# What does this PR do?

From the offline discussion + the comments from @patrickvonplaten in https://github.com/huggingface/transformers/pull/24827#issuecomment-1641750464 I propose a new design for tightly integrating PEFT into transformers. 
This integration enables loading any PEFT adapter that is saved locally or on the Hub directly into PEFT without dispatching the entire model creation process to PEFT as introduced in #24827. 

This would also enable an easier pipeline integration (a one-liner to load adapter weights) | EDIT: pipeline should work out of the box

Let's constraint this integration to few PEFT methods only, for simplicity and redirect users to use PEFT for advanced features (e.g. merge and unload) and advanced PEFT methods (adaptation prompt, prompt learning).

Current API:

## Load a model with an adapter locally or from the Hub:

```python
import torch
from transformers import AutoModelForCausalLM, OPTForCausalLM

model_id = "facebook/opt-350m"
adapter_model_id = "ybelkada/opt-350m-lora"

# directly on from_pretrained
model = AutoModelForCausalLM.from_pretrained(adapter_model_id)
print(model)

# directly on from_pretrained
model = OPTForCausalLM.from_pretrained(adapter_model_id)
print(model)
```

## Load and attach adapter to an existing model

```python
from transformers import AutoModelForCausalLM

# with load_adapter
model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(adapter_model_id)

print(model)

# 8-bit + multiGPU compatiblity
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True, device_map="balanced")
model.load_adapter(adapter_model_id)

print(model)
print(set(model.hf_device_map.values()))

_ = model(torch.LongTensor([[0, 1, 2, 3]]).to(0))
```

## Attach an adapter, iteratively enable / disable adapters

```python
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig

model_id = "facebook/opt-350m"
adapter_model_id = "ybelkada/opt-350m-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id)
text = "Hello"
inputs = tokenizer(text, return_tensors="pt")

model = AutoModelForCausalLM.from_pretrained(model_id)
peft_config = PeftConfig.from_pretrained(adapter_model_id)

# To get random weights
peft_config.init_lora_weights = False

model.add_adapter(peft_config)
print(model)

model.disable_adapters()
output_disabled = model.generate(**inputs)
print(tokenizer.decode(output_disabled[0], skip_special_tokens=True))
>>> Hello, I'm a newbie to this sub. I'm looking for a good place to

model.enable_adapters()
output_enabled = model.generate(**inputs)
print(tokenizer.decode(output_enabled[0], skip_special_tokens=True))
>>> Hello, MMMMMMMM
```

## Add multiple adapters

```python
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig, LoraConfig

model_id = "facebook/opt-350m"

# directly on from_pretrained
model = AutoModelForCausalLM.from_pretrained(model_id)

lora_config = LoraConfig(
    target_modules=["q_proj", "k_proj"],
    init_lora_weights=False
)

model.add_adapter(lora_config, adapter_name="adapter_1")

# attach new adapter with same config
model.add_adapter(lora_config, adapter_name="adapter_2")

model.set_adapter("adapter_1")
output_disabled = model.generate(**inputs)
print(tokenizer.decode(output_disabled[0], skip_special_tokens=True))
>>> Hello, I'm a newbie to this sub. I'm looking for a good place to

model.set_adapter("adapter_2")
output_enabled = model.generate(**inputs)
print(tokenizer.decode(output_enabled[0], skip_special_tokens=True))
>>> Hello, I'm a newbie to the game. I'm looking for a good way to
```

## Save adapters

```python
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig, LoraConfig

model_id = "facebook/opt-350m"

# directly on from_pretrained
model = AutoModelForCausalLM.from_pretrained(model_id)

lora_config = LoraConfig(
    target_modules=["q_proj", "k_proj"],
)

model.add_adapter(lora_config)

... # train here

model.save_pretrained(save_dir) 

# you can either load it back with transformers or PEFT

from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained(save_dir)

# or

model = AutoModelForCausalLM.from_pretrained(save_dir)
```

## Train adapters using Trainer

Check this gist: https://gist.github.com/younesbelkada/cdda6e4abcb09e58f6324d75e0d88862 

This PR is on par with: https://github.com/huggingface/peft/pull/749

Features to support:

- [x] loading PEFT adapters
- [x] using multiple adapters
- [x] deal with models loaded with accelerate
- [x] Loading directly from `from_pretrained`
- [ ] Merging adapter weights - to not support
- [ ] Unload adapter weights - to not support
- [x] Training with BC with expected PEFT checkpoints format (do we really want to support training? Shall we just redirect users to load a classic `PeftModel` if they want to train a model?)
- [x] What about `save_pretrained` ? 
 
