Upload CM3PForBeatmapClassification

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
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+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
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+#### Hardware
+
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+
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+
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+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+## Model Card Authors [optional]
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+## Model Card Contact
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config.json

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+{
+  "architectures": [
+    "CM3PForBeatmapClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "audio_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "decoder_bias": true,
+    "deterministic_flash_attn": false,
+    "embedding_dropout": 0.0,
+    "f_max": 8000,
+    "f_min": 0,
+    "global_attn_every_n_layers": 3,
+    "global_rope_theta": 160000.0,
+    "hidden_activation": "gelu",
+    "hidden_size": 512,
+    "hop_length": 128,
+    "initializer_cutoff_factor": 2.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 1024,
+    "local_attention": 128,
+    "local_rope_theta": 10000.0,
+    "max_position_embeddings": 4096,
+    "mlp_bias": false,
+    "mlp_dropout": 0.0,
+    "model_type": "cm3p_audio_model",
+    "n_ftt": 2048,
+    "n_mels": 80,
+    "norm_bias": false,
+    "norm_eps": 1e-05,
+    "num_attention_heads": 8,
+    "num_hidden_layers": 6,
+    "pad_mode": "constant",
+    "projector_dim": 768,
+    "projector_hidden_act": "gelu",
+    "projector_intermediate_size": 2048,
+    "sample_rate": 16000,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 1
+  },
+  "audio_eos_token_id": 3966,
+  "audio_sos_token_id": null,
+  "audio_token_id": 3967,
+  "auto_map": {
+    "AutoConfig": "configuration_cm3p.CM3PBeatmapConfig",
+    "AutoModelForSequenceClassification": "modeling_cm3p.CM3PForBeatmapClassification"
+  },
+  "bos_token_id": 3958,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "cls_embed": true,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 3959,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Graveyard",
+    "1": "Ranked"
+  },
+  "initializer_cutoff_factor": 2.0,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "label2id": null,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "cm3p_beatmap_model",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 3962,
+  "problem_type": "single_label_classification",
+  "projection_dim": 512,
+  "repad_logits_with_grad": false,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0",
+  "vocab_size": 3968
+}

configuration_cm3p.py

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+"""CM3P model configuration"""
+from transformers import AutoConfig
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class CM3PMetadataConfig(PretrainedConfig):
+    model_type = "cm3p_metadata_model"
+    base_config_key = "metadata_config"
+
+    def __init__(
+        self,
+        cls_embed=False,
+
+        projection_dim=512,
+        initializer_factor=1.0,
+
+        vocab_size=1000,
+        hidden_size=256,
+        intermediate_size=512,
+        num_hidden_layers=6,
+        num_attention_heads=4,
+        hidden_activation="gelu",
+        max_position_embeddings=128,
+        initializer_range=0.02,
+        initializer_cutoff_factor=2.0,
+        norm_eps=1e-5,
+        norm_bias=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        global_rope_theta=10000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        global_attn_every_n_layers=1,
+        local_attention=128,
+        local_rope_theta=10000.0,
+        embedding_dropout=0.0,
+        mlp_bias=False,
+        mlp_dropout=0.0,
+        decoder_bias=True,
+        deterministic_flash_attn=False,
+        reference_compile=None,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+
+        self.cls_embed = cls_embed
+
+        self.projection_dim = projection_dim
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.initializer_range = initializer_range
+        self.initializer_cutoff_factor = initializer_cutoff_factor
+        self.norm_eps = norm_eps
+        self.norm_bias = norm_bias
+        self.global_rope_theta = global_rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.hidden_activation = hidden_activation
+        self.global_attn_every_n_layers = global_attn_every_n_layers
+        self.local_attention = local_attention
+        self.local_rope_theta = local_rope_theta
+        self.embedding_dropout = embedding_dropout
+        self.mlp_bias = mlp_bias
+        self.mlp_dropout = mlp_dropout
+        self.decoder_bias = decoder_bias
+        self.deterministic_flash_attn = deterministic_flash_attn
+        self.reference_compile = reference_compile
+
+    def to_dict(self):
+        output = super().to_dict()
+        output.pop("reference_compile", None)
+        return output
+
+
+class CM3PAudioConfig(PretrainedConfig):
+    model_type = "cm3p_audio_model"
+    base_config_key = "audio_config"
+
+    def __init__(
+        self,
+        hidden_size=512,
+        intermediate_size=1024,
+        num_hidden_layers=6,
+        num_attention_heads=8,
+        hidden_activation="gelu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        initializer_cutoff_factor=2.0,
+        norm_eps=1e-5,
+        norm_bias=False,
+        global_rope_theta=160000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        global_attn_every_n_layers=3,
+        local_attention=128,
+        local_rope_theta=10000.0,
+        embedding_dropout=0.0,
+        mlp_bias=False,
+        mlp_dropout=0.0,
+        decoder_bias=True,
+        deterministic_flash_attn=False,
+        reference_compile=None,
+
+        projector_intermediate_size=2048,  # 4 * hidden_size for a 4x reduction in tokens
+        projector_dim=768,
+        projector_hidden_act="gelu",
+
+        sample_rate: int = 16000,
+        n_ftt: int = 2048,
+        n_mels: int = 80,
+        hop_length: int = 128,
+        f_min: int = 0,
+        f_max: int = 8000,
+        pad_mode: str = "constant",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = 1
