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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 import UserDict
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
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
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


@auto_docstring(
    custom_intro="""
    Base class for Gemma4 outputs, with hidden states and attentions.
    """
)
@dataclass
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*):
        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
    audio_hidden_states (`torch.FloatTensor`, *optional*):
        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
        audio_hidden_states of the model produced by the audio encoder and after projecting the last hidden state.
    shared_kv_states (`dict`, *optional*):
        Dictionary mapping layer type strings to tuples of (key_states, value_states) tensors.
        Used to pass shared KV states between layers during KV sharing.
    """

    image_hidden_states: torch.FloatTensor | None = None

    audio_hidden_states: torch.FloatTensor | None = None

    shared_kv_states: dict[str, tuple[torch.Tensor, torch.Tensor]] | None = None


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

        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*):
        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
        image_hidden_states of the model produced by the vision encoder after projecting last hidden state.
    audio_hidden_states (`torch.FloatTensor`, *optional*):
        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
        audio_hidden_states of the model produced by the audio encoder and after projecting the last hidden state.
    shared_kv_states (`dict`, *optional*):
        Dictionary mapping layer type strings to tuples of (key_states, value_states) tensors.
        Used to pass shared KV states between layers during KV sharing.
    """

    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
    image_hidden_states: torch.FloatTensor | None = None

    audio_hidden_states: torch.FloatTensor | None = None

    shared_kv_states: dict[str, tuple[torch.Tensor, torch.Tensor]] | None = None


@dataclass
class Gemma4TextModelOutputWithPast(BaseModelOutputWithPast):
    """
    BaseModelOutputWithPast extended with shared_kv_states for KV sharing.

    Args:
        shared_kv_states (`dict`, *optional*):
            Dictionary mapping layer type strings to tuples of (key_states, value_states) tensors.
            Used to pass shared KV states between layers during KV sharing.
    """

    shared_kv_states: dict[str, tuple[torch.Tensor, torch.Tensor]] | None = None


@auto_docstring
@dataclass
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,
        config: Gemma4VisionConfig | Gemma4AudioConfig,
        in_features: int,
        out_features: int,
    ) -> None:
        super().__init__()
        self.use_clipped_linears = config.use_clipped_linears
        self.linear = nn.Linear(in_features, out_features, bias=False)

        if self.use_clipped_linears:
            self.register_buffer("input_min", torch.tensor(-float("inf")))
            self.register_buffer("input_max", torch.tensor(float("inf")))
            self.register_buffer("output_min", torch.tensor(-float("inf")))
            self.register_buffer("output_max", torch.tensor(float("inf")))

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        if self.use_clipped_linears:
            hidden_states = torch.clamp(hidden_states, self.input_min, self.input_max)

        hidden_states = self.linear(hidden_states)

        if self.use_clipped_linears:
            hidden_states = torch.clamp(hidden_states, self.output_min, self.output_max)

        return hidden_states


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)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        normed_output = self._norm(hidden_states.float())
        if self.with_scale:
            normed_output = normed_output * self.weight.float()
        return normed_output.type_as(hidden_states)


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

    Produces position embeddings of shape [1, context_size // 2 + 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
        )
        min_timescale = 1.0
        max_timescale = 10000.0
        num_timescales = self.hidden_size // 2
        log_timescale_increment = math.log(max_timescale / min_timescale) / max(num_timescales - 1, 1)
        inv_timescales = min_timescale * torch.exp(torch.arange(num_timescales) * -log_timescale_increment)
        self.register_buffer("inv_timescales", inv_timescales.unsqueeze(0).unsqueeze(0), persistent=False)

    @torch.no_grad()
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        position_ids = torch.arange(self.context_size // 2, -1, -1, device=hidden_states.device)
        position_ids = position_ids[..., None]
        scaled_time = position_ids * self.inv_timescales.to(device=hidden_states.device)
        pos_embed = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=-1)
        return pos_embed.to(dtype=hidden_states.dtype)


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)

        self.chunk_size = config.attention_chunk_size
        self.max_past_horizon = config.attention_context_left - 1
        self.max_future_horizon = config.attention_context_right
        self.context_size = self.chunk_size + self.max_past_horizon + self.max_future_horizon

        self.q_proj = Gemma4ClippableLinear(config, config.hidden_size, self.num_heads * self.head_dim)
        self.k_proj = Gemma4ClippableLinear(config, config.hidden_size, self.num_heads * self.head_dim)
        self.v_proj = Gemma4ClippableLinear(config, config.hidden_size, self.num_heads * self.head_dim)
        self.post = Gemma4ClippableLinear(config, config.hidden_size, config.hidden_size)

        self.relative_k_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.per_dim_scale = nn.Parameter(torch.zeros(self.head_dim))

        self.register_buffer("softcap", torch.tensor(self.attention_logits_soft_cap), persistent=False)

    def _convert_to_block(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """Splits a `(batch_size, seq_len, num_heads, head_dim)` tensor into non-overlapping blocks of `chunk_size` along the sequence dim."""
        batch_size, seq_len, num_heads, head_dim = hidden_states.shape
        num_blocks = (seq_len + self.chunk_size - 1) // self.chunk_size
        pad = num_blocks * self.chunk_size - seq_len
        hidden_states = F.pad(hidden_states, (0, 0, 0, 0, 0, pad))
        return hidden_states.reshape(batch_size, num_blocks, self.chunk_size, num_heads, head_dim).contiguous()

    def _extract_block_context(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """Extracts overlapping context windows of `context_size` for every block, strided by `chunk_size`."""
        batch_size, seq_len, num_heads, head_dim = hidden_states.shape
        hidden_states = F.pad(
            hidden_states, (0, 0, 0, 0, self.max_past_horizon, self.max_future_horizon + self.chunk_size - 1)
        )
        hidden_states = hidden_states.unfold(1, self.context_size, self.chunk_size)
        hidden_states = torch.movedim(hidden_states, -1, 2)
        return hidden_states.contiguous()

    def _rel_shift(self, x: torch.Tensor) -> torch.Tensor:
        """Relative position shift for blocked attention. See appendix B of https://huggingface.co/papers/1901.02860."""
        batch_size, num_heads, num_blocks, block_size, position_length = x.shape
        context_size = self.context_size
        x = F.pad(x, (0, context_size + 1 - position_length))
        x = x.view(batch_size, num_heads, num_blocks, block_size * (context_size + 1))
        x = x[..., : block_size * context_size]
        return x.view(batch_size, num_heads, num_blocks, block_size, context_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: torch.Tensor,
        attention_mask: torch.BoolTensor | None = None,
    ) -> tuple[torch.Tensor, None]:
        batch_size, seq_length, _ = hidden_states.shape
        hidden_shape = (batch_size, seq_length, self.num_heads, self.head_dim)

        query_states = self.q_proj(hidden_states).float().view(hidden_shape)
        key_states = self.k_proj(hidden_states).float().view(hidden_shape)
        value_states = self.v_proj(hidden_states).float().view(hidden_shape)

        query_states = query_states * self.q_scale * F.softplus(self.per_dim_scale)
        key_states = key_states * self.k_scale

        query_states = self._convert_to_block(query_states)
        key_states = self._extract_block_context(key_states)
        value_states = self._extract_block_context(value_states)
        num_blocks = query_states.shape[1]

        relative_key_states = self.relative_k_proj(position_embeddings)
        relative_key_states = relative_key_states.view(-1, self.num_heads, self.head_dim)
        relative_key_states = relative_key_states.to(dtype=query_states.dtype)

        queries = query_states.permute(0, 3, 1, 2, 4)
        matrix_ac = queries @ key_states.permute(0, 3, 1, 4, 2)

        queries_flat = queries.reshape(batch_size, self.num_heads, -1, self.head_dim)
        matrix_bd = queries_flat @ relative_key_states.permute(1, 2, 0)
        matrix_bd = matrix_bd.reshape(batch_size, self.num_heads, num_blocks, self.chunk_size, -1)
        matrix_bd = self._rel_shift(matrix_bd)

        attn_weights = matrix_ac + matrix_bd
        attn_weights = attn_weights / self.softcap
        attn_weights = torch.tanh(attn_weights)
        attn_weights = attn_weights * self.softcap

        if attention_mask is not None:
            attn_weights = attn_weights.masked_fill(
                attention_mask.logical_not(), self.config.attention_invalid_logits_value
            )