Features to **not** support:

- [x] disabling adapters
- [x] prompt tuning / prompt learning methods

TODOs:

- [ ] docs
- [x] tests

cc @sgugger @patrickvonplaten @BenjaminBossan @pacman100 
[`PEFT`] Peft integration alternative design . # What does this PR do?

From the offline discussion + the comments from @patrickvonplaten in https://github.com/huggingface/transformers/pull/24827#issuecomment-1641750464 I propose a new design for tightly integrating PEFT into transformers. 
This integration enables loading any PEFT adapter that is saved locally or on the Hub directly into PEFT without dispatching the entire model creation process to PEFT as introduced in #24827. 

This would also enable an easier pipeline integration (a one-liner to load adapter weights) | EDIT: pipeline should work out of the box

Let's constraint this integration to few PEFT methods only, for simplicity and redirect users to use PEFT for advanced features (e.g. merge and unload) and advanced PEFT methods (adaptation prompt, prompt learning).

Current API:

## Load a model with an adapter locally or from the Hub:

```python
import torch
from transformers import AutoModelForCausalLM, OPTForCausalLM

model_id = "facebook/opt-350m"
adapter_model_id = "ybelkada/opt-350m-lora"

# directly on from_pretrained
model = AutoModelForCausalLM.from_pretrained(adapter_model_id)
print(model)

# directly on from_pretrained
model = OPTForCausalLM.from_pretrained(adapter_model_id)
print(model)
```

## Load and attach adapter to an existing model

```python
from transformers import AutoModelForCausalLM

# with load_adapter
model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(adapter_model_id)

print(model)

# 8-bit + multiGPU compatiblity
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True, device_map="balanced")
model.load_adapter(adapter_model_id)

print(model)
print(set(model.hf_device_map.values()))

_ = model(torch.LongTensor([[0, 1, 2, 3]]).to(0))
```

## Attach an adapter, iteratively enable / disable adapters

```python
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig

model_id = "facebook/opt-350m"
adapter_model_id = "ybelkada/opt-350m-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id)
text = "Hello"
inputs = tokenizer(text, return_tensors="pt")

model = AutoModelForCausalLM.from_pretrained(model_id)
peft_config = PeftConfig.from_pretrained(adapter_model_id)

# To get random weights
peft_config.init_lora_weights = False

model.add_adapter(peft_config)
print(model)

model.disable_adapters()
output_disabled = model.generate(**inputs)
print(tokenizer.decode(output_disabled[0], skip_special_tokens=True))
>>> Hello, I'm a newbie to this sub. I'm looking for a good place to

model.enable_adapters()
output_enabled = model.generate(**inputs)
print(tokenizer.decode(output_enabled[0], skip_special_tokens=True))
>>> Hello, MMMMMMMM
```

## Add multiple adapters

```python
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig, LoraConfig

model_id = "facebook/opt-350m"

# directly on from_pretrained
model = AutoModelForCausalLM.from_pretrained(model_id)

lora_config = LoraConfig(
    target_modules=["q_proj", "k_proj"],
    init_lora_weights=False
)

model.add_adapter(lora_config, adapter_name="adapter_1")

# attach new adapter with same config
model.add_adapter(lora_config, adapter_name="adapter_2")

model.set_adapter("adapter_1")
output_disabled = model.generate(**inputs)
print(tokenizer.decode(output_disabled[0], skip_special_tokens=True))
>>> Hello, I'm a newbie to this sub. I'm looking for a good place to

model.set_adapter("adapter_2")
output_enabled = model.generate(**inputs)
print(tokenizer.decode(output_enabled[0], skip_special_tokens=True))
>>> Hello, I'm a newbie to the game. I'm looking for a good way to
```

## Save adapters

```python
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig, LoraConfig

model_id = "facebook/opt-350m"

# directly on from_pretrained
model = AutoModelForCausalLM.from_pretrained(model_id)

lora_config = LoraConfig(
    target_modules=["q_proj", "k_proj"],
)

model.add_adapter(lora_config)

... # train here

model.save_pretrained(save_dir) 

# you can either load it back with transformers or PEFT

from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained(save_dir)

# or

model = AutoModelForCausalLM.from_pretrained(save_dir)
```

## Train adapters using Trainer

Check this gist: https://gist.github.com/younesbelkada/cdda6e4abcb09e58f6324d75e0d88862 

This PR is on par with: https://github.com/huggingface/peft/pull/749

Features to support:

- [x] loading PEFT adapters
- [x] using multiple adapters
- [x] deal with models loaded with accelerate
- [x] Loading directly from `from_pretrained`
- [ ] Merging adapter weights - to not support
- [ ] Unload adapter weights - to not support
- [x] Training with BC with expected PEFT checkpoints format (do we really want to support training? Shall we just redirect users to load a classic `PeftModel` if they want to train a model?)
- [x] What about `save_pretrained` ? 
 