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.initializer_range = initializer_range
+        self.initializer_cutoff_factor = initializer_cutoff_factor
+        self.norm_eps = norm_eps
+        self.norm_bias = norm_bias
+        self.global_rope_theta = global_rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.hidden_activation = hidden_activation
+        self.global_attn_every_n_layers = global_attn_every_n_layers
+        self.local_attention = local_attention
+        self.local_rope_theta = local_rope_theta
+        self.embedding_dropout = embedding_dropout
+        self.mlp_bias = mlp_bias
+        self.mlp_dropout = mlp_dropout
+        self.decoder_bias = decoder_bias
+        self.deterministic_flash_attn = deterministic_flash_attn
+        self.reference_compile = reference_compile
+
+        self.projector_intermediate_size = projector_intermediate_size
+        self.projector_dim = projector_dim
+        self.projector_hidden_act = projector_hidden_act
+
+        self.sample_rate = sample_rate
+        self.n_ftt = n_ftt
+        self.n_mels = n_mels
+        self.hop_length = hop_length
+        self.f_min = f_min
+        self.f_max = f_max
+        self.pad_mode = pad_mode
+
+    def to_dict(self):
+        output = super().to_dict()
+        output.pop("reference_compile", None)
+        return output
+
+
+class CM3PBeatmapConfig(PretrainedConfig):
+    model_type = "cm3p_beatmap_model"
+    base_config_key = "beatmap_config"
+    sub_configs = {"audio_config": CM3PAudioConfig}
+
+    def __init__(
+        self,
+        audio_config: dict = None,
+        audio_sos_token_id=3164,
+        audio_eos_token_id=3165,
+        audio_token_id=3166,
+        cls_embed=False,
+
+        projection_dim=512,
+        initializer_factor=1.0,
+
+        vocab_size=3167,
+        hidden_size=768,
+        intermediate_size=1152,
+        num_hidden_layers=22,
+        num_attention_heads=12,
+        hidden_activation="gelu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        initializer_cutoff_factor=2.0,
+        norm_eps=1e-5,
+        norm_bias=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        global_rope_theta=160000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        global_attn_every_n_layers=3,
+        local_attention=128,
+        local_rope_theta=10000.0,
+        embedding_dropout=0.0,
+        mlp_bias=False,
+        mlp_dropout=0.0,
+        decoder_bias=True,
+        classifier_bias=False,
+        classifier_activation="gelu",
+        deterministic_flash_attn=False,
+        sparse_prediction=False,
+        sparse_pred_ignore_index=-100,
+        reference_compile=None,
+        repad_logits_with_grad=False,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+
+        if audio_config is None:
+            audio_config = {}
+            logger.info("`audio_config` is `None`. Initializing the `CM3PAudioConfig` with default values.")
+
+        self.audio_config = CM3PAudioConfig(**audio_config)
+        self.audio_sos_token_id = audio_sos_token_id
+        self.audio_eos_token_id = audio_eos_token_id
+        self.audio_token_id = audio_token_id
+        self.cls_embed = cls_embed
+
+        self.projection_dim = projection_dim
+        self.initializer_factor = initializer_factor
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.initializer_range = initializer_range
+        self.initializer_cutoff_factor = initializer_cutoff_factor
+        self.norm_eps = norm_eps
+        self.norm_bias = norm_bias
+        self.global_rope_theta = global_rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.hidden_activation = hidden_activation
+        self.global_attn_every_n_layers = global_attn_every_n_layers
+        self.local_attention = local_attention
+        self.local_rope_theta = local_rope_theta
+        self.embedding_dropout = embedding_dropout
+        self.mlp_bias = mlp_bias
+        self.mlp_dropout = mlp_dropout
+        self.decoder_bias = decoder_bias
+        self.classifier_bias = classifier_bias
+        self.classifier_activation = classifier_activation
+        self.deterministic_flash_attn = deterministic_flash_attn
+        self.sparse_prediction = sparse_prediction
+        self.sparse_pred_ignore_index = sparse_pred_ignore_index
+        self.reference_compile = reference_compile
+        self.repad_logits_with_grad = repad_logits_with_grad
+
+    def to_dict(self):
+        output = super().to_dict()
+        output.pop("reference_compile", None)
+        return output
+
+
+class CM3PConfig(PretrainedConfig):
+    model_type = "cm3p"
+    sub_configs = {"metadata_config": CM3PMetadataConfig, "beatmap_config": CM3PBeatmapConfig}
+
+    def __init__(
+        self,
+        metadata_config=None,
+        beatmap_config=None,
+        projection_dim=512,
+        logit_scale_init_value=2.6592,
+        initializer_factor=1.0,
+        initializer_range=0.02,
+        loss_type=None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        if metadata_config is None:
+            metadata_config = {}
+            logger.debug("`metadata_config` is `None`. Initializing the `CM3PMetadataConfig` with default values.")
+
+        if beatmap_config is None:
+            beatmap_config = {}
+            logger.debug("`beatmap_config` is `None`. initializing the `CM3PBeatmapConfig` with default values.")
+
+        self.metadata_config = CM3PMetadataConfig(**metadata_config)
+        self.beatmap_config = CM3PBeatmapConfig(**beatmap_config)
+
+        self.projection_dim = projection_dim
+        self.logit_scale_init_value = logit_scale_init_value
+        self.initializer_factor = initializer_factor
+        self.initializer_range = initializer_range
+        self.loss_type = loss_type
+
+
+AutoConfig.register("cm3p_metadata_model", CM3PMetadataConfig)
+AutoConfig.register("cm3p_audio_model", CM3PAudioConfig)
+AutoConfig.register("cm3p_beatmap_model", CM3PBeatmapConfig)
+AutoConfig.register("cm3p", CM3PConfig)
+
+__all__ = ["CM3PConfig", "CM3PMetadataConfig", "CM3PAudioConfig", "CM3PBeatmapConfig"]

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c89b644fcd1d5b2016aa5688ff95a5f8ba136f5983658e9b2d8fb1acda56b2fd
+size 264400804

modeling_cm3p.py

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+"""PyTorch CM3P model."""