        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
        attn_output = attn_weights @ value_states.permute(0, 3, 1, 2, 4)
        attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, num_blocks * self.chunk_size, -1)
        attn_output = attn_output[:, :seq_length].contiguous()
        attn_output = self.post(attn_output.to(hidden_states.dtype))

        return attn_output, attn_weights


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()

    def forward(self, hidden_states: torch.Tensor, mask: torch.Tensor | None = None):
        if mask is not None:
            mask = mask.to(device=hidden_states.device)
            hidden_states = hidden_states * mask[:, None, :, None]

        hidden_states = self.conv(hidden_states.to(self.conv.weight.dtype))
        hidden_states = self.act(self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2).contiguous())

        if mask is not None:
            mask = mask[:, ::2]

        return hidden_states, mask


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]
        self.input_proj_linear = nn.Linear(proj_input_dim, config.hidden_size, bias=False)

    def forward(
        self,
        input_features: torch.Tensor,
        input_features_mask: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        hidden_states = input_features.unsqueeze(1)
        hidden_states, mask = self.layer0(hidden_states, input_features_mask)
        hidden_states, mask = self.layer1(hidden_states, mask)

        batch_size, _, seq_len, _ = hidden_states.shape
        hidden_states = hidden_states.permute(0, 2, 3, 1).contiguous().reshape(batch_size, seq_len, -1)
        return self.input_proj_linear(hidden_states), mask


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

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # This is needed to avoid any underflow/overflow issues when clipping
        gradient_clipping = min(self.gradient_clipping, torch.finfo(hidden_states.dtype).max)

        residual = hidden_states
        hidden_states = torch.clamp(hidden_states, -gradient_clipping, gradient_clipping)
        hidden_states = self.pre_layer_norm(hidden_states)

        hidden_states = self.ffw_layer_1(hidden_states)
        hidden_states = self.act_fn(hidden_states)
        hidden_states = self.ffw_layer_2(hidden_states)

        hidden_states = torch.clamp(hidden_states, -gradient_clipping, gradient_clipping)
        hidden_states = self.post_layer_norm(hidden_states)
        hidden_states *= self.post_layer_scale
        hidden_states += residual

        return hidden_states


# TODO: this could be imported from Voxtral realtime
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

    @cached_property
    def left_pad(self):
        effective_kernel_size = (self.kernel_size[0] - 1) * self.dilation[0] + 1
        return effective_kernel_size - self.stride[0]

    def forward(
        self,
        x: torch.Tensor,
        # padding_cache: VoxtralRealtimeConv1dPaddingCache | None = None,  # TODO: we might want to add a cache?
    ) -> torch.Tensor:
        # if padding_cache is not None:
        #     x = padding_cache.update(x, self.cache_key, self)
        # else:
        #     x = nn.functional.pad(x, (self.left_pad, 0))
        x = nn.functional.pad(x, (self.left_pad, 0))

        return super().forward(x)


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,
        )

        self.pre_layer_norm = Gemma4RMSNorm(config.hidden_size, eps=config.rms_norm_eps, with_scale=True)
        self.conv_norm = Gemma4RMSNorm(config.hidden_size, eps=config.rms_norm_eps, with_scale=True)
        self.act_fn = ACT2FN[config.hidden_act]

        self.gradient_clipping = config.gradient_clipping

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        residual = hidden_states

        hidden_states = self.pre_layer_norm(hidden_states)
        hidden_states = self.linear_start(hidden_states)
        hidden_states = nn.functional.glu(hidden_states, dim=-1)

        hidden_states = self.depthwise_conv1d(hidden_states.transpose(1, 2)).transpose(1, 2)

        # This is needed to avoid any underflow/overflow issues when clipping
        gradient_clipping = min(self.gradient_clipping, torch.finfo(hidden_states.dtype).max)
        hidden_states = torch.clamp(hidden_states, -gradient_clipping, gradient_clipping)
        hidden_states = self.conv_norm(hidden_states)

        hidden_states = self.act_fn(hidden_states)
        hidden_states = self.linear_end(hidden_states)
        hidden_states += residual
        return hidden_states


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)

        self.gradient_clipping = config.gradient_clipping

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.BoolTensor | None,
        position_embeddings: torch.Tensor,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        # This is needed to avoid any underflow/overflow issues when clipping
        gradient_clipping = min(self.gradient_clipping, torch.finfo(self.norm_pre_attn.weight.dtype).max)

        hidden_states = self.feed_forward1(hidden_states)
        residual = hidden_states

        hidden_states = torch.clamp(hidden_states, -gradient_clipping, gradient_clipping)
        hidden_states = self.norm_pre_attn(hidden_states)

        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
        )

        hidden_states = torch.clamp(hidden_states, -gradient_clipping, gradient_clipping)
        hidden_states = self.norm_post_attn(hidden_states)
        hidden_states += residual

        hidden_states = self.lconv1d(hidden_states)
        hidden_states = self.feed_forward2(hidden_states)

        hidden_states = torch.clamp(hidden_states, -gradient_clipping, gradient_clipping)
        hidden_states = self.norm_out(hidden_states)

        return hidden_states


# ---- Vision Encoder Layers ----


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:
        """Compute 2-D patch position embeddings via embedding lookup.

        ``pixel_position_ids`` has shape ``(batch, num_patches, 2)`` where the
        last dimension holds (x, y) indices into ``position_embedding_table``
        (shape ``(2, position_embedding_size, hidden_size)``).  The result is the
        sum of the x- and y-embeddings for each patch.
        """
        # Clamp only the lower bound: negative values encode padding and must be
        # mapped to a valid index before lookup (padding embeddings are zeroed
        # out unconditionally below).  Valid positions are always in-range by
        # construction, so no upper-bound clamping is needed.
        clamped_positions = pixel_position_ids.clamp(min=0)
        # position_embedding_table: (2, position_embedding_size, hidden_size)
        # clamped_positions[..., 0]: (batch, num_patches) — x indices
        # clamped_positions[..., 1]: (batch, num_patches) — y indices
        x_emb = F.embedding(clamped_positions[..., 0], self.position_embedding_table[0])
        y_emb = F.embedding(clamped_positions[..., 1], self.position_embedding_table[1])
        position_embeddings = x_emb + y_emb
        position_embeddings = torch.where(padding_positions.unsqueeze(-1), 0.0, position_embeddings)
        return position_embeddings

    def forward(
        self, pixel_values: torch.Tensor, pixel_position_ids: torch.Tensor, padding_positions: torch.Tensor
    ) -> torch.Tensor:
        # Gemma4 applies no normalization and instead scales in model code
        pixel_values = 2 * (pixel_values - 0.5)
        hidden_states = self.input_proj(pixel_values.to(self.input_proj.weight.dtype))
        position_embeddings = self._position_embeddings(pixel_position_ids, padding_positions)
        return hidden_states + position_embeddings


class Gemma4VisionPooler(nn.Module):
    """Spatial pooling and ``sqrt(hidden_size)`` scaling for vision encodings.

