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# Copyright 2025 Sesame 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.

from collections.abc import Callable
from dataclasses import dataclass
from typing import Optional

import torch
import torch.nn as nn

from transformers.utils.generic import check_model_inputs

from ... import initialization as init
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
from ...masking_utils import create_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.generic import maybe_autocast
from ...utils.import_utils import is_torchdynamo_compiling
from ..auto import AutoModel
from .configuration_csm import CsmConfig, CsmDepthDecoderConfig
from .generation_csm import CsmGenerationMixin


logger = logging.get_logger(__name__)


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for the model autoregressive outputs.
    """
)
class CsmOutputWithPast(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Language modeling loss (for next-token prediction).
    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
        `past_key_values` input) to speed up sequential decoding.
    depth_decoder_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Language modeling loss (for next-token prediction) of the depth decoder model.
    depth_decoder_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
        Prediction scores of the depth decoder (scores for each vocabulary token before SoftMax).
    depth_decoder_past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
    depth_decoder_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 layer) of shape `(batch_size, sequence_length, hidden_size)`.

        Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
    depth_decoder_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, sequence_length,
        sequence_length)`.
    backbone_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Language modeling loss (for next-token prediction) of the backbone model.
    """

    loss: torch.FloatTensor | None = None
    logits: torch.FloatTensor | None = None
    past_key_values: Cache | None = None
    hidden_states: tuple[torch.FloatTensor, ...] | None = None
    attentions: tuple[torch.FloatTensor, ...] | None = None
    depth_decoder_loss: torch.FloatTensor | None = None
    depth_decoder_logits: torch.FloatTensor | None = None
    depth_decoder_past_key_values: Cache | None = None
    depth_decoder_hidden_states: tuple[torch.FloatTensor, ...] | None = None
    depth_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None
    backbone_loss: torch.FloatTensor | None = None


@use_kernel_forward_from_hub("RMSNorm")
class CsmRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        CsmRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class CsmRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: CsmConfig, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)

    @staticmethod
    def compute_default_rope_parameters(
        config: CsmConfig | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


class CsmMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


@use_kernel_func_from_hub("rotary_pos_emb")
def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


@use_kernelized_func(apply_rotary_pos_emb)
class CsmAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: CsmConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_mask: torch.Tensor | None = None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_values is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class CsmDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: CsmConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        self.self_attn = CsmAttention(config=config, layer_idx=layer_idx)

        self.mlp = CsmMLP(config)
        self.input_layernorm = CsmRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = CsmRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        use_cache: bool | None = False,
        cache_position: torch.LongTensor | None = None,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states
        return hidden_states


@auto_docstring(
    custom_intro="""
    The bare Csm Model outputting raw hidden-states without any specific head on top.
    """
)
@auto_docstring
class CsmPreTrainedModel(PreTrainedModel):
    config: CsmConfig
    base_model_prefix = "model"
    input_modalities = ("audio", "text")
    supports_gradient_checkpointing = True
    _no_split_modules = ["CsmDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn = True
    _supports_sdpa = True
    # does not because of Mimi codec model
    # _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": CsmDecoderLayer,
        "attentions": CsmAttention,
    }

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if isinstance(module, CsmCodebooksHead):
            num_codebooks = module.num_codebooks
            for i in range(num_codebooks - 1):
                init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, CsmBackboneModelEmbeddings):
            init.copy_(module.audio_tokens_offsets, torch.arange(self.config.num_codebooks) * self.config.vocab_size)