+from contextlib import nullcontext
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers import ModernBertModel, AutoModel, AutoModelForSequenceClassification, AutoModelForMaskedLM
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling, MaskedLMOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import ModelOutput, auto_docstring, can_return_tuple, logging
+
+from .configuration_cm3p import CM3PConfig, CM3PMetadataConfig, CM3PBeatmapConfig, CM3PAudioConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# contrastive loss function, adapted from
+# https://sachinruk.github.io/blog/2021-03-07-clip.html
+def contrastive_loss(logits: torch.Tensor, target: torch.LongTensor = None) -> torch.Tensor:
+    target = target if target is not None else torch.arange(len(logits), device=logits.device)
+    return nn.functional.cross_entropy(logits, target)
+
+
+# CM3P loss function, adapted from CLIP
+def cm3p_loss(similarity: torch.Tensor, metadata_variation_classes: torch.LongTensor = None) -> torch.Tensor:
+    if similarity.dim() == 3:  # (metadata_batch_size, variations, beatmap_batch_size)
+        metadata_batch_size = similarity.size(0)
+        num_variations = similarity.size(1)
+        beatmap_batch_size = similarity.size(2)
+        assert metadata_batch_size == beatmap_batch_size
+
+        true_metadata_indices = (metadata_variation_classes == 0).int().argmax(dim=1)
+        metadata_loss = contrastive_loss(similarity[torch.arange(metadata_batch_size), true_metadata_indices])  # only use original metadata for loss
+
+        beatmap_similarity = similarity.permute(2, 0, 1)  # (beatmap_batch_size, metadata_batch_size, variations)
+        beatmap_similarity = beatmap_similarity.reshape(beatmap_batch_size, -1)  # (beatmap_batch_size, metadata_batch_size * variations)
+        target = torch.arange(0, beatmap_similarity.size(1), num_variations, device=similarity.device)  # (metadata_batch_size,)
+        target += true_metadata_indices
+        beatmap_loss = contrastive_loss(beatmap_similarity, target=target)
+    else:
+        metadata_loss = contrastive_loss(similarity)
+        beatmap_loss = contrastive_loss(similarity.t())
+    return (metadata_loss + beatmap_loss) / 2.0
+
+
+def _get_vector_norm(tensor: torch.Tensor) -> torch.Tensor:
+    """
+    This method is equivalent to tensor.norm(p=2, dim=-1, keepdim=True) and used to make
+    model `executorch` exportable. See issue https://github.com/pytorch/executorch/issues/3566
+    """
+    square_tensor = torch.pow(tensor, 2)
+    sum_tensor = torch.sum(square_tensor, dim=-1, keepdim=True)
+    normed_tensor = torch.pow(sum_tensor, 0.5)
+    return normed_tensor
+
+
+def _unpad_cm3p_input(
+    inputs: torch.Tensor,
+    attention_mask: torch.Tensor,
+    position_ids: Optional[torch.Tensor] = None,
+    labels: Optional[torch.Tensor] = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, Optional[torch.Tensor], Optional[torch.Tensor]]:
+    """
+    Remove padding from input sequences.
+
+    Args:
+        inputs: (batch, seqlen, ...) or (batch, seqlen)
+        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+        position_ids: (batch, seqlen), int, position ids
+        labels: (batch, seqlen), int, labels
+
+    Returns:
+        unpadded_inputs: (total_nnz, ...), where total_nnz = number of tokens selected in attention_mask.
+        indices: (total_nnz)
+        cu_seqlens: (batch + 1), the cumulative sequence lengths
+        max_seqlen_in_batch: int
+        unpadded_position_ids: (total_nnz) or None
+        unpadded_labels: (total_nnz) or None
+    """
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = int(seqlens_in_batch.max().item())
+    cu_seqlens = torch.nn.functional.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+
+    if inputs.dim() == 2:
+        unpadded_inputs = inputs.flatten()[indices]
+    else:
+        batch, seqlen, *rest = inputs.shape
+        shape = batch * seqlen
+        unpadded_inputs = inputs.view(shape, *rest)[indices]
+
+    unpadded_position_ids = position_ids.flatten()[indices] if position_ids is not None else None
+    unpadded_labels = labels.flatten()[indices] if labels is not None else None
+
+    return unpadded_inputs, indices, cu_seqlens, max_seqlen_in_batch, unpadded_position_ids, unpadded_labels
+
+
+def _pad_cm3p_output(
+    inputs: torch.Tensor,
+    indices: torch.Tensor,
+    batch: int,
+    seqlen: int,
+) -> torch.Tensor:
+    """
+    Add padding to sequences.
+
+    Args:
+        inputs: (total_nnz, ...) or (total_nnz,), where total_nnz = number of tokens selected in attention_mask.
+        indices: (total_nnz)
+        batch: int, batch size
+        seqlen: int, max sequence length
+
+    Returns:
+        padded_inputs: (batch, seqlen, ...) or (batch, seqlen)
+    """
+    if inputs.dim() == 1:
+        output = torch.zeros(batch * seqlen, dtype=inputs.dtype, device=inputs.device)
+        output[indices] = inputs
+        padded_inputs = output.view(batch, seqlen)
+    else:
+        _, *rest = inputs.shape
+        output = torch.zeros(batch * seqlen, *rest, dtype=inputs.dtype, device=inputs.device)
+        output[indices] = inputs
+        padded_inputs = output.view(batch, seqlen, *rest)
+
+    return padded_inputs
+
+
+@dataclass
+class BeatmapClassifierOutput(ModelOutput):
+    """
+    Base class for outputs of beatmap classification models.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
+            (also called feature maps) of the model at the output of each stage.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: Optional[torch.FloatTensor] = None
+    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for audio model's outputs that also contains a pooling of the last hidden states.
+    """
+)
+class CM3PAudioModelOutput(BaseModelOutput):
+    r"""
+    audio_embeds (`torch.FloatTensor` of shape `(batch_size * sequence_length, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
+        The audio embeddings obtained by applying the projection layer to the last hidden state.
+    """
+
+    audio_embeds: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for beatmap model's outputs that also contains beatmap embeddings of the pooling of the last hidden states.
+    """
+)
+class CM3PBeatmapModelOutput(BaseModelOutputWithPooling):
+    r"""
+    audio_model_output (`BaseModelOutput`):
+        The output of the audio model, which contains the last hidden state, hidden states, and attentions.
