# === HEADER (license + imports) ===
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# Copyright 2026 the HuggingFace 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.

import math
from collections.abc import Callable
from dataclasses import dataclass
from functools import cached_property
from typing import Optional

import torch
from torch import nn
from torch.nn import functional as F

from ... import initialization as init
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...configuration_utils import PreTrainedConfig
from ...generation import GenerationMixin
from ...integrations import use_experts_implementation, use_kernelized_func
from ...masking_utils import (
    create_bidirectional_mask,
    create_causal_mask,
    create_masks_for_generate,
    create_sliding_window_causal_mask,
)
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, 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,
    is_accelerate_available,
    torch_compilable_check,
)
from ...utils.generic import maybe_autocast, merge_with_config_defaults
from ...utils.output_capturing import OutputRecorder, capture_outputs
from ..auto.modeling_auto import AutoModel
from .configuration_gemma4 import Gemma4AudioConfig, Gemma4Config, Gemma4TextConfig, Gemma4VisionConfig


if is_accelerate_available():
    from accelerate.hooks import add_hook_to_module


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for Gemma4 outputs, with hidden states and attentions.
    """
)
class Gemma4ModelOutputWithPast(BaseModelOutputWithPast):
    r"""
    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.
    image_hidden_states (`torch.FloatTensor`, *optional*):

# === CLASS/FUNCTION OUTLINE (signatures + short body) ===
@dataclass
@auto_docstring(
    custom_intro="""
    Base class for Gemma4 outputs, with hidden states and attentions.
    """
)
class Gemma4ModelOutputWithPast(BaseModelOutputWithPast):
    r"""
    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.
    image_hidden_states (`torch.FloatTensor`, *optional*):
    ...

@dataclass
@auto_docstring(
    custom_intro="""
    Base class for Gemma4 causal language model (or autoregressive) outputs.
    """
)
class Gemma4CausalLMOutputWithPast(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.text_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).
    ...

@dataclass
@auto_docstring
class Gemma4AudioModelOutput(BaseModelOutputWithPooling):
    r"""
    attention_mask (`torch.BoolTensor`, *optional*):
        A torch.BoolTensor of shape `(batch_size, num_frames)`. True for valid positions, False for padding.
    """

    attention_mask: torch.BoolTensor | None = None


class Gemma4ClippableLinear(nn.Module):
    def __init__(
        self,
    ...

class Gemma4RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6, with_scale: bool = True):
        super().__init__()
        self.eps = eps
        self.with_scale = with_scale

        if self.with_scale:
            self.weight = nn.Parameter(torch.ones(dim), requires_grad=True)

    def _norm(self, hidden_states: torch.Tensor):
        mean_squared = hidden_states.pow(2).mean(-1, keepdim=True) + self.eps
        # Use torch.pow() (over torch.sqrt() or torch.rsqrt()) to addess compiler differences between Torch and JAX
        return hidden_states * torch.pow(mean_squared, -0.5)

    ...

class Gemma4AudioRelPositionalEncoding(nn.Module):
    """Sinusoidal relative positional encoding for the audio encoder.

    Produces position embeddings of shape [1, 2*context_size - 1, hidden_size] with
    concatenated [sin..., cos...] layout matching the original Gemma4 convention.
    """

    inv_timescales: torch.Tensor

    def __init__(self, config: Gemma4AudioConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.context_size = (
            config.attention_chunk_size + config.attention_context_left - 1 + config.attention_context_right
    ...

class Gemma4AudioAttention(nn.Module):
    """Chunked local attention with relative position bias"""

    def __init__(self, config: Gemma4AudioConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.attention_logits_soft_cap = config.attention_logit_cap
        self.head_dim = config.hidden_size // config.num_attention_heads
        self.num_heads = config.num_attention_heads

        self.q_scale = (self.head_dim**-0.5) / math.log(2)
        self.k_scale = math.log(1 + math.e) / math.log(2)

