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edit//Qwen3-TTS-test//qwen_tts//core//tokenizer_12hz//modeling_qwen3_tts_tokenizer_v2.py

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+# coding=utf-8
+# Copyright 2026 The Qwen team, Alibaba Group 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.
+"""PyTorch Qwen3TTSTokenizerV2 model."""
+
+import math
+from dataclasses import dataclass
+from typing import Callable, Optional, Union, List
+
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import Parameter
+from torch.nn import functional as F
+from transformers import MimiConfig, MimiModel
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.masking_utils import (
+    create_causal_mask,
+    create_sliding_window_causal_mask,
+)
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import ModelOutput, auto_docstring, logging
+from transformers.utils.deprecation import deprecate_kwarg
+from transformers.utils.generic import check_model_inputs
+
+from .configuration_qwen3_tts_tokenizer_v2 import (
+    Qwen3TTSTokenizerV2Config,
+    Qwen3TTSTokenizerV2DecoderConfig,
+)
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring
+class Qwen3TTSTokenizerV2EncoderOutput(ModelOutput):
+    r"""
+    audio_codes (`List[torch.LongTensor]`):
+        Discret code embeddings computed using `model.encode`, each tensor has shape (codes_length_i, num_quantizers).
+    """
+
+    audio_codes: List[torch.LongTensor] = None
+
+
+@dataclass
+@auto_docstring
+class Qwen3TTSTokenizerV2DecoderOutput(ModelOutput):
+    r"""
+    audio_values (`List[torch.FloatTensor]`):
+        Decoded audio values, obtained using the decoder part of Qwen3TTSTokenizerV1.
+        Each tensor has shape (segment_length_i).
+    """
+
+    audio_values: List[torch.FloatTensor] = None
+
+
+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)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        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: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    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
+
+
+@auto_docstring
+class Qwen3TTSTokenizerV2DecoderPreTrainedModel(PreTrainedModel):
+    config: Qwen3TTSTokenizerV2DecoderConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _can_compile_fullgraph = False
+    _supports_attention_backend = True
+
+
+class Qwen3TTSTokenizerV2CausalConvNet(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        dilation=1,
+        stride=1,
+        groups=1,
+    ):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            dilation=dilation,
+            groups=groups,
+        )
+        self.stride = stride
+        self.kernel_size = (kernel_size - 1) * dilation + 1
+        self.dilation = dilation
+        self.padding = self.kernel_size - self.stride
+
+    def _get_extra_padding_for_conv1d(self, hidden_state: torch.Tensor) -> int:
+        length = hidden_state.shape[-1]
+        n_frames = (length - self.kernel_size + self.padding) / self.stride + 1
+        ideal_length = (math.ceil(n_frames) - 1) * self.stride + (self.kernel_size - self.padding)
+        return ideal_length - length
+
+    def forward(self, hidden_state):
+        extra_padding = self._get_extra_padding_for_conv1d(hidden_state)
+        hidden_state = F.pad(hidden_state, (self.padding, extra_padding), mode="constant", value=0)
+        return self.conv(hidden_state).contiguous()
+
+
+class Qwen3TTSTokenizerV2CausalTransConvNet(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1):
+        super().__init__()
+        self.conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride=stride)
+
+        pad = kernel_size - stride
+        self.left_pad = 0
+        self.right_pad = int(pad)
+
+    def forward(self, hidden_state):
+        hidden_state = self.conv(hidden_state)
+        if self.right_pad > 0:
+            hidden_state = hidden_state[..., : hidden_state.shape[-1] - self.right_pad]
+        return hidden_state.contiguous()
+
+
+class Qwen3TTSTokenizerV2ConvNeXtBlock(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.dwconv = Qwen3TTSTokenizerV2CausalConvNet(
+            dim,
+            dim,
+            kernel_size=7,
+            groups=dim,
+            dilation=1,
+        )
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, 4 * dim)
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(4 * dim, dim)
+        self.gamma = nn.Parameter(1e-6 * torch.ones(dim))
+
+    def forward(self, hidden_states):
+        input = hidden_states
+
+        hidden_states = self.dwconv(hidden_states)