+    beatmap_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
+        The beatmap embeddings obtained by applying the projection layer to the pooler_output.
+    """
+
+    beatmap_embeds: Optional[torch.FloatTensor] = None
+    audio_model_output: CM3PAudioModelOutput = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for metadata model's outputs that also contains a pooling of the last hidden states.
+    """
+)
+class CM3PMetadataModelOutput(BaseModelOutput):
+    r"""
+    metadata_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
+        The metadata embeddings obtained by applying the projection layer to the pooler_output.
+    """
+
+    metadata_embeds: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring
+class CM3POutput(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
+        Contrastive loss for beatmap-metadata similarity.
+    logits_per_beatmap (`torch.FloatTensor` of shape `(beatmap_batch_size, metadata_batch_size)`):
+        The scaled dot product scores between `beatmap_embeds` and `metadata_embeds`. This represents the beatmap-metadata
+        similarity scores.
+    logits_per_metadata (`torch.FloatTensor` of shape `(metadata_batch_size, beatmap_batch_size)`):
+        The scaled dot product scores between `metadata_embeds` and `beatmap_embeds`. This represents the metadata-beatmap
+        similarity scores.
+    metadata_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
+        The metadata embeddings obtained by applying the projection layer to the pooled output of [`CM3PMetadataModel`].
+    beatmap_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
+        The beatmap embeddings obtained by applying the projection layer to the pooled output of [`CM3PBeatmapModel`].
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, vocab_size)`, *optional*, returned when `labels` is provided):
+        Prediction scores of the masked language modeling head. Only computed if `labels` is provided.
+    metadata_model_output (`BaseModelOutputWithPooling`):
+        The output of the [`CM3PMetadataModel`].
+    beatmap_model_output (`BaseModelOutputWithPooling`):
+        The output of the [`CM3PBeatmapModel`].
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits_per_beatmap: Optional[torch.FloatTensor] = None
+    logits_per_metadata: Optional[torch.FloatTensor] = None
+    metadata_embeds: Optional[torch.FloatTensor] = None
+    beatmap_embeds: Optional[torch.FloatTensor] = None
+    logits: Optional[torch.FloatTensor] = None
+    metadata_model_output: BaseModelOutputWithPooling = None
+    beatmap_model_output: BaseModelOutputWithPooling = None
+
+    def to_tuple(self) -> tuple[Any]:
+        return tuple(
+            self[k] if k not in ["metadata_model_output", "beatmap_model_output"] else getattr(self, k).to_tuple()
+            for k in self.keys()
+        )
+
+
+@auto_docstring
+class CM3PPreTrainedModel(PreTrainedModel):
+    config_class = CM3PConfig
+    base_model_prefix = "cm3p"
+    supports_gradient_checkpointing = True
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = False
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, (nn.Linear, nn.Conv1d)):
+            nn.init.normal_(module.weight, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.weight.data.fill_(1.0)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, ModernBertModel):
+            module.initialize_weights()
+        elif isinstance(module, CM3PModel):
+            nn.init.normal_(
+                module.metadata_projection.weight,
+                std=module.metadata_embed_dim**-0.5 * self.config.initializer_factor,
+            )
+            nn.init.normal_(
+                module.beatmap_projection.weight,
+                std=module.beatmap_embed_dim**-0.5 * self.config.initializer_factor,
+            )
+        elif isinstance(module, CM3PBeatmapModelWithProjection):
+            nn.init.normal_(
+                module.beatmap_projection.weight,
+                std=self.config.hidden_size**-0.5 * self.config.initializer_factor,
+            )
+        elif isinstance(module, CM3PMetadataModelWithProjection):
+            nn.init.normal_(
+                module.metadata_projection.weight,
+                std=self.config.hidden_size**-0.5 * self.config.initializer_factor,
+            )
+        elif isinstance(module, CM3PForBeatmapClassification):
+            nn.init.normal_(
+                module.classifier.weight,
+                std=self.config.hidden_size**-0.5 * self.config.initializer_factor,
+            )
+
+
+class CM3PMetadataTransformer(nn.Module):
+    def __init__(self, config: CM3PMetadataConfig):
+        super().__init__()
+        self.config = config
+        self.encoder = ModernBertModel(config)
+
+    def get_input_embeddings(self):
+        return self.encoder.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.encoder.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_pooler: bool = True,
+    ) -> BaseModelOutputWithPooling:
+        r"""
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        output_pooler (`bool`, *optional*, defaults to `True`):
+            Whether to return the pooled output of the model. The pooled output is usually the representation of
+            the first token (CLS) or the mean of the token representations.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if input_ids is None:
+            raise ValueError("You have to specify input_ids")
+
+        is_3d = input_ids.dim() == 3
+        batch_size_3d = input_ids.size(0)
+        if is_3d:
+            # flatten to 2D batch if multiple metadata variations are provided
+            input_ids = input_ids.view(-1, input_ids.size(-1))
+            if attention_mask is not None:
+                attention_mask = attention_mask.view(-1, attention_mask.size(-1))
+
+        encoder_outputs: BaseModelOutput = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = None
+
+        if is_3d:
+            # un-flatten back to 3D batch (batch_size, variations, seq_length, hidden_size)
+            last_hidden_state = last_hidden_state.view(
+                batch_size_3d, -1, last_hidden_state.size(-2), last_hidden_state.size(-1)
+            )
+            if attention_mask is not None:
+                attention_mask = attention_mask.view(batch_size_3d, -1, attention_mask.size(-1))
+
+        if output_pooler:
+            if indices is not None:
+                raise NotImplementedError("Pooling with unpadded input is not implemented yet.")