    ...

class Gemma4AudioSubSampleConvProjectionLayer(nn.Module):
    def __init__(self, in_channels, out_channels, norm_eps):
        super().__init__()
        self.conv = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=(3, 3),
            stride=(2, 2),
            padding=1,
            bias=False,
        )
        self.norm = nn.LayerNorm(out_channels, eps=norm_eps, elementwise_affine=True, bias=False)
        self.act = nn.ReLU()

    ...

class Gemma4AudioSubSampleConvProjection(nn.Module):
    def __init__(self, config: Gemma4AudioConfig):
        super().__init__()
        self.layer0 = Gemma4AudioSubSampleConvProjectionLayer(
            in_channels=1,
            out_channels=config.subsampling_conv_channels[0],
            norm_eps=config.rms_norm_eps,
        )
        self.layer1 = Gemma4AudioSubSampleConvProjectionLayer(
            in_channels=config.subsampling_conv_channels[0],
            out_channels=config.subsampling_conv_channels[1],
            norm_eps=config.rms_norm_eps,
        )
        proj_input_dim = (config.subsampling_conv_channels[0] // 4) * config.subsampling_conv_channels[1]
    ...

class Gemma4AudioFeedForward(nn.Module):
    def __init__(self, config: Gemma4AudioConfig):
        super().__init__()
        self.config = config

        self.ffw_layer_1 = Gemma4ClippableLinear(config, config.hidden_size, config.hidden_size * 4)
        self.ffw_layer_2 = Gemma4ClippableLinear(config, config.hidden_size * 4, config.hidden_size)

        self.pre_layer_norm = Gemma4RMSNorm(config.hidden_size)
        self.post_layer_norm = Gemma4RMSNorm(config.hidden_size)
        self.act_fn = ACT2FN[config.hidden_act]

        self.gradient_clipping = config.gradient_clipping
        self.post_layer_scale = config.residual_weight
    ...

class Gemma4AudioCausalConv1d(nn.Conv1d):
    # def __init__(
    #     self,
    #     in_channels: int,
    #     out_channels: int,
    #     kernel_size: int,
    #     # cache_key: str,
    #     stride: int = 1,
    #     dilation: int = 1,
    #     bias: bool = True,
    # ):
    #     super().__init__(in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, bias=bias)
    # self.cache_key = cache_key

    ...

class Gemma4AudioLightConv1d(nn.Module):
    def __init__(self, config: Gemma4AudioConfig):
        super().__init__()
        self.config = config

        self.linear_start = Gemma4ClippableLinear(config, config.hidden_size, config.hidden_size * 2)
        self.linear_end = Gemma4ClippableLinear(config, config.hidden_size, config.hidden_size)
        self.depthwise_conv1d = Gemma4AudioCausalConv1d(
            in_channels=config.hidden_size,
            out_channels=config.hidden_size,
            kernel_size=config.conv_kernel_size,
            groups=config.hidden_size,
            bias=False,
        )
    ...

class Gemma4AudioLayer(nn.Module):
    def __init__(self, config: Gemma4AudioConfig, layer_idx: int):
        super().__init__()
        self.config = config

        self.feed_forward1 = Gemma4AudioFeedForward(config)
        self.feed_forward2 = Gemma4AudioFeedForward(config)
        self.self_attn = Gemma4AudioAttention(config, layer_idx)
        self.lconv1d = Gemma4AudioLightConv1d(config)

        self.norm_pre_attn = Gemma4RMSNorm(config.hidden_size)
        self.norm_post_attn = Gemma4RMSNorm(config.hidden_size)
        self.norm_out = Gemma4RMSNorm(config.hidden_size)

    ...