+        hidden_states = hidden_states.permute(0, 2, 1)
+        hidden_states = self.norm(hidden_states)
+        hidden_states = self.pwconv1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.pwconv2(hidden_states)
+
+        hidden_states = self.gamma * hidden_states
+
+        hidden_states = hidden_states.permute(0, 2, 1)
+
+        hidden_states = input + hidden_states
+
+        return hidden_states
+
+
+class Qwen3TTSTokenizerV2DecoderRotatoryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: Qwen3TTSTokenizerV2DecoderConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @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 torch.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 Qwen3TTSTokenizerV2DecoderAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Qwen3TTSTokenizerV2DecoderConfig, layer_idx):
+        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
+        )
+        self.q_norm = nn.Identity()
+        self.k_norm = nn.Identity()
+        self.sliding_window = config.sliding_window
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_values: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        key_states = self.k_norm(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,
+            sliding_window=self.sliding_window,  # diff with Llama
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class Qwen3TTSTokenizerV2DecoderMlp(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=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        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
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Qwen3TTSTokenizerV2DecoderRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        Qwen3TTSTokenizerV2DecoderRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        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 Qwen3TTSTokenizerV2DecoderLayerScale(nn.Module):
+    """Layer scale from [Touvron et al 2021] (https://huggingface.co/papers/2103.17239).
+    This rescales diagonally the residual outputs close to 0, with a learnt scale.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        channels = config.hidden_size
+        initial_scale = config.layer_scale_initial_scale
+        self.scale = nn.Parameter(torch.full((channels,), initial_scale, requires_grad=True))
+
+    def forward(self, x: torch.Tensor):
+        return self.scale * x
+
+
+class Qwen3TTSTokenizerV2DecoderTransformerLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Qwen3TTSTokenizerV2DecoderConfig, layer_idx):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Qwen3TTSTokenizerV2DecoderAttention(config, layer_idx)
+        self.mlp = Qwen3TTSTokenizerV2DecoderMlp(config)
+        self.input_layernorm = Qwen3TTSTokenizerV2DecoderRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen3TTSTokenizerV2DecoderRMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.self_attn_layer_scale = Qwen3TTSTokenizerV2DecoderLayerScale(config)
+        self.mlp_layer_scale = Qwen3TTSTokenizerV2DecoderLayerScale(config)
+        self.attention_type = "sliding_attention"
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        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,
+            **kwargs,
+        )
+        hidden_states = residual + self.self_attn_layer_scale(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + self.mlp_layer_scale(hidden_states)
+
+        return hidden_states
+
+
+@auto_docstring
+class Qwen3TTSTokenizerV2DecoderTransformerModel(Qwen3TTSTokenizerV2DecoderPreTrainedModel):
+    _can_record_outputs = {
+        "hidden_states": Qwen3TTSTokenizerV2DecoderTransformerLayer,
+        "attentions": Qwen3TTSTokenizerV2DecoderAttention,
+    }
+
+    def __init__(self, config: Qwen3TTSTokenizerV2DecoderConfig):
+        super().__init__(config)
+        self.layers = nn.ModuleList(
+            [Qwen3TTSTokenizerV2DecoderTransformerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = Qwen3TTSTokenizerV2DecoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Qwen3TTSTokenizerV2DecoderRotatoryEmbedding(config=config)
+        self.gradient_checkpointing = False
+        self.has_sliding_layers = "sliding_attention" in self.config.layer_types
+        self.window_size = config.sliding_window
+
+        self.input_proj = nn.Linear(config.latent_dim, config.hidden_size)
+        self.output_proj = nn.Linear(config.hidden_size, config.latent_dim)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @check_model_inputs()
+    @auto_docstring