@auto_docstring
class CsmDepthDecoderModel(CsmPreTrainedModel):
    config: CsmDepthDecoderConfig

    def __init__(self, config):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding((config.num_codebooks * config.vocab_size), config.backbone_hidden_size)
        self.layers = nn.ModuleList(
            [CsmDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = CsmRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = CsmRotaryEmbedding(config=config)
        self.gradient_checkpointing = False
        self.inputs_embeds_projector = nn.Linear(config.backbone_hidden_size, config.hidden_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    @check_model_inputs
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        backbone_last_hidden_state: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPast:
        r"""
        backbone_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, backbone_hidden_size)`, *optional*):
            The last hidden state of the backbone model. Such input is required when the first codebook token (the one generated by the backbone model)
            is provided in the `input_ids` argument.
        """
        if position_ids is not None and not is_torchdynamo_compiling():
            logger.warning_once(
                "Custom `position_ids` were provided but will be ignored. CSM depth decoder automatically determines position_ids "
                "from `cache_position` and as it requires them to be identical across the batch, the provided position_ids will be ignored."
            )
            position_ids = None
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds.")

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            inputs_seq_length = inputs_embeds.shape[1] if inputs_embeds is not None else input_ids.shape[1]
            device = inputs_embeds.device if inputs_embeds is not None else input_ids.device
            cache_position = torch.arange(past_seen_tokens, past_seen_tokens + inputs_seq_length, device=device)

        if inputs_embeds is None:
            codebook_idxs = torch.clamp(cache_position - 1, min=0)
            offset = codebook_idxs * self.vocab_size
            inputs_embeds = self.embed_tokens(input_ids + offset)

            input_ids_are_first_codebook = cache_position[0] == 0
            if backbone_last_hidden_state is not None:
                inputs_embeds[:, 0] = backbone_last_hidden_state
            else:
                if not is_torchdynamo_compiling() and input_ids_are_first_codebook:
                    logger.warning(
                        "When the first codebook token is provided, `backbone_last_hidden_state` should also be provided for correct inference."
                    )

        inputs_embeds = self.inputs_embeds_projector(inputs_embeds)

        causal_mask = create_causal_mask(
            config=self.config,
            input_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_ids = cache_position.unsqueeze(0)
        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
        )


class CsmCodebooksHead(nn.Module):
    def __init__(self, hidden_size, num_codebooks, vocab_size):
        super().__init__()
        self.num_codebooks = num_codebooks
        self.weight = nn.Parameter(torch.empty(self.num_codebooks - 1, hidden_size, vocab_size))

    def forward(self, hidden_states, cache_position=None):
        if cache_position is None:
            seq_length = hidden_states.shape[1]
            codebook_weight = self.weight[torch.arange(seq_length)]
        else:
            codebook_idxs = cache_position - 1
            codebook_weight = self.weight[codebook_idxs]

        hidden_states = [
            nn.functional.linear(hidden_states[:, codebook_idx, :], codebook_weight[codebook_idx].T)
            for codebook_idx in range(codebook_weight.shape[0])
        ]
        hidden_states = torch.stack(hidden_states, dim=1)

        return hidden_states


@auto_docstring(
    custom_intro="""
    The CsmDepthDecoder Model transformer, with a [`CsmCodebooksHead`] on top,
    which can be seen a position-specific language modeling head, allowing to use a different linear layer for each codebook
    (e.g. position 0 is the first codebook and uses the first codebook head, etc.)
    """
)
class CsmDepthDecoderForCausalLM(CsmPreTrainedModel, GenerationMixin):
    _tied_weights_keys = None
    _tp_plan = None
    _pp_plan = None

    def __init__(self, config):
        super().__init__(config)
        self.model = CsmDepthDecoderModel(config)
        self.vocab_size = config.vocab_size
        self.codebooks_head = CsmCodebooksHead(config.hidden_size, config.num_codebooks, config.vocab_size)