    The scaling expands the activation magnitude, which can exceed the float16 range, so it is
    computed in float32 and the pooled features are returned in float32. The caller
    (``Gemma4VisionModel.forward``) standardizes them and casts back to the working dtype.
    """

    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
        input and output lengths
        """
        input_seq_len = hidden_states.shape[1]
        k = int((input_seq_len // length) ** 0.5)
        k_squared = k**2
        if k_squared * length != input_seq_len:
            raise ValueError(
                f"Cannot pool {hidden_states.shape} to {length}: {k=}^2 times {length=} must be {input_seq_len}."
            )

        # Clamp padding positions (which are -1) to 0 so they don't break one_hot.
        # Padding patches have zero hidden states so they contribute nothing to the average.
        clamped_positions = pixel_position_ids.clamp(min=0)
        max_x = clamped_positions[..., 0].max(dim=-1, keepdim=True)[0] + 1
        kernel_idxs = torch.div(clamped_positions, k, rounding_mode="floor")
        kernel_idxs = kernel_idxs[..., 0] + (max_x // k) * kernel_idxs[..., 1]
        weights = F.one_hot(kernel_idxs.long(), length).float() / k_squared
        output = weights.transpose(1, 2) @ hidden_states.float()
        mask = torch.logical_not((weights == 0).all(dim=1))
        return output.to(hidden_states.dtype), mask

    def forward(
        self,
        hidden_states: torch.Tensor,
        pixel_position_ids: torch.Tensor,
        padding_positions: torch.Tensor,
        output_length: int | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if output_length > hidden_states.shape[1]:
            raise ValueError(
                f"Cannot output more soft tokens (requested {output_length}) than there are patches"
                f" ({hidden_states.shape[1]}). Change the value of `num_soft_tokens` when processing."
            )

        hidden_states = hidden_states.masked_fill(padding_positions.unsqueeze(-1), 0.0)

        if hidden_states.shape[1] != output_length:
            hidden_states, padding_positions = self._avg_pool_by_positions(
                hidden_states, pixel_position_ids, output_length
            )

        # Scale in float32 and return float32: the sqrt(hidden_size) scaling can push the
        # activations past the float16 range (max 65504), so the magnitude is kept in float32
        # until the caller standardizes it.
        hidden_states = hidden_states.float() * self.root_hidden_size
        return hidden_states, padding_positions


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]
        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: Gemma4VisionConfig | None = None,
        device: torch.device | None = 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

        # The reference implementation computes RoPE frequencies INDEPENDENTLY
        # for each spatial dimension using the partitioned head_dim (head_dim // ndim),
        # so both x and y dimensions get identical frequency ranges.
        # This is different from splitting the global inv_freq between dimensions.
        spatial_dim = dim // 2

        attention_factor = 1.0  # Unused in this type of RoPE
        inv_freq = 1.0 / (
            base
            ** (torch.arange(0, spatial_dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / spatial_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)
        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"

        # Multidimensional positions: [batch, num_patches, ndim]. Apply rotations to each spatial dim separately
        all_cos, all_sin = [], []
        for i in range(2):
            dim_position_ids = position_ids[:, :, i]
            dim_position_ids_expanded = dim_position_ids[:, None, :].float()

            with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
                freqs = (inv_freq_expanded.float() @ dim_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
            all_cos.append(cos)
            all_sin.append(sin)

        cos = torch.cat(all_cos, dim=-1).to(dtype=x.dtype)
        sin = torch.cat(all_sin, dim=-1).to(dtype=x.dtype)
        return cos, sin


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.
        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)
    return (x * cos) + (rotate_half(x) * sin)


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,
    dropout: float | int = 0.0,
    scaling: float | None = None,
    softcap: float | None = None,
    **kwargs,
) -> tuple[torch.Tensor, torch.Tensor]:
    if scaling is None:
        scaling = module.head_dim**-0.5

    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 softcap is not None:
        attn_weights = attn_weights / softcap
        attn_weights = torch.tanh(attn_weights)
        attn_weights = attn_weights * softcap
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    # upcast attention to fp32
    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


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*):
            If position_ids.ndim + 2 == x.ndim, then this function passes through to `apply_rotary_pos_emb()`.
            Otherwise, position_ids is used to split the inputs, x, into multiple pieces, where each piece is fed to
            `apply_rotary_pos_emb()`, and then concatenated back together.
        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:
      Tensor of shape [B, L, N, H] with RoPE applied.
    """
    ndim = position_ids.shape[-1]
    num_input_channels = x.shape[-1]
    num_rotated_channels_per_dim = 2 * (num_input_channels // (2 * ndim))

    if num_rotated_channels_per_dim <= 0:
        raise ValueError(
            "Invalid configuration: num_rotated_channels_per_dim must be > 0, got"
            f" {num_rotated_channels_per_dim} (num_input_channels={num_input_channels},"
            f" ndim={ndim})"
        )

    # Correctly split the input tensor into ndim parts
    split_sizes = [num_rotated_channels_per_dim] * ndim
    x_parts = torch.split(x, split_sizes, dim=-1)
    cos_parts = torch.split(cos, split_sizes, dim=-1)
    sin_parts = torch.split(sin, split_sizes, dim=-1)
    y_parts = [
        apply_rotary_pos_emb(
            x=x_parts[k],
            cos=cos_parts[k],
            sin=sin_parts[k],
            unsqueeze_dim=unsqueeze_dim,
        )
        for k in range(ndim)
    ]
    return torch.cat(y_parts, dim=-1)


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
        self.q_proj = Gemma4ClippableLinear(config, config.hidden_size, config.num_attention_heads * self.head_dim)
        self.k_proj = Gemma4ClippableLinear(config, config.hidden_size, config.num_key_value_heads * self.head_dim)
        self.v_proj = Gemma4ClippableLinear(config, config.hidden_size, config.num_key_value_heads * self.head_dim)
        self.o_proj = Gemma4ClippableLinear(config, config.num_attention_heads * self.head_dim, config.hidden_size)

        self.q_norm = Gemma4RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
        self.k_norm = Gemma4RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
        self.v_norm = Gemma4RMSNorm(self.head_dim, eps=config.rms_norm_eps, with_scale=False)

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

        cos, sin = position_embeddings

        query_states = self.q_proj(hidden_states).view(hidden_shape)
        query_states = self.q_norm(query_states)
        query_states = apply_multidimensional_rope(query_states, cos, sin, position_ids)
        query_states = query_states.transpose(1, 2)

        key_states = self.k_proj(hidden_states).view(hidden_shape)
        key_states = self.k_norm(key_states)
        key_states = apply_multidimensional_rope(key_states, cos, sin, position_ids)
        key_states = key_states.transpose(1, 2)

        value_states = self.v_proj(hidden_states).view(hidden_shape)
        value_states = self.v_norm(value_states)
        value_states = value_states.transpose(1, 2)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=self.attention_dropout if self.training else 0.0,
            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 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(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: torch.Tensor = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            position_ids=position_ids,
            **kwargs,
        )
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.pre_feedforward_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_feedforward_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states


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,
        pixel_position_ids: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        r"""
        pixel_position_ids (torch.Tensor):
            Patch positions as (x, y) coordinates in the image as [batch, num_patches, 2].
        """
        attention_mask = create_bidirectional_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
        )

        # embed positions
        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, pixel_position_ids)

        # decoder layers
        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=attention_mask,
                position_embeddings=position_embeddings,
                position_ids=pixel_position_ids,
                **kwargs,
            )

        return BaseModelOutputWithPast(last_hidden_state=hidden_states)


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]

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


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:
            rope_params = self.config.rope_parameters[layer_type]
            if rope_params is None:
                continue

            if (rope_type := rope_params["rope_type"]) != "default":
                rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type]
            else:
                rope_init_fn = self.compute_default_rope_parameters

            self.rope_init_fns[layer_type] = rope_init_fn
            self.rope_type[layer_type] = rope_type

            rope_init_fn_kwargs = {"device": device, "layer_type": layer_type}
            if layer_type == "full_attention" and rope_type == "proportional":
                rope_init_fn_kwargs["head_dim_key"] = "global_head_dim"

            curr_inv_freq, curr_attention_scaling = rope_init_fn(self.config, **rope_init_fn_kwargs)
            self.register_buffer(f"{layer_type}_inv_freq", curr_inv_freq, persistent=False)
            self.register_buffer(f"{layer_type}_original_inv_freq", curr_inv_freq.clone(), persistent=False)
            setattr(self, f"{layer_type}_attention_scaling", curr_attention_scaling)

    @staticmethod
    def compute_default_rope_parameters(
        config: Gemma4TextConfig | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
        layer_type: str | 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.
            layer_type (`str`, *optional*):
                The current layer type if the model has different RoPE parameters per type.
                Should not be used unless `config.layer_types is not None`

        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).
        """
        # For backward compatibility standardize the `rope_parameters_dict` if it uses old format
        base = config.rope_parameters[layer_type]["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, layer_type=None):
        inv_freq = getattr(self, f"{layer_type}_inv_freq")
        attention_scaling = getattr(self, f"{layer_type}_attention_scaling")

        inv_freq_expanded = 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() * attention_scaling
            sin = emb.sin() * attention_scaling

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


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
        num_key_value_heads = (
            config.num_global_key_value_heads if self.use_alternative_attention else config.num_key_value_heads
        )
        self.num_key_value_groups = config.num_attention_heads // num_key_value_heads
        self.scaling = 1.0
        self.attention_dropout = self.config.attention_dropout
        self.is_causal = config.use_bidirectional_attention != "all"