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        use_cache=None,
+        cache_position=None,
+        **kwargs,
+    ) -> BaseModelOutputWithPast:
+        if input_ids is not None:
+            raise ValueError("input_ids is not expected")
+        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 = self.embed_tokens(input_ids)
+        
+        inputs_embeds = self.input_proj(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
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "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,
+            }
+            # Create the masks
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+            }
+            # The sliding window alternating layers are not always activated depending on the config
+            if self.has_sliding_layers:
+                causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+                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)
+        hidden_states = self.output_proj(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+        )
+
+
+class SnakeBeta(nn.Module):
+    """
+    A modified Snake function which uses separate parameters for the magnitude of the periodic components
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter that controls frequency
+        - beta - trainable parameter that controls magnitude
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://huggingface.co/papers/2006.08195
+    """
+
+    def __init__(self, in_features, alpha=1.0):
+        super().__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha = Parameter(torch.zeros(in_features) * alpha)
+        self.beta = Parameter(torch.zeros(in_features) * alpha)
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, hidden_states):
+        """
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        """
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        alpha = torch.exp(alpha)
+        beta = torch.exp(beta)
+        hidden_states = hidden_states + (1.0 / (beta + self.no_div_by_zero)) * torch.pow(
+            torch.sin(hidden_states * alpha), 2
+        )
+
+        return hidden_states
+
+
+class Qwen3TTSTokenizerV2DecoderDecoderResidualUnit(nn.Module):
+    def __init__(self, dim: int = 16, dilation: int = 1):
+        super().__init__()
+
+        self.act1 = SnakeBeta(dim)
+        self.conv1 = Qwen3TTSTokenizerV2CausalConvNet(dim, dim, kernel_size=7, dilation=dilation)
+        self.act2 = SnakeBeta(dim)
+        self.conv2 = Qwen3TTSTokenizerV2CausalConvNet(dim, dim, kernel_size=1)
+
+    def forward(self, hidden_state):
+        residual = hidden_state
+
+        hidden_state = self.act1(hidden_state)
+        hidden_state = self.conv1(hidden_state)
+        hidden_state = self.act2(hidden_state)
+        hidden_state = self.conv2(hidden_state)
+        return hidden_state + residual
+
+
+class Qwen3TTSTokenizerV2DecoderDecoderBlock(Qwen3TTSTokenizerV2DecoderPreTrainedModel):
+    def __init__(self, config: Qwen3TTSTokenizerV2DecoderConfig, layer_idx):
+        super().__init__(config)
+        in_dim = config.decoder_dim // 2**layer_idx
+        out_dim = config.decoder_dim // 2 ** (layer_idx + 1)
+        upsample_rate = config.upsample_rates[layer_idx]
+
+        block = [
+            SnakeBeta(in_dim),
+            Qwen3TTSTokenizerV2CausalTransConvNet(in_dim, out_dim, 2 * upsample_rate, upsample_rate),
+        ]
+
+        for dilation in (1, 3, 9):
+            block.append(Qwen3TTSTokenizerV2DecoderDecoderResidualUnit(out_dim, dilation))
+
+        self.block = nn.ModuleList(block)
+
+    def forward(self, hidden):
+        for block in self.block:
+            hidden = block(hidden)
+        return hidden
+
+
+class EuclideanCodebook(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        epsilon: float = 1e-5,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.codebook_size = codebook_size
+        self.epsilon = epsilon
+
+        self.cluster_usage = nn.Parameter(torch.ones(codebook_size))
+        self.embedding_sum = nn.Parameter(torch.zeros(codebook_size, dim))
+
+    def decode(self, codes: torch.Tensor) -> torch.Tensor:
+        embedding = self.embedding_sum / self.cluster_usage.clamp(min=self.epsilon)[:, None]
+        quantized = F.embedding(codes, embedding)
+        return quantized
+
+
+class VectorQuantization(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        codebook_dim: Optional[int] = None,
+        epsilon: float = 1e-5,
+    ):
+        super().__init__()