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        backbone_last_hidden_state: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | CausalLMOutputWithPast:
        r"""
        backbone_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, backbone_hidden_size)`, *optional*):
            The last hidden state of the backbone model. Such input is required when the first codebook token (the one generated by the backbone model)
            is provided in the `input_ids` argument.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
        """
        outputs = self.model(
            input_ids=input_ids,
            backbone_last_hidden_state=backbone_last_hidden_state,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        if isinstance(logits_to_keep, int):
            if logits_to_keep == 0:
                # skip idx 0 logits since it's for the concatenated backbone last hidden state
                slice_indices = slice(1, None)
            else:
                slice_indices = slice(-logits_to_keep, None)
        else:
            slice_indices = logits_to_keep

        logits = self.codebooks_head(
            hidden_states[:, slice_indices, :], cache_position[slice_indices] if cache_position is not None else None
        )
        logits = logits.contiguous()

        loss = None
        if labels is not None:
            shift_labels = labels[..., 1:].contiguous()
            loss = self.loss_function(
                logits=logits, labels=None, vocab_size=self.config.vocab_size, shift_labels=shift_labels, **kwargs
            )

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor,
        past_key_values: Cache | None = None,
        attention_mask: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs,
    ):
        model_inputs = super().prepare_inputs_for_generation(
            input_ids, past_key_values, attention_mask, inputs_embeds, cache_position, **kwargs
        )

        is_first_generation_step = model_inputs["cache_position"][0] == 0
        if not is_first_generation_step:
            model_inputs.pop("backbone_last_hidden_state")

        # csm depth decoder does not use position_ids
        model_inputs.pop("position_ids")

        return model_inputs


class CsmBackboneModelEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.embed_audio_tokens = nn.Embedding((config.num_codebooks * config.vocab_size), config.hidden_size)
        self.register_buffer(
            "audio_tokens_offsets", torch.arange(config.num_codebooks) * config.vocab_size, persistent=False
        )

    def forward(self, input_ids):
        input_embeds = self.embed_audio_tokens(input_ids + self.audio_tokens_offsets)
        input_embeds = input_embeds.sum(dim=2)
        return input_embeds


@auto_docstring
class CsmBackboneModel(CsmPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = CsmBackboneModelEmbeddings(config)
        self.layers = nn.ModuleList(
            [CsmDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = CsmRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = CsmRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    @check_model_inputs
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        cache_position: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks) or (batch_size, sequence_length)`):
            1. (batch_size, sequence_length): corresponds to the input sequence prepared with the processor from the text prompt. Such input
            requires `input_values` to be provided so that audio can be encoded in codebook tokens and then merged with the text tokens.

            2. (batch_size, sequence_length, num_codebooks): codebook tokens generated during the autoregressive decoding. Such input is not meant to be used by end users.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        """
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position: torch.Tensor = (
                torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = create_causal_mask(
            config=self.config,
            input_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_embeddings=position_embeddings,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


@auto_docstring(
    custom_intro="""
    The Csm model consists of two llama-like auto-regressive transformer models: a backbone model that predicts the first codebook token and a depth decoder that predicts the other codebook tokens.
    """
)
class CsmForConditionalGeneration(CsmPreTrainedModel, CsmGenerationMixin):
    _tied_weights_keys = {
        "backbone_model.embed_tokens.embed_audio_tokens.weight": "depth_decoder.model.embed_tokens.weight"
    }

    def __init__(self, config):
        super().__init__(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.embed_text_tokens = nn.Embedding(config.text_vocab_size, config.hidden_size)
        self.backbone_model = CsmBackboneModel._from_config(config)
        self.depth_decoder = CsmDepthDecoderForCausalLM._from_config(config.depth_decoder_config)
        self.codec_model = AutoModel.from_config(config.codec_config)
        self.post_init()

    def get_input_embeddings(self):
        return self.backbone_model.embed_tokens

    def set_input_embeddings(self, value):
        self.backbone_model.embed_tokens = value

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        if kwargs.get("output_loading_info", False):
            model, loading_info = super().from_pretrained(*args, **kwargs)
        else:
            model = super().from_pretrained(*args, **kwargs)

        # copy depth decoder generation conf attr to the depth decoder generation config
        prefix = "depth_decoder_"
        prefix_len = len(prefix)
        depth_decoder_attrs = {
            attr[prefix_len:]: value
            for attr, value in vars(model.generation_config).items()
            if attr.startswith(prefix)
        }

        vars(model.depth_decoder.generation_config).update({"_from_model_config": False, **depth_decoder_attrs})