        # Shared kv cache
        first_kv_shared_layer_idx = self.config.num_hidden_layers - getattr(self.config, "num_kv_shared_layers", 0)
        self.is_kv_shared_layer = layer_idx >= first_kv_shared_layer_idx >= 0
        prev_layers = config.layer_types[:first_kv_shared_layer_idx]
        self.store_full_length_kv = not self.is_kv_shared_layer and layer_idx == len(prev_layers) - 1 - prev_layers[
            ::-1
        ].index(config.layer_types[layer_idx])

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.q_norm = Gemma4RMSNorm(dim=self.head_dim, eps=config.rms_norm_eps)

        # Layers sharing kv states don't need any weight matrices
        if not self.is_kv_shared_layer:
            self.k_norm = Gemma4RMSNorm(dim=self.head_dim, eps=config.rms_norm_eps)
            self.v_norm = Gemma4RMSNorm(self.head_dim, eps=config.rms_norm_eps, with_scale=False)

            self.k_proj = nn.Linear(
                config.hidden_size, num_key_value_heads * self.head_dim, bias=config.attention_bias
            )
            self.v_proj = (
                nn.Linear(config.hidden_size, num_key_value_heads * self.head_dim, bias=config.attention_bias)
                if not self.use_alternative_attention
                else None
            )

        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: torch.Tensor,
        attention_mask: torch.Tensor | None,
        shared_kv_states: dict[str, tuple[torch.Tensor, torch.Tensor]],
        past_key_values: Cache | None = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        cos, sin = position_embeddings

        query_states = self.q_proj(hidden_states).view(hidden_shape)
        query_states = self.q_norm(query_states)
        query_states = apply_rotary_pos_emb(query_states, cos, sin, unsqueeze_dim=2)
        query_states = query_states.transpose(1, 2)

        # For layers with shared KV (from kv sharing point onwards), we reuse the same keys/values states as the last non-sharing layer.
        # We cannot simply reuse the cached state if we have a Cache, as sliding layers will not remember the full states in their Cache
        # once we are past the sliding window - so we always use `shared_kv_states` instead, even when past_key_values is not None
        if self.is_kv_shared_layer:
            key_states, value_states = shared_kv_states[self.layer_type]
            # Device of past layer may be different from current one
            key_states = key_states.to(query_states.device)
            value_states = value_states.to(query_states.device)
        else:
            key_states = self.k_proj(hidden_states).view(hidden_shape)
            value_states = self.v_proj(hidden_states).view(hidden_shape) if self.v_proj is not None else key_states

            key_states = self.k_norm(key_states)
            key_states = apply_rotary_pos_emb(key_states, cos, sin, unsqueeze_dim=2)
            key_states = key_states.transpose(1, 2)

            value_states = self.v_norm(value_states)
            value_states = value_states.transpose(1, 2)

        if past_key_values is not None and not self.is_kv_shared_layer:
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
        if self.store_full_length_kv:
            shared_kv_states[self.layer_type] = key_states, value_states

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=self.attention_dropout if self.training else 0.0,
            scaling=self.scaling,
            sliding_window=self.sliding_window,
            **kwargs,
        )

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


@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(
        self,
        hidden_states: torch.Tensor,
        top_k_index: torch.Tensor,
        top_k_weights: torch.Tensor,
    ) -> torch.Tensor:
        final_hidden_states = torch.zeros_like(hidden_states)
        with torch.no_grad():
            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
            expert_mask = expert_mask.permute(2, 1, 0)
            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()

        for expert_idx in expert_hit:
            expert_idx = expert_idx[0]
            if expert_idx == self.num_experts:
                continue
            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
            current_state = hidden_states[token_idx]
            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
            current_hidden_states = self.act_fn(gate) * up
            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))

        return final_hidden_states


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]:
        hidden_states = self.norm(hidden_states)
        hidden_states = hidden_states * self.scale * self.scalar_root_size

        expert_scores = self.proj(hidden_states)  # [B*S, E]
        router_probabilities = nn.functional.softmax(expert_scores, dim=-1)

        # topk returns both values (probabilities) and indices directly
        top_k_weights, top_k_index = torch.topk(
            router_probabilities,
            k=self.config.top_k_experts,
            dim=-1,
        )  # both [B*S, K]

        # Normalize the top-k weights so they sum to 1 per token
        top_k_weights /= top_k_weights.sum(dim=-1, keepdim=True)

        # Apply per-expert scale directly to the weights
        top_k_weights = top_k_weights * self.per_expert_scale[top_k_index]

        return router_probabilities, top_k_weights, top_k_index


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))

        self.hidden_size_per_layer_input = config.hidden_size_per_layer_input
        if self.hidden_size_per_layer_input:
            self.act_fn = ACT2FN[config.hidden_activation]
            self.per_layer_input_gate = nn.Linear(self.hidden_size, self.hidden_size_per_layer_input, bias=False)
            self.per_layer_projection = nn.Linear(self.hidden_size_per_layer_input, self.hidden_size, bias=False)
            self.post_per_layer_input_norm = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)

        self.enable_moe_block = config.enable_moe_block
        if self.enable_moe_block:
            self.router = Gemma4TextRouter(config)
            self.experts = Gemma4TextExperts(config)
            self.post_feedforward_layernorm_1 = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
            self.post_feedforward_layernorm_2 = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
            self.pre_feedforward_layernorm_2 = Gemma4RMSNorm(self.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        per_layer_input: torch.Tensor = None,
        shared_kv_states: dict[str, tuple[torch.Tensor, torch.Tensor]] | None = None,
        position_embeddings: torch.Tensor = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        **kwargs,
    ) -> torch.Tensor:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            shared_kv_states=shared_kv_states,
            position_ids=position_ids,
            past_key_values=past_key_values,
            **kwargs,
        )
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.pre_feedforward_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)

        if self.enable_moe_block:
            hidden_states_1 = self.post_feedforward_layernorm_1(hidden_states)

            # Take hidden states before MLP here
            hidden_states_flat = residual.reshape(-1, residual.shape[-1])
            _, top_k_weights, top_k_index = self.router(hidden_states_flat)
            hidden_states_2 = self.pre_feedforward_layernorm_2(hidden_states_flat)
            hidden_states_2 = self.experts(hidden_states_2, top_k_index, top_k_weights)
            hidden_states_2 = hidden_states_2.reshape(residual.shape)
            hidden_states_2 = self.post_feedforward_layernorm_2(hidden_states_2)

            # Combine mlp and moe outputs
            hidden_states = hidden_states_1 + hidden_states_2

        hidden_states = self.post_feedforward_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        if self.hidden_size_per_layer_input:
            residual = hidden_states
            hidden_states = self.per_layer_input_gate(hidden_states)
            hidden_states = self.act_fn(hidden_states)
            hidden_states = hidden_states * per_layer_input
            hidden_states = self.per_layer_projection(hidden_states)
            hidden_states = self.post_per_layer_input_norm(hidden_states)
            hidden_states = residual + hidden_states

        hidden_states *= self.layer_scalar
        return hidden_states


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",
        "Gemma4VisionPatchEmbedder",
        "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
    input_modalities = ("image", "text", "video", "audio")