+        if codebook_dim is None:
+            codebook_dim = dim
+
+        requires_projection = codebook_dim != dim
+
+        self.project_out = (
+            nn.Linear(codebook_dim, dim) if requires_projection else nn.Identity()
+        )
+        self.epsilon = epsilon
+        self._codebook = EuclideanCodebook(
+            dim=codebook_dim,
+            codebook_size=codebook_size,
+            epsilon=epsilon
+        )
+        self.codebook_size = codebook_size
+
+    def decode(self, codes: torch.Tensor) -> torch.Tensor:
+        quantized = self._codebook.decode(codes)
+        quantized = self.project_out(quantized)
+        quantized = quantized.transpose(1, 2)
+        return quantized
+
+
+class ResidualVectorQuantization(nn.Module):
+    def __init__(self, *, num_quantizers: int, **kwargs):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [VectorQuantization(**kwargs) for _ in range(num_quantizers)]
+        )
+
+    def decode(self, codes: torch.Tensor) -> torch.Tensor:
+        quantized = torch.zeros([1], device=codes.device)[0]
+        for idx, layer_codes in enumerate(codes):
+            layer = self.layers[idx]
+            assert isinstance(layer, VectorQuantization)
+            quantized = quantized + layer.decode(layer_codes)
+        return quantized
+
+
+class ResidualVectorQuantizer(nn.Module):
+    def __init__(
+        self,
+        dimension: int = 128,
+        input_dimension: Optional[int] = None,
+        output_dimension: Optional[int] = None,
+        n_q: int = 8,
+        q_dropout: bool = False,
+        no_quantization_rate: float = 0.0,
+        bins: int = 1024,
+        decay: float = 0.99,
+        force_projection: bool = False,
+    ):
+        super().__init__()
+        self.max_n_q = n_q
+        self.n_q = n_q
+        self.q_dropout = q_dropout
+        self.no_quantization_rate = no_quantization_rate
+        self.dimension = dimension
+        self.input_dimension = input_dimension or dimension
+        self.output_dimension = output_dimension or dimension
+        self.bins = bins
+        self.decay = decay
+        self.input_proj: torch.nn.Module
+        self.output_proj: torch.nn.Module
+        if self.input_dimension == self.dimension and not force_projection:
+            self.input_proj = torch.nn.Identity()
+        else:
+            self.input_proj = torch.nn.Conv1d(
+                self.input_dimension, self.dimension, 1, bias=False
+            )
+        if self.output_dimension == self.dimension and not force_projection:
+            self.output_proj = torch.nn.Identity()
+        else:
+            self.output_proj = torch.nn.Conv1d(
+                self.dimension, self.output_dimension, 1, bias=False
+            )
+        self.vq = ResidualVectorQuantization(
+            dim=self.dimension,
+            codebook_size=self.bins,
+            num_quantizers=self.n_q
+        )
+
+    def decode(self, codes: torch.Tensor) -> torch.Tensor:
+        codes = codes.transpose(0, 1)
+        quantized = self.vq.decode(codes)
+        quantized = self.output_proj(quantized)
+        return quantized
+
+
+class SplitResidualVectorQuantizer(nn.Module):
+    """Residual Vector Quantizer with separate projections for the first quantizer and the rest.
+
+    Args:
+        n_q (int): Number of residual vector quantizers used.
+        n_semantic_q (int): Number of residual vector quantizers used for the semantic quantizer.
+        **kwargs: Arguments to the constructor of `ResidualVectorQuantizer` that are shared between both.
+    """
+
+    def __init__(
+        self,
+        *,
+        n_q: int = 8,
+        n_q_semantic: int = 1,
+        **kwargs,
+    ):
+        super().__init__()
+        assert n_q > n_q_semantic, (
+            f"Number of quantizers {n_q} must be larger "
+            f"than the number of semantic quantizers {n_q_semantic}."
+        )
+        self.max_n_q = n_q
+        self.n_q_semantic = n_q_semantic
+        self.n_q_acoustic = n_q - n_q_semantic
+        q_dropout = kwargs.pop("q_dropout", False)
+        self.rvq_first = ResidualVectorQuantizer(
+            n_q=n_q_semantic, force_projection=True, q_dropout=False, **kwargs
+        )
+        self.rvq_rest = ResidualVectorQuantizer(
+            n_q=n_q - n_q_semantic,
+            force_projection=True,
+            q_dropout=q_dropout,
+            **kwargs,
+        )
+
+    def decode(self, codes: torch.Tensor) -> torch.Tensor:
+        """Decode the given codes to the quantized representation."""