Features to **not** support:

- [x] disabling adapters
- [x] prompt tuning / prompt learning methods

TODOs:

- [ ] docs
- [x] tests

cc @sgugger @patrickvonplaten @BenjaminBossan @pacman100 
# coding=utf-8
# Copyright 2023 Meta AI 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.
""" MusicGen model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


logger = logging.get_logger(__name__)

MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}


class MusicgenDecoderConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of an [`MusicgenDecoder`]. It is used to instantiate a
    MusicGen decoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the MusicGen
    [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) 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 2048):
            Vocabulary size of the MusicgenDecoder model. Defines the number of different tokens that can be
            represented by the `inputs_ids` passed when calling [`MusicgenDecoder`].
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of decoder layers.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer block.
        ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer block.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the decoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, text_encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            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).
        initializer_factor (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        scale_embedding (`bool`, *optional*, defaults to `False`):
            Scale embeddings by diving by sqrt(hidden_size).
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether the model should return the last key/values attentions (not used by all models)
        num_codebooks (`int`, *optional*, defaults to 4):
            The number of parallel codebooks forwarded to the model.
        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
            Whether input and output word embeddings should be tied.
    """
    model_type = "musicgen_decoder"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=2048,
        max_position_embeddings=2048,
        num_hidden_layers=24,
        ffn_dim=4096,
        num_attention_heads=16,
        layerdrop=0.0,
        use_cache=True,
        activation_function="gelu",
        hidden_size=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        initializer_factor=0.02,
        scale_embedding=False,
        num_codebooks=4,
        pad_token_id=2048,
        bos_token_id=2048,
        eos_token_id=None,
        tie_word_embeddings=False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.ffn_dim = ffn_dim
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.initializer_factor = initializer_factor
        self.layerdrop = layerdrop
        self.use_cache = use_cache
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
        self.num_codebooks = num_codebooks
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


class MusicgenConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MusicgenModel`]. It is used to instantiate a
    MusicGen model according to the specified arguments, defining the text encoder, audio encoder and MusicGen decoder
    configs.

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

    Args:
        kwargs (*optional*):
            Dictionary of keyword arguments. Notably:

                - **text_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
                  defines the text encoder config.
                - **audio_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
                  defines the audio encoder config.
                - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                  the decoder config.

    Example:

    ```python
    >>> from transformers import (
    ...     MusicgenConfig,
    ...     MusicgenDecoderConfig,
    ...     T5Config,
    ...     EncodecConfig,
    ...     MusicgenForConditionalGeneration,
    ... )

    >>> # Initializing text encoder, audio encoder, and decoder model configurations
    >>> text_encoder_config = T5Config()
    >>> audio_encoder_config = EncodecConfig()
    >>> decoder_config = MusicgenDecoderConfig()

    >>> configuration = MusicgenConfig.from_sub_models_config(
    ...     text_encoder_config, audio_encoder_config, decoder_config
    ... )

    >>> # Initializing a MusicgenForConditionalGeneration (with random weights) from the facebook/musicgen-small style configuration
    >>> model = MusicgenForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    >>> config_text_encoder = model.config.text_encoder
    >>> config_audio_encoder = model.config.audio_encoder
    >>> config_decoder = model.config.decoder

    >>> # Saving the model, including its configuration
    >>> model.save_pretrained("musicgen-model")

    >>> # loading model and config from pretrained folder
    >>> musicgen_config = MusicgenConfig.from_pretrained("musicgen-model")
    >>> model = MusicgenForConditionalGeneration.from_pretrained("musicgen-model", config=musicgen_config)
    ```"""

    model_type = "musicgen"
    is_composition = True

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")

        text_encoder_config = kwargs.pop("text_encoder")
        text_encoder_model_type = text_encoder_config.pop("model_type")

        audio_encoder_config = kwargs.pop("audio_encoder")
        audio_encoder_model_type = audio_encoder_config.pop("model_type")

        decoder_config = kwargs.pop("decoder")

        self.text_encoder = AutoConfig.for_model(text_encoder_model_type, **text_encoder_config)
        self.audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
        self.decoder = MusicgenDecoderConfig(**decoder_config)
        self.is_encoder_decoder = True

    @classmethod
    def from_sub_models_config(
        cls,
        text_encoder_config: PretrainedConfig,
        audio_encoder_config: PretrainedConfig,
        decoder_config: MusicgenDecoderConfig,
        **kwargs,
    ):
        r"""
        Instantiate a [`MusicgenConfig`] (or a derived class) from text encoder, audio encoder and decoder
        configurations.