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if isinstance(module, Gemma4VisionPatchEmbedder):
            init.ones_(module.position_embedding_table)
        elif isinstance(module, Gemma4AudioRelPositionalEncoding):
            min_timescale = 1.0
            max_timescale = 10000.0
            num_timescales = module.hidden_size // 2
            log_timescale_increment = math.log(max_timescale / min_timescale) / max(num_timescales - 1, 1)
            inv_timescales = min_timescale * torch.exp(torch.arange(num_timescales) * -log_timescale_increment)
            init.copy_(module.inv_timescales, inv_timescales.unsqueeze(0).unsqueeze(0))
        elif isinstance(module, Gemma4AudioAttention):
            init.constant_(module.softcap, module.attention_logits_soft_cap)
            init.zeros_(module.per_dim_scale)
        elif isinstance(module, Gemma4TextRotaryEmbedding):
            for layer_type, rope_init_fn in module.rope_init_fns.items():
                rope_init_fn_kwargs = {"layer_type": layer_type}
                if layer_type == "full_attention" and module.rope_type[layer_type] == "proportional":
                    rope_init_fn_kwargs["head_dim_key"] = "global_head_dim"

                curr_inv_freq, _ = rope_init_fn(module.config, **rope_init_fn_kwargs)
                init.copy_(getattr(module, f"{layer_type}_inv_freq"), curr_inv_freq)
                init.copy_(getattr(module, f"{layer_type}_original_inv_freq"), curr_inv_freq)
        elif isinstance(module, Gemma4VisionRotaryEmbedding):
            rope_fn = (
                ROPE_INIT_FUNCTIONS[module.rope_type]
                if module.rope_type != "default"
                else module.compute_default_rope_parameters
            )
            buffer_value, _ = rope_fn(module.config)
            init.copy_(module.inv_freq, buffer_value)
            init.copy_(module.original_inv_freq, buffer_value)
        elif isinstance(module, Gemma4TextScaledWordEmbedding):
            init.constant_(module.embed_scale, module.scalar_embed_scale)
        elif isinstance(module, Gemma4TextRouter):
            init.ones_(module.scale)
            init.ones_(module.per_expert_scale)
        elif isinstance(module, Gemma4TextExperts):
            std = self.config.initializer_range
            init.normal_(module.gate_up_proj, mean=0.0, std=std)
            init.normal_(module.down_proj, mean=0.0, std=std)
        elif isinstance(module, Gemma4TextDecoderLayer):
            init.ones_(module.layer_scalar)
        elif isinstance(module, Gemma4ClippableLinear) and module.use_clipped_linears:
            init.constant_(module.input_min, -float("inf"))
            init.constant_(module.input_max, float("inf"))
            init.constant_(module.output_min, -float("inf"))
            init.constant_(module.output_max, float("inf"))
        elif isinstance(module, Gemma4VisionModel) and module.config.standardize:
            init.zeros_(module.std_bias)
            init.ones_(module.std_scale)

    def get_per_layer_input_embeddings(self):
        return self.base_model.embed_tokens_per_layer

    def set_per_layer_input_embeddings(self, value):
        self.base_model.embed_tokens_per_layer = value

    def resize_token_embeddings(
        self,
        new_num_tokens: int | None = None,
        pad_to_multiple_of: int | None = None,
        mean_resizing: bool = True,
    ) -> nn.Embedding:
        inputs_embeds = super().resize_token_embeddings(
            new_num_tokens=new_num_tokens,
            pad_to_multiple_of=pad_to_multiple_of,
            mean_resizing=mean_resizing,
        )
        self._resize_per_layer_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
        return inputs_embeds

    def _resize_per_layer_embeddings(
        self,
        new_num_tokens: int | None = None,
        pad_to_multiple_of: int | None = None,
        mean_resizing: bool = True,
    ):
        self.config.get_text_config().vocab_size_per_layer_input = self.vocab_size
        if self.config.get_text_config().hidden_size_per_layer_input:
            embed_tokens_per_layer = self.get_per_layer_input_embeddings()
            new_embeddings_per_layer = self._get_resized_embeddings(
                embed_tokens_per_layer, new_num_tokens, pad_to_multiple_of, mean_resizing
            )
            if hasattr(embed_tokens_per_layer, "_hf_hook"):
                hook = embed_tokens_per_layer._hf_hook
                add_hook_to_module(new_embeddings_per_layer, hook)
            new_embeddings_per_layer.requires_grad_(embed_tokens_per_layer.weight.requires_grad)
            self.set_per_layer_input_embeddings(new_embeddings_per_layer)


@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

        # Gemma4 downcasts the below to bfloat16, causing sqrt(3072)=55.4256 to become 55.5. See https://github.com/huggingface/transformers/pull/29402
        self.embed_tokens = Gemma4TextScaledWordEmbedding(
            config.vocab_size, config.hidden_size, self.padding_idx, embed_scale=self.config.hidden_size**0.5
        )
        self.layers = nn.ModuleList(
            [Gemma4TextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = Gemma4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = Gemma4TextRotaryEmbedding(config)
        self.gradient_checkpointing = False
        self.unique_layer_types = set(self.config.layer_types)

        # Per-Layer Embeddings (PLE): auxiliary embedding that feeds a residual signal
        # into each decoder layer. See `get_per_layer_inputs()` and `project_per_layer_inputs()`
        # for the full pipeline. The embedding is packed: total dim = num_layers * per_layer_dim.
        self.hidden_size_per_layer_input = config.hidden_size_per_layer_input
        if self.hidden_size_per_layer_input:
            self.embed_tokens_per_layer = Gemma4TextScaledWordEmbedding(
                config.vocab_size_per_layer_input,
                config.num_hidden_layers * config.hidden_size_per_layer_input,
                self.padding_idx,
                embed_scale=config.hidden_size_per_layer_input**0.5,
            )
            self.per_layer_input_scale = 2.0**-0.5
            self.per_layer_model_projection = nn.Linear(
                config.hidden_size,
                config.num_hidden_layers * config.hidden_size_per_layer_input,
                bias=False,
            )
            self.per_layer_model_projection_scale = config.hidden_size**-0.5
            self.per_layer_projection_norm = Gemma4RMSNorm(config.hidden_size_per_layer_input, eps=config.rms_norm_eps)

        # Update `_keys_to_ignore_on_load_unexpected` to drop all k/v proj and norms for the shared layers
        self._keys_to_ignore_on_load_unexpected = []
        for i, layer in enumerate(self.layers):
            if layer.self_attn.is_kv_shared_layer:
                self._keys_to_ignore_on_load_unexpected.extend(
                    [f"layers.{i}.self_attn.{name}" for name in ("k_proj", "v_proj", "k_norm", "v_norm")]
                )

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

    @merge_with_config_defaults
    @capture_outputs
    @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,
        per_layer_inputs: torch.Tensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Gemma4TextModelOutputWithPast:
        r"""
        per_layer_inputs (`torch.Tensor`, *optional*):
            Pre-computed per-layer input text embeddings of shape `(batch_size, sequence_length, num_hidden_layers,
            hidden_size_per_layer_input)`. When provided, these are used directly instead of being computed from `input_ids`
            via `get_per_layer_inputs()` in the text model. If calling the `forward` with `inputs_embeds` instead of `input_ids`,
            you should probably precompute them and forward them along `inputs_embeds`, otherwise recomputing them needs
            to reverse the main embedding, which is expensive.
        """
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if input_ids is not None and per_layer_inputs is not None:
            raise ValueError("You cannot specify per_layer_inputs if input_ids is provided")

        if input_ids is not None:
            inputs_embeds = self.embed_tokens(input_ids)

        if self.hidden_size_per_layer_input:
            if per_layer_inputs is None:
                per_layer_inputs = self.get_per_layer_inputs(input_ids, inputs_embeds)
            per_layer_inputs = self.project_per_layer_inputs(inputs_embeds, per_layer_inputs)

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

        if position_ids is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            position_ids = position_ids.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,
                "inputs_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            # Create the masks
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        # embed positions
        hidden_states = inputs_embeds
        position_embeddings = {}
        for layer_type in self.unique_layer_types:
            position_embeddings[layer_type] = self.rotary_emb(hidden_states, position_ids, layer_type)

        # Initialize as empty dict, or reuse past shared states. We use a UserDict instead of built-in dict (it behaves
        # the same) for fsdp2 support (otherwise, `_apply_to_tensors` rebuilds every dict it recurses into, and `shared_kv_states`
        # is not correctly shared, see https://github.com/pytorch/pytorch/blob/v2.10.0/torch/distributed/utils.py#L223-L255)
        shared_kv_states = kwargs.pop("shared_kv_states", UserDict())

        # decoder layers
        for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
            per_layer_input = per_layer_inputs[:, :, i, :] if per_layer_inputs is not None else None

            hidden_states = decoder_layer(
                hidden_states,
                per_layer_input,
                shared_kv_states=shared_kv_states,
                position_embeddings=position_embeddings[self.config.layer_types[i]],
                attention_mask=causal_mask_mapping[self.config.layer_types[i]],
                position_ids=position_ids,
                past_key_values=past_key_values,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)

        return Gemma4TextModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            shared_kv_states=shared_kv_states if kwargs.get("return_shared_kv_states", False) else None,
        )

    def get_per_layer_inputs(self, input_ids: torch.Tensor | None, inputs_embeds: torch.Tensor | None) -> torch.Tensor:
        """Compute the token-identity component of Per-Layer Embeddings (PLE).