+        # codes is [B, K, T], with T frames, K nb of codebooks.
+        quantized = self.rvq_first.decode(codes[:, : self.n_q_semantic])
+        if codes.shape[1] > self.n_q_semantic:
+            quantized += self.rvq_rest.decode(codes[:, self.n_q_semantic :])
+        return quantized
+
+
+class Qwen3TTSTokenizerV2Decoder(Qwen3TTSTokenizerV2DecoderPreTrainedModel):
+    def __init__(self, config: Qwen3TTSTokenizerV2DecoderConfig):
+        super().__init__(config)
+        self.total_upsample = np.prod(config.upsample_rates + config.upsampling_ratios)
+        self.pre_transformer = Qwen3TTSTokenizerV2DecoderTransformerModel._from_config(config)
+        
+        self.quantizer = SplitResidualVectorQuantizer(
+            dimension=config.codebook_dim // 2,
+            n_q=config.num_quantizers,
+            n_q_semantic=1,
+            bins=config.codebook_size,
+            input_dimension=config.codebook_dim,
+            output_dimension=config.codebook_dim,
+        )
+
+        self.pre_conv = Qwen3TTSTokenizerV2CausalConvNet(
+            config.codebook_dim,
+            config.latent_dim,
+            kernel_size=3,
+        )
+
+        upsample = []
+        for factor in config.upsampling_ratios:
+            upsample.append(
+                nn.ModuleList(
+                    [
+                        Qwen3TTSTokenizerV2CausalTransConvNet(config.latent_dim, config.latent_dim, factor, factor),
+                        Qwen3TTSTokenizerV2ConvNeXtBlock(config.latent_dim),
+                    ]
+                )
+            )
+        self.upsample = nn.ModuleList(upsample)
+
+        decoder = [Qwen3TTSTokenizerV2CausalConvNet(config.latent_dim, config.decoder_dim, 7)]
+        for i in range(len(config.upsample_rates)):
+            decoder.append(Qwen3TTSTokenizerV2DecoderDecoderBlock(config, i))
+        output_dim = config.decoder_dim // 2 ** len(config.upsample_rates)
+        decoder += [
+            SnakeBeta(output_dim),
+            Qwen3TTSTokenizerV2CausalConvNet(output_dim, 1, 7),
+        ]
+        self.decoder = nn.ModuleList(decoder)
+
+        self.post_init()
+
+    def forward(self, codes):
+        if codes.shape[1] != self.config.num_quantizers:
+            raise ValueError(f"Expected {self.config.num_quantizers} layer of codes, got {codes.shape[1]}")
+
+        hidden = self.quantizer.decode(codes)
+        hidden = self.pre_conv(hidden).transpose(1, 2)
+
+        hidden = self.pre_transformer(inputs_embeds=hidden).last_hidden_state
+        hidden = hidden.permute(0, 2, 1)
+        for blocks in self.upsample:
+            for block in blocks:
+                hidden = block(hidden)
+        wav = hidden
+        for block in self.decoder:
+            wav = block(wav)
+        return wav.clamp(min=-1, max=1)
+
+    def chunked_decode(self, codes, chunk_size=300, left_context_size=25):
+        wavs = []
+        start_index = 0
+        while start_index < codes.shape[-1]:
+            end_index = min(start_index + chunk_size, codes.shape[-1])
+            context_size = left_context_size if start_index - left_context_size > 0 else start_index
+            codes_chunk = codes[..., start_index - context_size : end_index]
+            wav_chunk = self(codes_chunk)
+            wavs.append(wav_chunk[..., context_size * self.total_upsample :])
+            start_index = end_index
+        return torch.cat(wavs, dim=-1)
+
+
+class Qwen3TTSTokenizerV2Encoder(MimiModel):
+    def __init__(self, config: MimiConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.upsample = None
+        self.decoder_transformer = None
+        self.decoder = None
+
+        self.post_init()
+
+
+@auto_docstring
+class Qwen3TTSTokenizerV2PreTrainedModel(PreTrainedModel):
+    config: Qwen3TTSTokenizerV2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _can_compile_fullgraph = False
+    _supports_attention_backend = True
+
+
+@auto_docstring(
+    custom_intro="""
+    The Qwen3TTSTokenizerV2 model.
