_val = div_val
        self.pre_lnorm = pre_lnorm
        self.n_layer = n_layer
        self.n_head = n_head
        self.mem_len = mem_len
        self.same_length = same_length
        self.attn_type = attn_type
        self.clamp_len = clamp_len
        self.sample_softmax = sample_softmax
        self.adaptive = adaptive
        self.dropout = dropout
        self.dropatt = dropatt
        self.untie_r = untie_r
        self.init = init
        self.init_range = init_range
        self.proj_init_std = proj_init_std
        self.init_std = init_std
        self.layer_norm_epsilon = layer_norm_epsilon
        super().__init__(eos_token_id=eos_token_id, **kwargs)

    @property
    def max_position_embeddings(self):
        # Message copied from Transformer-XL documentation
        logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
        return -1

    @max_position_embeddings.setter
    def max_position_embeddings(self, value):
        # Message copied from Transformer-XL documentation
        raise NotImplementedError(
            f"The model {self.model_type} is one of the few models that has no sequence length limit."
        )

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" Transformer XL configuration"""

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


logger = logging.get_logger(__name__)

TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}


class TransfoXLConfig(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. It is
    used to instantiate a Transformer-XL 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 TransfoXL
    [transfo-xl-wt103](https://huggingface.co/transfo-xl-wt103) architecture.

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

    Args:
        vocab_size (`int`, *optional*, defaults to 267735):
            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`TransfoXLModel`] or [`TFTransfoXLModel`].
        cutoffs (`List[int]`, *optional*, defaults to `[20000, 40000, 200000]`):
            Cutoffs for the adaptive softmax.
        d_model (`int`, *optional*, defaults to 1024):
            Dimensionality of the model's hidden states.
        d_embed (`int`, *optional*, defaults to 1024):
            Dimensionality of the embeddings
        n_head (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        d_head (`int`, *optional*, defaults to 64):
            Dimensionality of the model's heads.
        d_inner (`int`, *optional*, defaults to 4096):
            Inner dimension in FF
        div_val (`int`, *optional*, defaults to 4):
            Divident value for adapative input and softmax
        pre_lnorm (`boolean`, *optional*, defaults to `False`):
            Whether or not to apply LayerNorm to the input instead of the output in the blocks.
        n_layer (`int`, *optional*, defaults to 18):
            Number of hidden layers in the Transformer encoder.
        mem_len (`int`, *optional*, defaults to 1600):
            Length of the retained previous heads.
        clamp_len (`int`, *optional*, defaults to 1000):
            Use the same pos embeddings after clamp_len.
        same_length (`boolean`, *optional*, defaults to `True`):
            Whether or not to use the same attn length for all tokens
        proj_share_all_but_first (`boolean`, *optional*, defaults to `True`):
            True to share all but first projs, False not to share.
        attn_type (`int`, *optional*, defaults to 0):
            Attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.
        sample_softmax (`int`, *optional*, defaults to -1):
            Number of samples in the sampled softmax.
        adaptive (`boolean`, *optional*, defaults to `True`):
            Whether or not to use adaptive softmax.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        dropatt (`float`, *optional*, defaults to 0):
            The dropout ratio for the attention probabilities.
        untie_r (`boolean`, *optional*, defaults to `True`):
            Whether ot not to untie relative position biases.
        init (`str`, *optional*, defaults to `"normal"`):
            Parameter initializer to use.
        init_range (`float`, *optional*, defaults to 0.01):
            Parameters initialized by U(-init_range, init_range).
        proj_init_std (`float`, *optional*, defaults to 0.01):
            Parameters initialized by N(0, init_std)
        init_std (`float`, *optional*, defaults to 0.02):
            Parameters initialized by N(0, init_std)
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
            The epsilon to use in the layer normalization layers

    Examples:

    ```python
    >>> from transformers import TransfoXLConfig, TransfoXLModel

    >>> # Initializing a Transformer XL configuration
    >>> configuration = TransfoXLConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = TransfoXLModel(configuration)

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

    model_type = "transfo-xl"
    keys_to_ignore_at_inference = ["mems"]
    attribute_map = {
        "n_token": "vocab_size",
        "hidden_size": "d_model",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(
        self,
        vocab_size=267735,
        cutoffs=[20000, 40000, 200000],
        d_model=1024,
        d_embed=1024,
        n_head=16,
        d_head=64,
        d_inner=4096,
        div_val=4,
        pre_lnorm=False,
        n_layer=18,
        mem_len=1600,
        clamp_len=1000,
        same_length=True,
        proj_share_all_but_first=True,
        attn_type=0,
        sample_softmax=-1,
        adaptive=True,
        dropout=0.1,
        dropatt=0.0,
        untie_r=True,
        init="normal",
        init_range=0.01,
        proj_init_std=0.01,
        init_std=0.02,
        layer_norm_epsilon=1e-5,
        eos_token_id=0,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.cutoffs = []
        self.cutoffs.extend(cutoffs)
        if proj_share_all_but_first:
            self.tie_projs = [False] + [True] * len(self.cutoffs)
        else:
            self.tie_projs = [False] + [False] * len(self.cutoffs)
        self.d_model = d_model
        self.d_embed = d_embed
        self.d_head = d_head
        self.d_inner = d_inner
        self.div_val = div_val
        self.pre_lnorm = pre_lnorm
        self.n_layer = n_layer
        self.n_head = n_head
        self.mem_len = mem_len
        self.same_length = same_length
        self.attn_type = attn_type
        self.clamp_len = clamp_len
        self.sample_softmax = sample_softmax
        self.adaptive = adaptive
        self.dropout = dropout
        self.dropatt = dropatt
        self.untie_r = untie_r
        self.init = init
        self.init_range = init_range
        self.proj_init_std = proj_init_std
        self.init_std = init_std
        self.layer_norm_epsilon = layer_norm_epsilon
        super().__init__(eos_token_id=eos_token_id, **kwargs)

    @property
    def max_position_embeddings(self):
        # Message copied from Transformer-XL documentation
        logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
        return -1

    @max_position_embeddings.setter
    def max_position_embeddings(self, value):
        # Message copied from Transformer-XL documentation
        raise NotImplementedError(
            f"The model {self.model_type} is one of the few models that has no sequence length limit."
        )