        Looks up `input_ids` in `embed_tokens_per_layer` (a scaled embedding that multiplies
        by `sqrt(hidden_size_per_layer_input)`) and reshapes the packed output from
        `[batch, seq, num_hidden_layers * hidden_size_per_layer_input]` to
        `[batch, seq, num_hidden_layers, hidden_size_per_layer_input]`.

        If only `inputs_embeds` is provided (no `input_ids`), reverses the main embedding
        to recover `input_ids` for the PLE lookup.
        """
        if not self.hidden_size_per_layer_input:
            raise RuntimeError(
                "Attempting to call get_per_layer_inputs() from a model initialized with a config that does not support"
                f" per-layer embeddings. {self.config}"
            )

        # If only inputs_embeds are provided, reverse main embedding to find the input_ids - this allows to `generate`
        # from `inputs_embeds` only as other models (otherwise it would need the value from both embeddings)
        if input_ids is None:
            with torch.no_grad():
                input_ids = (
                    (
                        inputs_embeds[:, :, None, :]
                        == self.embed_tokens.weight[None, None, :, :] * self.config.hidden_size**0.5
                    )
                    .all(dim=3)
                    .nonzero()[:, 2]
                )
                try:
                    input_ids = input_ids.view(inputs_embeds.shape[:2])
                except RuntimeError:
                    raise RuntimeError(
                        "It seems like you tried to call `forward` from `inputs_embeds` without providing `input_ids`, and that "
                        "the `inputs_embeds` you provided do not exactly match the embedding weights. Since Gemma4 needs to reverse "
                        "the embedding to compute another embedding, make sure you provide exact `inputs_embeds`"
                    )

        return self.embed_tokens_per_layer(input_ids).reshape(
            *input_ids.shape,
            self.config.num_hidden_layers,
            self.hidden_size_per_layer_input,
        )

    def project_per_layer_inputs(
        self,
        inputs_embeds: torch.Tensor,
        per_layer_inputs: torch.Tensor | None = None,
    ) -> torch.Tensor:
        """Compute the context-aware component of PLE and combine with token-identity.

        Projects `inputs_embeds` through `per_layer_model_projection` (Linear), scales by
        `1/sqrt(hidden_size)`, reshapes to `[batch, seq, num_layers, ple_dim]`, and normalizes
        with `per_layer_projection_norm` (RMSNorm).

        If `per_layer_inputs` (the token-identity component from `get_per_layer_inputs()`)
        is provided, combines both: `(context_projection + token_identity) * (1/sqrt(2))`.
        If `per_layer_inputs` is None (e.g. for multimodal inputs where input_ids are not
        available), returns just the context projection.
        """
        if not self.hidden_size_per_layer_input:
            raise RuntimeError(
                "Attempting to call project_per_layer_inputs() from a model initialized with a config that does not"
                f" support per-layer embeddings. {self.config}"
            )

        per_layer_projection = self.per_layer_model_projection(inputs_embeds) * self.per_layer_model_projection_scale
        per_layer_projection = per_layer_projection.reshape(
            *inputs_embeds.shape[:-1],
            self.config.num_hidden_layers,
            self.hidden_size_per_layer_input,
        )
        per_layer_projection = self.per_layer_projection_norm(per_layer_projection)

        if per_layer_inputs is None:
            return per_layer_projection

        return (per_layer_projection + per_layer_inputs) * self.per_layer_input_scale


@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)

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

    @can_return_tuple
    @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,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        per_layer_inputs: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Gemma4CausalLMOutputWithPast:
        r"""
        per_layer_inputs (`torch.Tensor`, *optional*):
            Pre-computed per-layer input text embeddings of shape `(batch_size, sequence_length, num_hidden_layers,
            hidden_size_per_layer_input)`. When provided, these are used directly instead of being computed from `input_ids`
            via `get_per_layer_inputs()` in the text model. If calling the `forward` with `inputs_embeds` instead of `input_ids`,
            you should probably precompute them and forward them along `inputs_embeds`, otherwise recomputing them needs
            to reverse the main embedding, which is expensive.

        Example:

        ```python
        >>> from transformers import AutoTokenizer, Gemma4ForCausalLM

        >>> model = Gemma4ForCausalLM.from_pretrained("google/gemma-2-9b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")

        >>> prompt = "What is your favorite condiment?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "What is your favorite condiment?"
        ```"""
        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs: Gemma4TextModelOutputWithPast = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            per_layer_inputs=per_layer_inputs,
            use_cache=use_cache,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        # 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
        logits = self.lm_head(hidden_states[:, slice_indices, :])
        if self.config.final_logit_softcapping is not None:
            logits = logits / self.config.final_logit_softcapping
            logits = torch.tanh(logits)
            logits = logits * self.config.final_logit_softcapping

        loss = None
        if labels is not None:
            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)

        return Gemma4CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            shared_kv_states=outputs.shared_kv_states,
        )


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

        self.subsample_conv_projection = Gemma4AudioSubSampleConvProjection(config)
        self.rel_pos_enc = Gemma4AudioRelPositionalEncoding(config)
        self.layers = nn.ModuleList(
            [Gemma4AudioLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.output_proj = nn.Linear(config.hidden_size, config.output_proj_dims, bias=True)

        self.post_init()

    def _convert_4d_mask_to_blocked_5d(self, mask_4d: torch.Tensor) -> torch.Tensor:
        """
        Convert a standard 4D attention mask `[batch_size, 1, seq_len, seq_len]` to the 5D blocked format
        `[batch_size, 1, num_blocks, chunk_size, context_size]` expected by the chunked local attention,
        """
        batch_size, _, seq_len, _ = mask_4d.shape
        device = mask_4d.device

        chunk_size = self.config.attention_chunk_size
        max_past_horizon = self.config.attention_context_left - 1
        max_future_horizon = self.config.attention_context_right

        num_blocks = (seq_len + chunk_size - 1) // chunk_size
        padded_seq_len = num_blocks * chunk_size
        pad_amount = padded_seq_len - seq_len

        mask_4d = F.pad(mask_4d, (0, pad_amount, 0, pad_amount), value=False)
        mask_5d = mask_4d.reshape(batch_size, 1, num_blocks, chunk_size, padded_seq_len)
        mask_5d = F.pad(mask_5d, (max_past_horizon, max_future_horizon), value=False)

        block_starts = torch.arange(num_blocks, device=device) * chunk_size
        offsets = torch.arange(chunk_size + max_past_horizon + max_future_horizon, device=device)
        kv_indices = block_starts[:, None] + offsets[None, :]
        kv_indices = kv_indices[None, None, :, None, :].expand(batch_size, 1, -1, chunk_size, -1)

        return mask_5d.gather(-1, kv_indices)

    @merge_with_config_defaults
    @capture_outputs
    @auto_docstring(custom_intro="Encodes audio features to soft tokens.")
    def forward(
        self,
        input_features: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.BoolTensor]:
        hidden_states, output_mask = self.subsample_conv_projection(input_features, attention_mask)
        position_embeddings = self.rel_pos_enc(hidden_states)

        attention_mask = create_bidirectional_mask(
            config=self.config,
            inputs_embeds=hidden_states,
            attention_mask=output_mask,
            and_mask_function=sliding_window_mask_function(
                (self.config.attention_context_left - 1, self.config.attention_context_right)
            ),
        )
        if attention_mask is not None:
            attention_mask = self._convert_4d_mask_to_blocked_5d(attention_mask)

        for encoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask=attention_mask,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.output_proj(hidden_states)
        return Gemma4AudioModelOutput(last_hidden_state=hidden_states, attention_mask=output_mask)


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)

        if self.config.standardize:
            self.register_buffer("std_bias", torch.empty(self.config.hidden_size))
            self.register_buffer("std_scale", torch.empty(self.config.hidden_size))

        self.post_init()