class Gemma4VisionPatchEmbedder(nn.Module):
    def __init__(self, config: Gemma4VisionConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.patch_size = config.patch_size
        self.position_embedding_size = config.position_embedding_size

        self.input_proj = nn.Linear(3 * self.patch_size**2, self.hidden_size, bias=False)
        self.position_embedding_table = nn.Parameter(torch.ones(2, self.position_embedding_size, self.hidden_size))

    def _position_embeddings(self, pixel_position_ids: torch.Tensor, padding_positions: torch.Tensor) -> torch.Tensor:
        """Prepare patch positions map for matmul with positon embedding table."""
        # Expanding and permute patch positions to (batch_size, num_patches, 2, position_embedding_size) for matmul.
    ...

class Gemma4VisionPooler(nn.Module):
    """Scaling and optional spatial pooling for vision encodings"""

    def __init__(self, config: Gemma4VisionConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.root_hidden_size = self.hidden_size**0.5

    def _avg_pool_by_positions(
        self, hidden_states: torch.Tensor, pixel_position_ids: torch.Tensor, length: int
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """
        2D spatial pooling according to patch positions.
        Pools the input tokens by averaging patches within a `k^2` grid, where `k` is determined by the ratio between
    ...

class Gemma4VisionMLP(nn.Module):
    def __init__(self, config: Gemma4VisionConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = Gemma4ClippableLinear(config, self.hidden_size, self.intermediate_size)
        self.up_proj = Gemma4ClippableLinear(config, self.hidden_size, self.intermediate_size)
        self.down_proj = Gemma4ClippableLinear(config, self.intermediate_size, self.hidden_size)
        self.act_fn = ACT2FN[config.hidden_activation]

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

class Gemma4VisionRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: Gemma4VisionConfig, 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]
    ...

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(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, unsqueeze_dim: int = 1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        x (`torch.Tensor`): The tensor to embed.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
    ...

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,
    ...

def apply_multidimensional_rope(
    x: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
    position_ids: torch.Tensor,
    unsqueeze_dim: int = 2,
) -> torch.Tensor:
    """Applies multidimensional RoPE to inputs.

    Args:
        x (`torch.Tensor`): The tensor to embed.
        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*):
    ...

@use_kernelized_func(apply_rotary_pos_emb)
class Gemma4VisionAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Gemma4VisionConfig, layer_idx: int):
        super().__init__()
        self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
        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 = 1.0
        self.attention_dropout = self.config.attention_dropout
        self.is_causal = False
    ...

class Gemma4VisionEncoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: Gemma4VisionConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.layer_idx = layer_idx
        self.self_attn = Gemma4VisionAttention(config=config, layer_idx=layer_idx)
        self.mlp = Gemma4VisionMLP(config)
        self.input_layernorm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.pre_feedforward_layernorm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_feedforward_layernorm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)

    def forward(
    ...

class Gemma4VisionEncoder(nn.Module):
    def __init__(self, config: Gemma4VisionConfig):
        super().__init__()
        self.config = config
        self.num_layers = config.num_hidden_layers
        self.rotary_emb = Gemma4VisionRotaryEmbedding(config)
        self.layers = nn.ModuleList(
            [Gemma4VisionEncoderLayer(config=config, layer_idx=i) for i in range(self.num_layers)]
        )

    def forward(
        self,
        inputs_embeds: torch.Tensor,
        attention_mask: torch.Tensor,
    ...

class Gemma4TextMLP(nn.Module):
    def __init__(self, config: Gemma4TextConfig, layer_idx: int):
        super().__init__()
        first_kv_shared_layer_idx = config.num_hidden_layers - config.num_kv_shared_layers
        is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx > 0
        use_double_wide_mlp = config.use_double_wide_mlp and is_kv_shared_layer
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size * (2 if use_double_wide_mlp else 1)
        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_activation]

    ...

class Gemma4TextRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: Gemma4TextConfig, device=None, layer_type=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.layer_types = set(config.layer_types)
        self.rope_init_fns: dict[str, Callable[..., tuple[torch.Tensor, float]]] = {}
        self.rope_type: dict[str, str] = {}

        for layer_type in self.layer_types:
    ...