        # remove the depth decoder generation conf attr from the model generation config
        for attr in depth_decoder_attrs:
            delattr(model.generation_config, prefix + attr)

        if "output_loading_info" in kwargs:
            return model, loading_info
        else:
            return model

    def save_pretrained(self, *args, **kwargs):
        # copy the depth decoder generation config attributes to the model generation config
        prefix = "depth_decoder_"
        depth_decoder_attrs = self.depth_decoder.generation_config.to_diff_dict()
        depth_decoder_attrs.pop("transformers_version", None)
        for attr, value in depth_decoder_attrs.items():
            setattr(self.generation_config, prefix + attr, value)

        super().save_pretrained(*args, **kwargs)

    def _merge_input_ids_with_input_values(
        self,
        input_ids: torch.Tensor | None = None,
        input_values: torch.Tensor | None = None,
        input_values_cutoffs: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
    ) -> torch.Tensor | None:
        """
        Merges the input_ids and input_values to produce a single inputs_embeds tensor:
        1 - Infers the codec model on the input_values to retrieve codebook token.
        2 - Embeds codebook tokens and places them at the correct positions in the inputs_embeds tensor.
        3 - If labels are provided, expands them to match codebook dimensions and position the target codebook tokens in the inputs_embeds tensor.

        Args:
            input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`):
                The input ids to embed.
            input_values (`torch.Tensor` of shape `(batch_size, channels, audio_sequence_length)`):
                The audio input values to embed.
            input_values_cutoffs (`torch.Tensor` of shape `(batch_size, max_num_audio)`):
                The cutoffs of the audio input values relative to its batch index, padded with -1 when no audio.
        """
        inputs_embeds = self.embed_text_tokens(input_ids)

        if input_values is not None:
            # infer input_values_mask
            input_values_cutoffs = nn.functional.pad(input_values_cutoffs, (1, 0))
            audio_lengths = input_values_cutoffs[input_values_cutoffs >= 0].diff()
            audio_lengths = audio_lengths[audio_lengths > 0]
            input_values_mask = torch.arange(input_values_cutoffs.max(), device=input_values.device).expand(
                len(audio_lengths), -1
            )
            input_values_mask = input_values_mask < audio_lengths.unsqueeze(1)

            # =======================================
            # TODO: @eustlb, this should be batched !!!
            # but requires making sure batched inference of the codec model works as intended
            with torch.no_grad():
                audio_tokens_list = []
                for batch_input_values, batch_input_values_cutoffs in zip(input_values, input_values_cutoffs):
                    batch_input_values_cutoffs = batch_input_values_cutoffs[batch_input_values_cutoffs >= 0]
                    for i in range(batch_input_values_cutoffs.shape[0] - 1):
                        start_idx = batch_input_values_cutoffs[i]
                        end_idx = batch_input_values_cutoffs[i + 1]
                        audio_batch = batch_input_values[..., start_idx:end_idx]
                        codec_outputs = self.codec_model.encode(audio_batch.unsqueeze(0))
                        codebook_ids = codec_outputs.audio_codes.transpose(1, -1)
                        audio_tokens_list.append(codebook_ids[0])

                max_audio_frames = max(el.shape[0] for el in audio_tokens_list)
                batched_audio_token_ids = torch.stack(
                    [nn.functional.pad(el, (0, 0, 0, max_audio_frames - el.shape[0])) for el in audio_tokens_list]
                )
                audio_codes_mask = self.codec_model.get_audio_codes_mask(input_values_mask)
            # =======================================
            audio_token_id = self.config.audio_token_id
            audio_token_mask = input_ids == audio_token_id

            audio_embeds = self.backbone_model.embed_tokens(batched_audio_token_ids)
            inputs_embeds[audio_token_mask] = audio_embeds[audio_codes_mask]