    @merge_with_config_defaults
    @capture_outputs
    @auto_docstring(custom_intro="Encodes image pixels to soft tokens from patches.")
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        pixel_position_ids: torch.LongTensor,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        r"""
        pixel_values (`torch.FloatTensor` or `list[torch.FloatTensor]`):
            The images to encode. Either a single `[batch, channels, height, width]` tensor
            (all images same size) or a list of `[1, channels, height, width]` tensors (different sizes).
        pixel_position_ids (`torch.LongTensor` of shape `(batch_size, max_patches, 2)`):
            The patch positions as (x, y) coordinates in the image. Padding patches are indicated by (-1, -1).
        """
        pooling_kernel_size = self.config.pooling_kernel_size
        output_length = pixel_values.shape[-2] // (pooling_kernel_size * pooling_kernel_size)

        padding_positions = (pixel_position_ids == -1).all(dim=-1)
        inputs_embeds = self.patch_embedder(pixel_values, pixel_position_ids, padding_positions)
        output = self.encoder(
            inputs_embeds=inputs_embeds,
            attention_mask=~padding_positions,  # encoder expects True=valid, padding_positions is True=padding
            pixel_position_ids=pixel_position_ids,
            **kwargs,
        )

        hidden_states, pooler_mask = self.pooler(
            hidden_states=output.last_hidden_state,
            pixel_position_ids=pixel_position_ids,
            padding_positions=padding_positions,
            output_length=output_length,
        )

        # Strip padding tokens. pooler_mask is True = valid, False = padding.
        hidden_states = hidden_states[pooler_mask]

        # The pooler returns float32-scaled features. Standardize in float32 (the std_bias
        # subtraction cancels large values) and cast back to the working dtype.
        if self.config.standardize:
            hidden_states = (hidden_states - self.std_bias.float()) * self.std_scale.float()
        hidden_states = hidden_states.to(inputs_embeds.dtype)

        return BaseModelOutputWithPast(last_hidden_state=hidden_states)


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)
        self.embedding_pre_projection_norm = Gemma4RMSNorm(self.multimodal_hidden_size, eps=self.eps, with_scale=False)

    def forward(self, inputs_embeds: torch.Tensor) -> torch.Tensor:
        """Embeds token ids or soft tokens for multimodal content into language model space.
        Args:
            inputs_embeds: A torch.Tensor containing the soft tokens to embed.
        Returns:
            A torch.Tensor of embeddings with shape `[batch_size, seq_len, self.config.text_config.hidden_size]`.
        """
        embs_normed = self.embedding_pre_projection_norm(inputs_embeds)
        return self.embedding_projection(embs_normed)


def get_block_sequence_ids_for_mask(mm_token_type_ids: torch.Tensor, device: torch.device) -> torch.Tensor:
    mm_token_type_ids = mm_token_type_ids.to(device)

    is_vision = (mm_token_type_ids == 1) | (mm_token_type_ids == 2)
    is_prev_vision = torch.roll(is_vision, shifts=1, dims=-1)
    is_prev_vision[..., 0] = False
    new_vision_starts = is_vision & ~is_prev_vision
    vision_group_ids = torch.cumsum(new_vision_starts.int(), dim=1) - 1
    block_sequence_ids = torch.where(is_vision, vision_group_ids, -1)
    return block_sequence_ids


@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.vision_tower = AutoModel.from_config(config.vision_config) if config.vision_config is not None else None
        self.vocab_size = config.text_config.vocab_size

        language_model = AutoModel.from_config(config=config.text_config)
        self.language_model = language_model
        self.vocab_size_per_layer_input = config.text_config.vocab_size_per_layer_input
        self.audio_tower = AutoModel.from_config(config.audio_config) if config.audio_config is not None else None
        self.embed_vision = (
            Gemma4MultimodalEmbedder(config.vision_config, config.text_config)
            if config.vision_config is not None
            else None
        )
        self.embed_audio = (
            Gemma4MultimodalEmbedder(config.audio_config, config.text_config)
            if config.audio_config is not None
            else None
        )
        self.post_init()

    @can_return_tuple
    @auto_docstring(custom_intro="Projects the last hidden state from the vision model into language model space.")
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_position_ids: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        r"""
        image_position_ids (`torch.LongTensor` of shape `(batch_size, max_patches, 2)`, *optional*):
            The patch positions as (x, y) coordinates in the image. Padding patches are indicated by (-1, -1).
        """
        vision_outputs = self.vision_tower(
            pixel_values=pixel_values,
            pixel_position_ids=image_position_ids,
            **kwargs,
        )
        last_hidden_state = vision_outputs.last_hidden_state
        vision_outputs.pooler_output = self.embed_vision(inputs_embeds=last_hidden_state)
        return vision_outputs

    def get_placeholder_mask(
        self,
        input_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
    ) -> tuple[torch.BoolTensor, torch.BoolTensor, torch.BoolTensor]:
        """
        Obtains mask for multimodal placeholders (replaced by soft tokens) and hard text tokens.

        Masks will be obtained from `mm_token_type_ids`, `input_ids`, or `inputs_embeds` as available and in that
        precedence order. If passing `input_ids` or `inputs_embeds`, the image mask will be derived using
        `config.image_token_id`. Same goes for audio and video masks

        Args:
            input_ids: A tensor containing the hard token IDs from the text tokenizer.
            inputs_embeds: A tensor containing the embeddings for all hard text tokens.

        Returns:
            image_mask, video_mask, audio_mask
        """
        if input_ids is not None:
            special_image_mask = input_ids == self.config.image_token_id
            special_video_mask = input_ids == self.config.video_token_id
            special_audio_mask = input_ids == self.config.audio_token_id
        else:
            special_image_mask = (
                inputs_embeds
                == self.get_input_embeddings()(
                    torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
                )
            ).all(-1)
            special_video_mask = (
                inputs_embeds
                == self.get_input_embeddings()(
                    torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
                )
            ).all(-1)
            special_audio_mask = (
                inputs_embeds
                == self.get_input_embeddings()(
                    torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
                )
            ).all(-1)

        return special_image_mask, special_video_mask, special_audio_mask

    @merge_with_config_defaults
    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        pixel_values_videos: torch.FloatTensor | None = None,
        input_features: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        input_features_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        mm_token_type_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        image_position_ids: torch.LongTensor | None = None,
        video_position_ids: torch.LongTensor | None = None,
        per_layer_inputs: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Gemma4ModelOutputWithPast:
        r"""
        input_features_mask (`torch.FloatTensor]` of shape `(num_images, seq_length)`):
            The attention mask for the input audio.
        image_position_ids (`torch.LongTensor` of shape `(batch_size, max_patches, 2)`, *optional*):
            2D patch position coordinates from the image processor, with `(-1, -1)` indicating padding.
            Passed through to the vision encoder for positional embedding computation.
        video_position_ids (`torch.LongTensor` of shape `(num_videos, num_frames, max_patches, 2)`, *optional*):
            2D patch position coordinates from the video processor, with `(-1, -1)` indicating padding.
            Passed through to the vision encoder for positional embedding computation.
        per_layer_inputs (`torch.Tensor`, *optional*):
            Pre-computed per-layer input text embeddings of shape `(batch_size, sequence_length, num_hidden_layers,
            hidden_size_per_layer_input)`. When provided, these are used directly instead of being computed from `input_ids`
            via `get_per_layer_inputs()` in the text model. If calling the `forward` with `inputs_embeds` instead of `input_ids`,
            you should probably precompute them and forward them along `inputs_embeds`, otherwise recomputing them needs
            to reverse the main embedding, which is expensive.
        """
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if input_ids is not None and per_layer_inputs is not None:
            raise ValueError("You cannot specify per_layer_inputs if input_ids is provided")

        image_mask, video_mask, audio_mask = self.get_placeholder_mask(input_ids, inputs_embeds)
        multimodal_mask = image_mask | video_mask | audio_mask

        # Replace image id with PAD if the image token if OOV, to avoid index-errors
        llm_input_ids = None
        if inputs_embeds is None:
            llm_input_ids = input_ids.clone()
            llm_input_ids = torch.where(multimodal_mask, self.config.text_config.pad_token_id, llm_input_ids)
            inputs_embeds = self.get_input_embeddings()(llm_input_ids)

        if per_layer_inputs is None and self.config.get_text_config().hidden_size_per_layer_input:
            pad_embedding = self.language_model.embed_tokens.weight[self.config.text_config.pad_token_id, :]
            multimodal_mask = multimodal_mask.to(inputs_embeds.device)
            llm_inputs_embeds = torch.where(multimodal_mask[..., None], pad_embedding.view(1, 1, -1), inputs_embeds)
            per_layer_inputs = self.language_model.get_per_layer_inputs(llm_input_ids, llm_inputs_embeds)