+            if self.config.cls_embed:
+                pooled_output = last_hidden_state[..., 0, :]
+            elif attention_mask is not None:
+                # Use the attention mask to exclude padding tokens
+                expanded_attention_mask = attention_mask.unsqueeze(-1).float()
+                masked_hidden_states = last_hidden_state * expanded_attention_mask
+                sum_hidden_states = torch.sum(masked_hidden_states, dim=-2)
+                sum_attention_mask = torch.sum(expanded_attention_mask, dim=-2)
+                pooled_output = sum_hidden_states / torch.clamp(sum_attention_mask, min=1e-9)
+                pooled_output = pooled_output.to(dtype=last_hidden_state.dtype)
+            else:
+                pooled_output = torch.mean(last_hidden_state, dim=-2)
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    The metadata model from CM3P without any head or projection on top.
+    """
+)
+class CM3PMetadataModel(CM3PPreTrainedModel):
+    config_class = CM3PMetadataConfig
+
+    def __init__(self, config: CM3PMetadataConfig):
+        super().__init__(config)
+        self.metadata_model = CM3PMetadataTransformer(config)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.metadata_model.encoder.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.metadata_model.encoder.embeddings.tok_embeddings = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_pooler: bool = True,
+    ) -> BaseModelOutputWithPooling:
+        r"""
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        output_pooler (`bool`, *optional*, defaults to `True`):
+            Whether to return the pooled output of the model. The pooled output is usually the representation of
+            the first token (CLS) or the mean of the token representations.
+        """
+        return self.metadata_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_pooler=output_pooler,
+        )
+
+
+class CM3PMultiModalProjector(nn.Module):
+    def __init__(self, config: CM3PAudioConfig):
+        super().__init__()
+        self.linear_1 = nn.Linear(config.projector_intermediate_size, config.projector_dim, bias=False)
+        self.act = ACT2FN[config.projector_hidden_act]
+        self.linear_2 = nn.Linear(config.projector_dim, config.projector_dim, bias=False)
+
+    def forward(self, audio_features):
+        hidden_states = self.linear_1(audio_features)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class CM3PAudioEncoder(nn.Module):
+    def __init__(self, config: CM3PAudioConfig):
+        super().__init__()
+        self.config = config
+        self.conv1 = nn.Conv1d(config.n_mels, config.hidden_size, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv1d(config.hidden_size, config.hidden_size, kernel_size=3, stride=2, padding=1)
+        self.encoder = ModernBertModel(config)
+        self.multi_modal_projector = CM3PMultiModalProjector(config)
+
+    def forward(
+            self,
+            input_features: torch.FloatTensor,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+    ) -> CM3PAudioModelOutput:
+        # Conv layers from Whisper followed by an modern Bert encoder
+        inputs_embeds = nn.functional.gelu(self.conv1(input_features))
+        inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
+
+        inputs_embeds = inputs_embeds.permute(0, 2, 1).contiguous()
+
+        position_ids = torch.arange(inputs_embeds.size(1), device=inputs_embeds.device).unsqueeze(0).repeat(
+            inputs_embeds.size(0), 1)
+
+        encoder_outputs: BaseModelOutput = self.encoder(
+            inputs_embeds=inputs_embeds,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        # Reduce the sequence length and project to the beatmap hidden size
+        audio_hidden_states = encoder_outputs.last_hidden_state
+        audio_hidden_states = audio_hidden_states.reshape(-1, self.config.projector_intermediate_size)
+        audio_embeds = self.multi_modal_projector(audio_hidden_states)
+
+        audio_outputs = CM3PAudioModelOutput(
+            audio_embeds=audio_embeds,
+            last_hidden_state=encoder_outputs.last_hidden_state,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+        return audio_outputs
+
+
+class CM3PBeatmapTransformer(nn.Module):
+    def __init__(self, config: CM3PBeatmapConfig):
+        super().__init__()
+        self.config = config
+        self.audio_encoder = CM3PAudioEncoder(config.audio_config)
+        self.encoder = ModernBertModel(config)
+
+    def get_input_embeddings(self):
+        return self.encoder.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.encoder.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        input_features: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        sliding_window_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_pooler: bool = True,
+    ) -> CM3PBeatmapModelOutput:
+        r"""
+        input_features (`torch.FloatTensor` of shape `(batch_size, num_frames, num_mels)`, *optional*):
+            The audio frames to be processed by the audio encoder. If provided, the model will use these frames to
+            compute the beatmap embeddings.
+        sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+            perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+            far-away tokens in the local attention layers when not using Flash Attention.
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        output_pooler (`bool`, *optional*, defaults to `True`):
+            Whether to return the pooled output of the model. The pooled output is usually the representation of
+            the first token (CLS) or the mean of the token representations.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        audio_model_outputs = None
+        if input_features is not None:
+            audio_model_outputs: CM3PAudioModelOutput = self.audio_encoder(
+                input_features=input_features,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            # replace text-audio token placeholders with audio embeddings
+            audio_embeds = audio_model_outputs.audio_embeds.to(dtype=inputs_embeds.dtype)
+            audio_token_mask = input_ids == self.config.audio_token_id
+            inputs_embeds[audio_token_mask] = audio_embeds
+
+        encoder_outputs: BaseModelOutput = self.encoder(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = None
+
+        if output_pooler:
+            if indices is not None:
+                if self.config.cls_embed:
+                    pooled_output = last_hidden_state[cu_seqlens[:-1]]
+                else:
+                    raise NotImplementedError("Pooling with unpadded input is not implemented yet.")
+            else:
+                if self.config.cls_embed:
+                    pooled_output = last_hidden_state[:, 0]
+                elif attention_mask is not None:
+                    # Use the attention mask to exclude padding tokens
+                    expanded_attention_mask = attention_mask.unsqueeze(-1).float()
+                    masked_hidden_states = last_hidden_state * expanded_attention_mask
+                    sum_hidden_states = torch.sum(masked_hidden_states, dim=1)
+                    sum_attention_mask = torch.sum(expanded_attention_mask, dim=1)
+                    pooled_output = sum_hidden_states / torch.clamp(sum_attention_mask, min=1e-9)
+                    pooled_output = pooled_output.to(dtype=last_hidden_state.dtype)
+                else:
+                    pooled_output = torch.mean(last_hidden_state, dim=1)
+
+        return CM3PBeatmapModelOutput(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            audio_model_output=audio_model_outputs,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    The beatmap model from CM3P without any head or projection on top.