+    """
+)
+class Qwen3TTSTokenizerV2Model(Qwen3TTSTokenizerV2PreTrainedModel):
+    def __init__(self, config: Qwen3TTSTokenizerV2Config):
+        super().__init__(config)
+        self.config = config
+
+        self.encoder_valid_num_quantizers = config.encoder_valid_num_quantizers
+
+        self.input_sample_rate = config.input_sample_rate
+        self.output_sample_rate = config.output_sample_rate
+
+        self.decode_upsample_rate = config.decode_upsample_rate
+        self.encode_downsample_rate = config.encode_downsample_rate
+
+        self.encoder = Qwen3TTSTokenizerV2Encoder._from_config(self.config.encoder_config)
+        self.decoder = Qwen3TTSTokenizerV2Decoder._from_config(self.config.decoder_config)
+
+        self.post_init()
+    
+    def get_model_type(self):
+        return self.config.model_type
+    
+    def get_input_sample_rate(self):
+        return self.input_sample_rate
+    
+    def get_output_sample_rate(self):
+        return self.output_sample_rate
+    
+    def get_encode_downsample_rate(self):
+        return self.encode_downsample_rate
+    
+    def get_decode_upsample_rate(self):
+        return self.decode_upsample_rate
+    
+    def encode(
+        self,
+        input_values: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor, Optional[torch.Tensor]], Qwen3TTSTokenizerV2EncoderOutput]:
+        """
+        Encodes the input audio waveform into discrete codes.
+
+        Args:
+            input_values (`torch.Tensor` of shape `(batch_size, sequence_length)`):
+                Float values of the input audio waveform.
+            padding_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`):
+                Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked* or 0
+                for *masked*.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        encoded_frames = self.encoder.encode(input_values=input_values.unsqueeze(1),
+                                             return_dict=True)
+        audio_codes = encoded_frames.audio_codes[:, :self.encoder_valid_num_quantizers]
+        audio_codes = [code[..., :-(-mask.sum() // self.encode_downsample_rate)].transpose(0, 1) for code, mask in zip(audio_codes, padding_mask)]
+
+        if not return_dict:
+            return (
+                audio_codes,
+            )
+
+        return Qwen3TTSTokenizerV2EncoderOutput(audio_codes)
+
+    def decode(
+        self,
+        audio_codes: torch.Tensor,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], Qwen3TTSTokenizerV2DecoderOutput]:
+        """
+        Decodes the given frames into an output audio waveform.
+
+        Note that the output might be a bit bigger than the input. In that case, any extra steps at the end can be
+        trimmed.
+
+        Args:
+            audio_codes (`torch.LongTensor`  of shape `(batch_size, codes_length, num_quantizers)`, *optional*):
+                Discret code embeddings computed using `model.encode`.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        audio_lengths = (audio_codes[..., 0] > -1).sum(1) * self.decode_upsample_rate
+
+        audio_codes = torch.clamp(audio_codes, min=0)
+        audio_values = self.decoder.chunked_decode(audio_codes.transpose(1, 2)).squeeze(1)
+
+        audio_values = [a[:l] for a, l in zip(audio_values, audio_lengths)]
+
+        if not return_dict:
+            return (
+                audio_values,
+            )
+
+        return Qwen3TTSTokenizerV2DecoderOutput(audio_values)
+
+
+__all__ = ["Qwen3TTSTokenizerV2Model", "Qwen3TTSTokenizerV2PreTrainedModel"]