        Returns:
            [`MusicgenConfig`]: An instance of a configuration object
        """

        return cls(
            text_encoder=text_encoder_config.to_dict(),
            audio_encoder=audio_encoder_config.to_dict(),
            decoder=decoder_config.to_dict(),
            **kwargs,
        )

# coding=utf-8
# Copyright 2023 Meta AI 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.
""" MusicGen model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


logger = logging.get_logger(__name__)

MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}


class MusicgenDecoderConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of an [`MusicgenDecoder`]. It is used to instantiate a
    MusicGen decoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the MusicGen
    [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) 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 2048):
            Vocabulary size of the MusicgenDecoder model. Defines the number of different tokens that can be
            represented by the `inputs_ids` passed when calling [`MusicgenDecoder`].
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of decoder layers.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer block.
        ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer block.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the decoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, text_encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            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).
        initializer_factor (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        scale_embedding (`bool`, *optional*, defaults to `False`):
            Scale embeddings by diving by sqrt(hidden_size).
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether the model should return the last key/values attentions (not used by all models)
        num_codebooks (`int`, *optional*, defaults to 4):
            The number of parallel codebooks forwarded to the model.
        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
            Whether input and output word embeddings should be tied.
    """
    model_type = "musicgen_decoder"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=2048,
        max_position_embeddings=2048,
        num_hidden_layers=24,
        ffn_dim=4096,
        num_attention_heads=16,
        layerdrop=0.0,
        use_cache=True,
        activation_function="gelu",
        hidden_size=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        initializer_factor=0.02,
        scale_embedding=False,
        num_codebooks=4,
        pad_token_id=2048,
        bos_token_id=2048,
        eos_token_id=None,
        tie_word_embeddings=False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.ffn_dim = ffn_dim
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.initializer_factor = initializer_factor
        self.layerdrop = layerdrop
        self.use_cache = use_cache
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
        self.num_codebooks = num_codebooks
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


class MusicgenConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MusicgenModel`]. It is used to instantiate a
    MusicGen model according to the specified arguments, defining the text encoder, audio encoder and MusicGen decoder
    configs.

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

    Args:
        kwargs (*optional*):
            Dictionary of keyword arguments. Notably:

                - **text_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
                  defines the text encoder config.
                - **audio_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
                  defines the audio encoder config.
                - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                  the decoder config.

    Example:

    ```python
    >>> from transformers import (
    ...     MusicgenConfig,
    ...     MusicgenDecoderConfig,
    ...     T5Config,
    ...     EncodecConfig,
    ...     MusicgenForConditionalGeneration,
    ... )

    >>> # Initializing text encoder, audio encoder, and decoder model configurations
    >>> text_encoder_config = T5Config()
    >>> audio_encoder_config = EncodecConfig()
    >>> decoder_config = MusicgenDecoderConfig()

    >>> configuration = MusicgenConfig.from_sub_models_config(
    ...     text_encoder_config, audio_encoder_config, decoder_config
    ... )

    >>> # Initializing a MusicgenForConditionalGeneration (with random weights) from the facebook/musicgen-small style configuration
    >>> model = MusicgenForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    >>> config_text_encoder = model.config.text_encoder
    >>> config_audio_encoder = model.config.audio_encoder
    >>> config_decoder = model.config.decoder

    >>> # Saving the model, including its configuration
    >>> model.save_pretrained("musicgen-model")

    >>> # loading model and config from pretrained folder
    >>> musicgen_config = MusicgenConfig.from_pretrained("musicgen-model")
    >>> model = MusicgenForConditionalGeneration.from_pretrained("musicgen-model", config=musicgen_config)
    ```"""

    model_type = "musicgen"
    is_composition = True

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")

        text_encoder_config = kwargs.pop("text_encoder")
        text_encoder_model_type = text_encoder_config.pop("model_type")

        audio_encoder_config = kwargs.pop("audio_encoder")
        audio_encoder_model_type = audio_encoder_config.pop("model_type")

        decoder_config = kwargs.pop("decoder")

        self.text_encoder = AutoConfig.for_model(text_encoder_model_type, **text_encoder_config)
        self.audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
        self.decoder = MusicgenDecoderConfig(**decoder_config)
        self.is_encoder_decoder = True

    @classmethod
    def from_sub_models_config(
        cls,
        text_encoder_config: PretrainedConfig,
        audio_encoder_config: PretrainedConfig,
        decoder_config: MusicgenDecoderConfig,
        **kwargs,
    ):
        r"""
        Instantiate a [`MusicgenConfig`] (or a derived class) from text encoder, audio encoder and decoder
        configurations.

        Returns:
            [`MusicgenConfig`]: An instance of a configuration object
        """

        return cls(
            text_encoder=text_encoder_config.to_dict(),
            audio_encoder=audio_encoder_config.to_dict(),
            decoder=decoder_config.to_dict(),
            **kwargs,
        )