+    """
+)
+class CM3PBeatmapModel(CM3PPreTrainedModel):
+    config_class = CM3PBeatmapConfig
+    main_input_name = "input_ids"
+
+    def __init__(self, config: CM3PBeatmapConfig):
+        super().__init__(config)
+        self.beatmap_model = CM3PBeatmapTransformer(config)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.beatmap_model.encoder.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.beatmap_model.encoder.embeddings.tok_embeddings = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        input_features: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_pooler: bool = True,
+    ) -> CM3PBeatmapModelOutput:
+        r"""
+        input_features (`torch.FloatTensor` of shape `(batch_size, num_frames, num_mels)`, *optional*):
+            The audio frames to be processed by the audio encoder. If provided, the model will use these frames to
+            compute the beatmap embeddings.
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        output_pooler (`bool`, *optional*, defaults to `True`):
+            Whether to return the pooled output of the model. The pooled output is usually the representation of
+            the first token (CLS) or the mean of the token representations.
+        """
+
+        return self.beatmap_model(
+            input_ids=input_ids,
+            input_features=input_features,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_pooler=output_pooler,
+        )
+
+
+@auto_docstring
+class CM3PModel(CM3PPreTrainedModel):
+    config_class = CM3PConfig
+
+    def __init__(self, config: CM3PConfig):
+        super().__init__(config)
+
+        if not isinstance(config.metadata_config, CM3PMetadataConfig):
+            raise TypeError(
+                "config.metadata_config is expected to be of type CM3PMetadataConfig but is of type"
+                f" {type(config.metadata_config)}."
+            )
+
+        if not isinstance(config.beatmap_config, CM3PBeatmapConfig):
+            raise TypeError(
+                "config.beatmap_config is expected to be of type CM3PBeatmapConfig but is of type"
+                f" {type(config.beatmap_config)}."
+            )
+
+        metadata_config = config.metadata_config
+        beatmap_config = config.beatmap_config
+
+        self.projection_dim = config.projection_dim
+        self.metadata_embed_dim = metadata_config.hidden_size
+        self.beatmap_embed_dim = beatmap_config.hidden_size
+        self.loss_type = config.loss_type
+
+        metadata_model = CM3PMetadataModel._from_config(metadata_config)
+        self.metadata_model = metadata_model.metadata_model
+
+        beatmap_model = CM3PBeatmapModel._from_config(beatmap_config)
+        self.beatmap_model = beatmap_model.beatmap_model
+
+        self.beatmap_projection = nn.Linear(self.beatmap_embed_dim, self.projection_dim, bias=False)
+        self.metadata_projection = nn.Linear(self.metadata_embed_dim, self.projection_dim, bias=False)
+        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
+
+        self.head = CM3PPredictionHead(beatmap_config)
+        self.decoder = nn.Linear(beatmap_config.hidden_size, beatmap_config.vocab_size, bias=beatmap_config.decoder_bias)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @auto_docstring
+    def get_metadata_features(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            The input IDs for the metadata model. The model will use these IDs to compute the metadata embeddings.
+        Returns:
+            metadata_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The metadata embeddings obtained by
+            applying the projection layer to the pooled output of [`CM3PMetadataModel`].
+        """
+        # Use CM3P model's config for some fields (if specified) instead of those of beatmap & metadata components.
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        metadata_outputs: BaseModelOutputWithPooling = self.metadata_model(
+            input_ids=input_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        pooled_output = metadata_outputs.pooler_output
+        metadata_features = self.metadata_projection(pooled_output)
+
+        return metadata_features
+
+    @auto_docstring
+    def get_beatmap_features(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        input_features: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+        r"""
+        input_features (`torch.FloatTensor` of shape `(batch_size, num_frames, num_mels)`, *optional*):
+            The audio frames to be processed by the audio encoder. If provided, the model will use these frames to
+            compute the beatmap embeddings.
+        Returns:
+            beatmap_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The beatmap embeddings obtained by
+            applying the projection layer to the pooled output of [`CM3PBeatmapModel`].
+        """
+        # Use CM3P model's config for some fields (if specified) instead of those of beatmap & metadata components.
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        beatmap_outputs: BaseModelOutputWithPooling = self.beatmap_model(
+            input_ids=input_ids,
+            input_features=input_features,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        pooled_output = beatmap_outputs.pooler_output
+        beatmap_features = self.beatmap_projection(pooled_output)
+
+        return beatmap_features
+
+    @torch.compile(dynamic=True)
+    def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
+        return self.decoder(self.head(output))
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        input_features: Optional[torch.FloatTensor] = None,
+        metadata_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        metadata_attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        metadata_variation_classes: Optional[torch.LongTensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        return_loss: Optional[bool] = True,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        **kwargs,
+    ) -> CM3POutput:
+        r"""
+        input_features (`torch.FloatTensor` of shape `(batch_size, num_frames, num_mels)`, *optional*):
+            The audio frames to be processed by the audio encoder. If provided, the model will use these frames to
+            compute the beatmap embeddings.
+        metadata_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)` or `(batch_size, variations, sequence_length)`):
+            The input IDs for the metadata model. The model will use these IDs to compute the metadata embeddings.
+        metadata_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)` or `(batch_size, variations, sequence_length)`, *optional*):
+            The attention mask for the metadata model. If provided, the model will not attend to the padded tokens.
+        metadata_variation_classes (`torch.LongTensor` of shape `(batch_size, variations)`, *optional*):
+            Tells the model what kind of variation each metadata sequence is.
+            0 indicates the original metadata, -1 indicates paddidng, and any positive integer indicates a specific variation class.