@use_kernelized_func(apply_rotary_pos_emb)
class Gemma4TextAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Gemma4TextConfig, layer_idx: int):
        super().__init__()
        self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
        self.config = config
        self.layer_idx = layer_idx
        self.is_sliding = self.layer_type == "sliding_attention"
        self.sliding_window = config.sliding_window if self.is_sliding else None

        self.head_dim = config.global_head_dim if not self.is_sliding and config.global_head_dim else config.head_dim
        self.use_alternative_attention = config.attention_k_eq_v and not self.is_sliding
    ...

@use_experts_implementation
class Gemma4TextExperts(nn.Module):
    """Collection of expert weights stored as 3D tensors."""

    def __init__(self, config: Gemma4TextConfig):
        super().__init__()
        self.num_experts = config.num_experts
        self.hidden_dim = config.hidden_size
        self.intermediate_dim = config.moe_intermediate_size
        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim))
        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim))
        self.act_fn = ACT2FN[config.hidden_activation]

    def forward(
    ...

class Gemma4TextRouter(nn.Module):
    def __init__(self, config: Gemma4TextConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.scalar_root_size = self.hidden_size**-0.5
        self.eps = config.rms_norm_eps

        self.norm = Gemma4RMSNorm(self.hidden_size, eps=self.eps, with_scale=False)
        self.proj = nn.Linear(config.hidden_size, config.num_experts, bias=False)
        self.scale = nn.Parameter(torch.ones(self.hidden_size))
        self.per_expert_scale = nn.Parameter(torch.ones(config.num_experts))

    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    ...

class Gemma4TextDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: Gemma4TextConfig | Gemma4VisionConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.layer_idx = layer_idx
        self.self_attn = Gemma4TextAttention(config=config, layer_idx=layer_idx)
        self.mlp = Gemma4TextMLP(config, layer_idx)
        self.input_layernorm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.pre_feedforward_layernorm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_feedforward_layernorm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.register_buffer("layer_scalar", torch.ones(1))

    ...

class Gemma4TextScaledWordEmbedding(nn.Embedding):
    """
    This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
    """

    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
        super().__init__(num_embeddings, embedding_dim, padding_idx)
        self.scalar_embed_scale = embed_scale
        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)

    def forward(self, input_ids: torch.Tensor):
        return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)


    ...

@auto_docstring
class Gemma4PreTrainedModel(PreTrainedModel):
    config: Gemma4Config
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Gemma4TextDecoderLayer", "Gemma4VisionEncoderLayer", "Gemma4AudioLayer"]
    _skip_keys_device_placement = ["past_key_values", "shared_kv_states"]
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = None  # override
    ...

@auto_docstring(custom_intro="The base Gemma 4 language model without a language modeling head.")
class Gemma4TextModel(Gemma4PreTrainedModel):
    config: Gemma4TextConfig
    input_modalities = ("text",)
    _can_record_outputs = {
        "router_logits": OutputRecorder(Gemma4TextRouter, index=0),
        "hidden_states": Gemma4TextDecoderLayer,
        "attentions": Gemma4TextAttention,
    }

    def __init__(self, config: Gemma4TextConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
    ...

@auto_docstring(custom_intro="The base Gemma 4 language model with a language modeling head.")
class Gemma4ForCausalLM(Gemma4PreTrainedModel, GenerationMixin):
    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
    _tp_plan = {"lm_head": "colwise_gather_output"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
    config: Gemma4TextConfig
    base_model_prefix = "model"

    def __init__(self, config: Gemma4TextConfig):
        super().__init__(config)
        self.model = Gemma4TextModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        # Grab the ones from the child
    ...

def sliding_window_mask_function(sliding_window: tuple[int, int]) -> Callable:
    """
    This creates uni/bidirectional attention mask with sliding window.
    """