            # same for the audio eos token
            audio_eos_frame_ids = (
                torch.ones((1, 1, self.config.num_codebooks), device=input_ids.device, dtype=torch.long)
                * self.config.codebook_eos_token_id
            )
            audio_eos_embeds = self.backbone_model.embed_tokens(audio_eos_frame_ids).squeeze(1)

            audio_eos_token_mask = input_ids == self.config.audio_eos_token_id
            inputs_embeds[audio_eos_token_mask] = audio_eos_embeds.repeat(audio_eos_token_mask.sum(), 1)

            # if the labels are provided, we need to expand the labels to (batch_size, seq_length, num_codebooks)
            if labels is not None:
                labels_expanded = labels.unsqueeze(-1).repeat(1, 1, self.config.num_codebooks)
                labels_expanded[audio_token_mask] = batched_audio_token_ids[audio_codes_mask]
                labels_expanded[audio_eos_token_mask] = audio_eos_frame_ids
                # mask depth decoder
                depth_decoder_ignore_frames_idxs = (labels == -101).nonzero(as_tuple=True)
                labels_expanded[depth_decoder_ignore_frames_idxs[0], depth_decoder_ignore_frames_idxs[1], 1:] = -100
                labels = labels_expanded

        return {"inputs_embeds": inputs_embeds, "labels": labels}

    def prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor,
        past_key_values: Cache | None = None,
        attention_mask: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs,
    ):
        model_inputs = super().prepare_inputs_for_generation(
            input_ids=input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            cache_position=cache_position,
            **kwargs,
        )

        if input_ids is not None and input_ids.ndim == 2 and model_inputs.get("inputs_embeds") is None:
            merged_inputs = self._merge_input_ids_with_input_values(
                input_ids=input_ids,
                input_values=kwargs.get("input_values"),
                input_values_cutoffs=kwargs.get("input_values_cutoffs"),
                labels=kwargs.get("labels"),
            )
            model_inputs.update(
                {"inputs_embeds": merged_inputs["inputs_embeds"], "labels": merged_inputs["labels"], "input_ids": None}
            )

        return model_inputs

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        input_values: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        input_values_cutoffs: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | CsmOutputWithPast:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks) or (batch_size, sequence_length)`):
            1. (batch_size, sequence_length): corresponds to the input sequence prepared with the processor from the text prompt. Such input
            requires `input_values` to be provided so that audio can be encoded in codebook tokens and then merged with the text tokens.

            2. (batch_size, sequence_length, num_codebooks): codebook tokens generated during the autoregressive decoding. Such input is not meant to be used by end users.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        input_values_cutoffs (`torch.Tensor` of shape `(batch_size, max_num_audio)`, *optional*):
            Specify the end positions of audio segments within each batch entry, relative to the concatenated audio input.
            If a batch entry has fewer segments than the maximum, it is padded with -1. For example, in a batch of 2 sequences
            where the first contains 2 audio segments of length l1, and the second contains 1 audio segment of length l2,
            the input_values_cutoffs would be: [[l1, 2 * l1], [l2, -1]].
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[config.audio_token_id, -100, -101]`.
            Requires targeted `input_values` to be provided as audio tokens will be inferred from it using the `codec_model`.
            - `config.audio_token_id` indicates an audio frames (considering sequence length elements as frames)
            - `-100` will be ignored in the loss computation
            - `-101` indicates the audio frame will be used only for the backbone model (using the first codebook token as labels)

            Such labels can be prepared using `output_labels=True` when calling [`CsmProcessor`].
        logits_to_keep (`int` or `torch.Tensor`, *optional*):
            Kept for compatibility. Does not support another value than:
            1. `0`, which is equivalent to keeping all logits, used in the training regime
            2. `1`, which is equivalent to keeping only the last logit, used in the generation regime

        Example:

        ```python
        >>> import torch
        >>> from transformers import CsmForConditionalGeneration, AutoProcessor
        >>> from datasets import load_dataset, Audio

        >>> model_id = "sesame/csm-1b"
        >>> torch_device = "cuda" if torch.cuda.is_available() else "cpu"

        >>> processor = AutoProcessor.from_pretrained(model_id)