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        return_loss (`bool`, *optional*):
+            Whether to return the contrastive loss.
+        """
+        # Use CM3P model's config for some fields (if specified) instead of those of beatmap & metadata components.
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if metadata_ids.dim() == 3 and return_loss and metadata_variation_classes is None:
+            raise ValueError("When providing multiple metadata variations, metadata_variation_classes must be provided in order to compute loss correctly.")
+
+        # noinspection PyProtectedMember
+        if self.config._attn_implementation == "flash_attention_2":
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                if batch_size is None and seq_len is None:
+                    if inputs_embeds is not None:
+                        batch_size, seq_len = inputs_embeds.shape[:2]
+                    else:
+                        batch_size, seq_len = input_ids.shape[:2]
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+                if attention_mask is None:
+                    attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_cm3p_input(
+                            inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_cm3p_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                    )
+
+        beatmap_outputs: BaseModelOutputWithPooling = self.beatmap_model(
+            input_ids=input_ids,
+            input_features=input_features,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        metadata_outputs: BaseModelOutputWithPooling = self.metadata_model(
+            input_ids=metadata_ids,
+            attention_mask=metadata_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        beatmap_embeds = beatmap_outputs.pooler_output
+        beatmap_embeds = self.beatmap_projection(beatmap_embeds)
+
+        metadata_embeds = metadata_outputs.pooler_output
+        metadata_embeds = self.metadata_projection(metadata_embeds)
+
+        # normalized features
+        beatmap_embeds = beatmap_embeds / _get_vector_norm(beatmap_embeds)
+        metadata_embeds = metadata_embeds / _get_vector_norm(metadata_embeds)
+
+        # cosine similarity as logits
+        logits_per_metadata = torch.matmul(metadata_embeds, beatmap_embeds.t().to(metadata_embeds.device))
+        logits_per_metadata = logits_per_metadata * self.logit_scale.exp().to(metadata_embeds.device)
+
+        if logits_per_metadata.dim() == 3:
+            logits_per_beatmap = logits_per_metadata.permute(2, 0, 1)
+        else:
+            logits_per_beatmap = logits_per_metadata.t()
+
+        loss = None
+        if return_loss:
+            loss = cm3p_loss(logits_per_metadata, metadata_variation_classes)
+
+        logits = (
+            self.compiled_head(beatmap_outputs.last_hidden_state)
+            if self.config.beatmap_config.reference_compile
+            else self.decoder(self.head(beatmap_outputs.last_hidden_state))
+        )
+
+        if labels is not None and return_loss:
+            mlm_loss = self.loss_function(logits, labels, vocab_size=self.config.beatmap_config.vocab_size, **kwargs)
+            loss += 0.5 * mlm_loss
+
+        # noinspection PyProtectedMember
+        if self.config._attn_implementation == "flash_attention_2":
+            with nullcontext() if self.config.beatmap_config.repad_logits_with_grad or labels is None else torch.no_grad():
+                logits = _pad_cm3p_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)
+
+        return CM3POutput(
+            loss=loss,
+            logits_per_beatmap=logits_per_beatmap,
+            logits_per_metadata=logits_per_metadata,
+            metadata_embeds=metadata_embeds,
+            beatmap_embeds=beatmap_embeds,
+            logits=logits,
+            metadata_model_output=metadata_outputs,
+            beatmap_model_output=beatmap_outputs,
+        )
+
+
+@auto_docstring
+class CM3PMetadataModelWithProjection(CM3PPreTrainedModel):
+    config_class = CM3PMetadataConfig
+
+    def __init__(self, config: CM3PMetadataConfig):
+        super().__init__(config)
+
+        metadata_model = CM3PMetadataModel._from_config(config)
+        self.metadata_model = metadata_model.metadata_model
+
+        self.metadata_projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.metadata_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.metadata_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> CM3PMetadataModelOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            The input IDs for the metadata model. The model will use these IDs to compute the metadata embeddings.
+        Returns:
+            metadata_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The metadata embeddings obtained by
+            applying the projection layer to the pooled output of [`CM3PMetadataModel`].
+        """
+        metadata_outputs: BaseModelOutputWithPooling = self.metadata_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        pooled_output = metadata_outputs.pooler_output
+        metadata_embeds = self.metadata_projection(pooled_output)
+
+        return CM3PMetadataModelOutput(
+            metadata_embeds=metadata_embeds,
+            last_hidden_state=metadata_outputs.last_hidden_state,
+            hidden_states=metadata_outputs.hidden_states,
+            attentions=metadata_outputs.attentions,
+        )
+
+
+@auto_docstring
+class CM3PBeatmapModelWithProjection(CM3PPreTrainedModel):
+    config_class = CM3PBeatmapConfig
+
+    def __init__(self, config: CM3PBeatmapConfig):
+        super().__init__(config)
+
+        beatmap_model = CM3PBeatmapModel._from_config(config)
+        self.beatmap_model = beatmap_model.beatmap_model
+
+        self.beatmap_projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.beatmap_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.beatmap_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        input_features: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> CM3PBeatmapModelOutput:
+        r"""
+        input_features (`torch.FloatTensor` of shape `(batch_size, num_frames, num_mels)`, *optional*):
+            The audio frames to be processed by the audio encoder. If provided, the model will use these frames to
+            compute the beatmap embeddings.
+        Returns:
+            beatmap_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The beatmap embeddings obtained by
+            applying the projection layer to the pooled output of [`CM3PBeatmapModel`].
+        """
+        beatmap_outputs: BaseModelOutputWithPooling = self.beatmap_model(
+            input_ids=input_ids,
+            input_features=input_features,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        pooled_output = beatmap_outputs.pooler_output
+        beatmap_embeds = self.beatmap_projection(pooled_output)
+
+        return CM3PBeatmapModelOutput(
+            beatmap_embeds=beatmap_embeds,
+            pooler_output=pooled_output,
+            last_hidden_state=beatmap_outputs.last_hidden_state,
+            hidden_states=beatmap_outputs.hidden_states,
+            attentions=beatmap_outputs.attentions,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    CM3P beatmap encoder with an beatmap classification head on top (a linear layer on top of the pooled final hidden states of
+    the beatmap embeddings) e.g. for BeatmapNet.