    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
        left_window_size, right_window_size = sliding_window

        dist = q_idx - kv_idx
        left_mask = (dist >= 0) & (dist < left_window_size)
        right_mask = (dist < 0) & (-dist < right_window_size)
        return left_mask | right_mask

    return inner_mask
    ...

class Gemma4AudioModel(Gemma4PreTrainedModel):
    """An audio encoder based on the [Universal Speech Model](https://huggingface.co/papers/2303.01037) architecture."""

    config: Gemma4AudioConfig
    main_input_name = "input_features"
    base_model_prefix = "model.audio_tower"  # prefix for Gemma4ForConditionalGeneration saved checkpoints, required for Gemma4AudioModel.from_pretrained()
    _can_record_outputs = {
        "hidden_states": Gemma4AudioLayer,
        "attentions": Gemma4AudioAttention,
    }

    def __init__(self, config: Gemma4AudioConfig):
        super().__init__(config)
        self.config = config
    ...

class Gemma4VisionModel(Gemma4PreTrainedModel):
    """The Gemma 4 Vision Encoder."""

    config = Gemma4VisionConfig
    _can_record_outputs = {
        "hidden_states": Gemma4VisionEncoderLayer,
        "attentions": Gemma4VisionAttention,
    }

    def __init__(self, config: Gemma4VisionConfig):
        super().__init__(config)
        self.patch_embedder = Gemma4VisionPatchEmbedder(config)
        self.encoder = Gemma4VisionEncoder(config)
        self.pooler = Gemma4VisionPooler(config)
    ...

class Gemma4MultimodalEmbedder(nn.Module):
    """Embeds token ids or soft tokens for multimodal content into language model space."""

    def __init__(
        self,
        multimodal_config: Gemma4AudioConfig | Gemma4VisionConfig,
        text_config: Gemma4TextConfig,
    ):
        super().__init__()

        self.multimodal_hidden_size = getattr(multimodal_config, "output_proj_dims", multimodal_config.hidden_size)
        self.eps = multimodal_config.rms_norm_eps
        self.text_hidden_size = text_config.hidden_size
        self.embedding_projection = nn.Linear(self.multimodal_hidden_size, self.text_hidden_size, bias=False)
    ...

def token_type_ids_mask_function(
    token_type_ids: torch.Tensor | None,
    image_group_ids: torch.Tensor | None,
) -> Callable | None:
    """
    This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
    not start and end indices.
    """
    # Do not return an additional mask in this case
    if token_type_ids is None:
        return None

    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
        seq_length = image_group_ids.shape[-1]
    ...

def create_causal_mask_mapping(
    config: PreTrainedConfig,
    inputs_embeds: torch.Tensor,
    attention_mask: torch.Tensor | None,
    past_key_values: Cache | None,
    position_ids: torch.Tensor | None,
    mm_token_type_ids: torch.Tensor | None = None,
    pixel_values: torch.FloatTensor | None = None,
    is_training: bool = False,
    is_first_iteration: bool | None = None,
    **kwargs,
) -> dict:
    """
    Overwrites the base `create_masks_for_generate` with `token_type_ids` masking to create the causal mask mapping
    ...

@auto_docstring(
    custom_intro="""
    The base Gemma 4 model comprising a vision backbone, an audio backbone, and a language model without a
    language modeling head.
    """
)
class Gemma4Model(Gemma4PreTrainedModel):
    # we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
    accepts_loss_kwargs = False

    def __init__(self, config: Gemma4Config):
        super().__init__(config)
        self.vocab_size = config.text_config.vocab_size

    ...

@auto_docstring(
    custom_intro="""
    The base Gemma 4 model comprising a vision backbone, an audio backbone, a language model, and a language modeling
    head.
    """
)
class Gemma4ForConditionalGeneration(Gemma4PreTrainedModel, GenerationMixin):
    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
    accepts_loss_kwargs = False
    base_model_prefix = "model"

    def __init__(self, config: Gemma4Config):
        super().__init__(config)
        self.model = Gemma4Model(config)
    ...