        >>> ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
        >>> # ensure the audio is 24kHz
        >>> ds = ds.cast_column("audio", Audio(sampling_rate=24000))

        >>> conversation = []
        >>> # prepare a conversation with text and corresponding audio
        >>> for text, audio, speaker_id in zip(ds[:4]["text"], ds[:4]["audio"], ds[:4]["speaker_id"]):
        ...     conversation.append(
        ...         {
        ...             "role": f"{speaker_id}",
        ...             "content": [{"type": "text", "text": text}, {"type": "audio", "path": audio["array"]}],
        ...         }
        ...     )

        >>> inputs = processor.apply_chat_template(
        ...     conversation,
        ...     tokenize=True,
        ...     return_dict=True,
        ...     output_labels=True,
        ... ).to(torch_device)

        >>> model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
        >>> output = model(**inputs)
        >>> output.loss.backward()
        ```"""
        if input_ids is not None and input_ids.ndim == 2:
            merged_inputs = self._merge_input_ids_with_input_values(
                input_ids, input_values, input_values_cutoffs, labels
            )
            inputs_embeds = merged_inputs["inputs_embeds"]
            labels = merged_inputs["labels"]
            input_ids = None

        backbone_outputs = self.backbone_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        backbone_hidden_states = backbone_outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        backbone_logits = self.lm_head(backbone_hidden_states[:, slice_indices, :])

        loss = None
        backbone_loss = None
        depth_decoder_loss = None
        depth_decoder_outputs = None
        if labels is not None:
            # select first codebook as labels for the backbone model
            backbone_labels = labels[:, :, 0]
            backbone_loss = self.loss_function(
                logits=backbone_logits, labels=backbone_labels, vocab_size=self.config.vocab_size, **kwargs
            )

            # for the depth decoder, we need to select the frames to train on
            # those are frames where the label is not uniformly `ignore_index` along the codebook dimension
            train_mask = ~(labels[:, :, 1:] == -100).all(dim=-1)
            depth_decoder_input_ids = labels[train_mask][..., : self.config.num_codebooks - 1]
            # add place holder in position 0 that will be replaced by the backbone_last_hidden_state
            depth_decoder_input_ids = nn.functional.pad(depth_decoder_input_ids, (1, 0), value=0)

            train_idxs = train_mask.nonzero(as_tuple=True)
            backbone_last_hidden_states = backbone_hidden_states[train_idxs[0], train_idxs[1] - 1, :]
            depth_decoder_labels = labels[train_mask]

            depth_decoder_outputs = self.depth_decoder(
                input_ids=depth_decoder_input_ids,
                backbone_last_hidden_state=backbone_last_hidden_states,
                use_cache=use_cache,
                return_dict=True,
                labels=depth_decoder_labels,
                **kwargs,
            )

            depth_decoder_loss = depth_decoder_outputs.loss
            loss = backbone_loss + depth_decoder_loss

        return CsmOutputWithPast(
            loss=loss,
            backbone_loss=backbone_loss,
            depth_decoder_loss=depth_decoder_loss,
            logits=backbone_logits,
            past_key_values=backbone_outputs.past_key_values,
            hidden_states=backbone_outputs.hidden_states,
            attentions=backbone_outputs.attentions,
            depth_decoder_logits=depth_decoder_outputs.logits if depth_decoder_outputs is not None else None,
            depth_decoder_past_key_values=depth_decoder_outputs.past_key_values
            if depth_decoder_outputs is not None
            else None,
            depth_decoder_hidden_states=depth_decoder_outputs.hidden_states
            if depth_decoder_outputs is not None
            else None,
            depth_decoder_attentions=depth_decoder_outputs.attentions if depth_decoder_outputs is not None else None,
        )


__all__ = [
    "CsmPreTrainedModel",
    "CsmBackboneModel",
    "CsmDepthDecoderModel",
    "CsmDepthDecoderForCausalLM",
    "CsmForConditionalGeneration",
]