+    """
+)
+class CM3PForBeatmapClassification(CM3PPreTrainedModel):
+    config_class = CM3PBeatmapConfig
+    base_model_prefix = "beatmap_model"
+
+    def __init__(self, config: CM3PBeatmapConfig) -> None:
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        beatmap_model = CM3PBeatmapModel._from_config(config)
+        self.beatmap_model = beatmap_model.beatmap_model
+
+        # Classifier head
+        self.classifier = (
+            nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        input_features: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> BeatmapClassifierOutput:
+        r"""
+        input_features (`torch.FloatTensor` of shape `(batch_size, num_frames, num_mels)`, *optional*):
+            The audio frames to be processed by the audio encoder. If provided, the model will use these frames to
+            compute the beatmap embeddings.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the beatmap classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        outputs: BaseModelOutputWithPooling = self.beatmap_model(
+            input_ids=input_ids,
+            input_features=input_features,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        pooled_output = outputs.pooler_output
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        return BeatmapClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class CM3PPredictionHead(nn.Module):
+    def __init__(self, config: CM3PBeatmapConfig):
+        super().__init__()
+        self.config = config
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size, config.classifier_bias)
+        self.act = ACT2FN[config.classifier_activation]
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.norm(self.act(self.dense(hidden_states)))
+
+
+class CM3PForMaskedLM(CM3PPreTrainedModel):
+    config_class = CM3PBeatmapConfig
+    base_model_prefix = "beatmap_model"
+    _tied_weights_keys = ["decoder.weight"]
+
+    def __init__(self, config: CM3PBeatmapConfig):
+        super().__init__(config)
+        self.config = config
+        beatmap_model = CM3PBeatmapModel._from_config(config)
+        self.beatmap_model = beatmap_model.beatmap_model
+        self.head = CM3PPredictionHead(config)
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
+
+        self.sparse_prediction = self.config.sparse_prediction
+        self.sparse_pred_ignore_index = self.config.sparse_pred_ignore_index
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.decoder
+
+    def set_output_embeddings(self, new_embeddings: nn.Linear):
+        self.decoder = new_embeddings
+
+    @torch.compile(dynamic=True)
+    def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
+        return self.decoder(self.head(output))
+
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        input_features: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        sliding_window_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+        batch_size: Optional[int] = None,
+        seq_len: Optional[int] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[tuple[torch.Tensor], MaskedLMOutput]:
+        r"""
+        input_features (`torch.FloatTensor` of shape `(batch_size, num_frames, num_mels)`, *optional*):
+            The audio frames to be processed by the audio encoder. If provided, the model will use these frames to
+            compute the beatmap embeddings.
+        sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+            perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+            far-away tokens in the local attention layers when not using Flash Attention.
+        indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+            Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+        cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+            Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+        max_seqlen (`int`, *optional*):
+            Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+        batch_size (`int`, *optional*):
+            Batch size of the input sequences. Used to pad the output tensors.
+        seq_len (`int`, *optional*):
+            Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+        """
+        # noinspection PyProtectedMember
+        if self.config._attn_implementation == "flash_attention_2":
+            if indices is None and cu_seqlens is None and max_seqlen is None:
+                if batch_size is None and seq_len is None:
+                    if inputs_embeds is not None:
+                        batch_size, seq_len = inputs_embeds.shape[:2]
+                    else:
+                        batch_size, seq_len = input_ids.shape[:2]
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+                if attention_mask is None:
+                    attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+                if inputs_embeds is None:
+                    with torch.no_grad():
+                        input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_cm3p_input(
+                            inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                        )
+                else:
+                    inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_cm3p_input(
+                        inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+                    )
+
+        outputs = self.beatmap_model(
+            input_ids=input_ids,
+            input_features=input_features,
+            attention_mask=attention_mask,
+            sliding_window_mask=sliding_window_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            indices=indices,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            batch_size=batch_size,
+            seq_len=seq_len,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_pooler=False,
+        )
+        last_hidden_state = outputs.last_hidden_state
+
+        if self.sparse_prediction and labels is not None:
+            # flatten labels and output first
+            labels = labels.view(-1)
+            last_hidden_state = last_hidden_state.view(labels.shape[0], -1)
+
+            # then filter out the non-masked tokens
+            mask_tokens = labels != self.sparse_pred_ignore_index
+            last_hidden_state = last_hidden_state[mask_tokens]
+            labels = labels[mask_tokens]
+
+        logits = (
+            self.compiled_head(last_hidden_state)
+            if self.config.reference_compile
+            else self.decoder(self.head(last_hidden_state))
+        )
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        # noinspection PyProtectedMember
+        if self.config._attn_implementation == "flash_attention_2":
+            with nullcontext() if self.config.repad_logits_with_grad or labels is None else torch.no_grad():
+                logits = _pad_cm3p_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+AutoModel.register(CM3PMetadataConfig, CM3PMetadataModel)
+AutoModel.register(CM3PBeatmapConfig, CM3PBeatmapModel)
+AutoModel.register(CM3PConfig, CM3PModel)
+AutoModelForSequenceClassification.register(CM3PBeatmapConfig, CM3PForBeatmapClassification)
+AutoModelForMaskedLM.register(CM3PBeatmapConfig, CM3PForMaskedLM)
+
+__all__ = [
+    "CM3PModel",
+    "CM3PPreTrainedModel",
+    "CM3PMetadataModel",
+    "CM3PMetadataModelWithProjection",
+    "CM3PBeatmapModel",
+    "CM3PBeatmapModelWithProjection",
+    "CM3PForBeatmapClassification",
+]