        # Merge text and images
        if pixel_values is not None:
            image_features = self.get_image_features(pixel_values, image_position_ids, return_dict=True).pooler_output
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)

            # Confirm the number of soft tokens from the vision tower matches the number of slots in the embeddings.
            n_image_tokens = image_mask.sum()
            image_mask = image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
            torch_compilable_check(
                inputs_embeds[image_mask].numel() == image_features.numel(),
                f"Image features and image tokens do not match, tokens: {n_image_tokens}, features:"
                f" {image_features.shape[0]}",
            )

            inputs_embeds = inputs_embeds.masked_scatter(
                image_mask.to(inputs_embeds.device), image_features.to(inputs_embeds.device)
            )

        if pixel_values_videos is not None:
            video_features = self.get_video_features(
                pixel_values_videos, video_position_ids, return_dict=True
            ).pooler_output
            video_features = video_features.to(inputs_embeds.device, inputs_embeds.dtype)

            # Confirm the number of soft tokens from the vision tower matches the number of slots in the embeddings.
            n_video_tokens = video_mask.sum()
            video_mask = video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
            torch_compilable_check(
                inputs_embeds[video_mask].numel() == video_features.numel(),
                f"Video features and video tokens do not match, tokens: {n_video_tokens}, features:"
                f" {video_features.shape[0]}",
            )

            inputs_embeds = inputs_embeds.masked_scatter(
                video_mask.to(inputs_embeds.device), video_features.to(inputs_embeds.device)
            )

        # Merge text and audio
        if input_features is not None and input_features_mask is not None:
            audio_output = self.get_audio_features(input_features, input_features_mask, return_dict=True)
            audio_features = audio_output.pooler_output
            audio_mask_from_encoder = audio_output.attention_mask  # True = valid

            # Strip padding tokens: only keep real (non-padding) audio soft tokens.
            # audio_mask_from_encoder is True for valid positions, False for padding tokens.
            # This mirrors the vision encoder's padding stripping (see Gemma4VisionEncoder.forward).
            audio_features = audio_features[audio_mask_from_encoder.to(audio_features.device)]

            n_audio_tokens = audio_mask.sum()
            audio_mask = audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
            torch_compilable_check(
                inputs_embeds[audio_mask].numel() == audio_features.numel(),
                f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features:"
                f" {audio_features.shape[0] * audio_features.shape[1]}",
            )

            inputs_embeds = inputs_embeds.masked_scatter(
                audio_mask.to(inputs_embeds.device), audio_features.to(inputs_embeds.device)
            )

        # It may already have been prepared by, e.g., `generate`
        if position_ids is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            position_ids = position_ids.unsqueeze(0)

        if not isinstance(causal_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config.get_text_config(),
                "inputs_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }

            # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
            # Smaller Gemma models use a conventional casual attention mask
            if self.config.get_text_config().use_bidirectional_attention == "vision":
                block_sequence_ids = torch.full([*inputs_embeds.size()[:-1]], -1, device=inputs_embeds.device)
                if mm_token_type_ids is not None:
                    block_sequence_ids = get_block_sequence_ids_for_mask(
                        mm_token_type_ids, device=inputs_embeds.device
                    )

                mask_kwargs["block_sequence_ids"] = block_sequence_ids

            # Create the masks
            causal_mask_mapping = create_masks_for_generate(**mask_kwargs)

        outputs = self.language_model(
            per_layer_inputs=per_layer_inputs,
            attention_mask=causal_mask_mapping,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            return_dict=True,
            **kwargs,
        )

        return Gemma4ModelOutputWithPast(
            last_hidden_state=outputs.last_hidden_state,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=image_features if pixel_values is not None else None,
            audio_hidden_states=audio_features if input_features is not None else None,
            shared_kv_states=outputs.shared_kv_states,
        )

    def get_per_layer_input_embeddings(self):
        return self.language_model.embed_tokens_per_layer

    def set_per_layer_input_embeddings(self, value):
        self.language_model.embed_tokens_per_layer = value

    @can_return_tuple
    @auto_docstring(custom_intro="Projects the last hidden state from the audio encoder into language model space.")
    def get_audio_features(
        self,
        input_features: torch.Tensor,
        input_features_mask: torch.Tensor,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | Gemma4AudioModelOutput:
        r"""
        input_features (`torch.FloatTensor]` of shape `(num_images, seq_length, num_features)`):
            The tensors corresponding to the input audio.
        input_features_mask (`torch.FloatTensor]` of shape `(num_images, seq_length)`):
            The attention mask for the input audio.
        """
        if self.audio_tower is None:
            raise ValueError(
                "Audio features were requested, but the model was initialized without an audio_config. "
                "Cannot process audio without an audio tower and audio embedder."
            )

        audio_outputs = self.audio_tower(input_features, input_features_mask, return_dict=True, **kwargs)
        audio_outputs.pooler_output = self.embed_audio(inputs_embeds=audio_outputs.last_hidden_state)

        return audio_outputs

    @can_return_tuple
    @auto_docstring(custom_intro="Projects the last hidden state from the vision encoder into language model space.")
    def get_video_features(
        self,
        pixel_values_videos: torch.FloatTensor,
        video_position_ids: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        r"""
        video_position_ids (`torch.LongTensor` of shape `(num_videos, num_frames, max_patches, 2)`, *optional*):
            2D patch position coordinates from the video processor, with `(-1, -1)` indicating padding.
            Passed through to the vision encoder for positional embedding computation.
        """
        pixel_values_videos = pixel_values_videos.flatten(0, 1)
        video_position_ids = video_position_ids.flatten(0, 1)
        vision_outputs = self.vision_tower(
            pixel_values=pixel_values_videos,
            pixel_position_ids=video_position_ids,
            **kwargs,
        )
        last_hidden_state = vision_outputs.last_hidden_state
        vision_outputs.pooler_output = self.embed_vision(inputs_embeds=last_hidden_state)
        return vision_outputs


@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)
        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
        self.post_init()

    @auto_docstring
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_position_ids: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        r"""
        image_position_ids (`torch.LongTensor` of shape `(batch_size, max_patches, 2)`, *optional*):
            2D patch position coordinates from the image processor, with `(-1, -1)` indicating padding.
            Passed through to the vision encoder for positional embedding computation.
        """
        return self.model.get_image_features(pixel_values, image_position_ids, **kwargs)

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        pixel_values_videos: torch.FloatTensor | None = None,
        input_features: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        input_features_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        image_position_ids: torch.LongTensor | None = None,
        video_position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        mm_token_type_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        per_layer_inputs: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Gemma4CausalLMOutputWithPast:
        r"""
        input_features_mask (`torch.FloatTensor]` of shape `(num_images, seq_length)`):
            The attention mask for the input audio.
        image_position_ids (`torch.LongTensor` of shape `(batch_size, max_patches, 2)`, *optional*):
            2D patch position coordinates from the image processor, with `(-1, -1)` indicating padding.
            Passed through to the vision encoder for positional embedding computation.
        video_position_ids (`torch.LongTensor` of shape `(num_videos, num_frames, max_patches, 2)`, *optional*):
            2D patch position coordinates from the video processor, with `(-1, -1)` indicating padding.
            Passed through to the vision encoder for positional embedding computation.
        per_layer_inputs (`torch.Tensor`, *optional*):
            Pre-computed per-layer input text embeddings of shape `(batch_size, sequence_length, num_hidden_layers,
            hidden_size_per_layer_input)`. When provided, these are used directly instead of being computed from `input_ids`
            via `get_per_layer_inputs()` in the text model. If calling the `forward` with `inputs_embeds` instead of `input_ids`,
            you should probably precompute them and forward them along `inputs_embeds`, otherwise recomputing them needs
            to reverse the main embedding, which is expensive.
        """
        outputs = self.model(
            input_ids=input_ids,
            pixel_values=pixel_values,
            pixel_values_videos=pixel_values_videos,
            input_features=input_features,
            attention_mask=attention_mask,
            input_features_mask=input_features_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            mm_token_type_ids=mm_token_type_ids,
            inputs_embeds=inputs_embeds,
            per_layer_inputs=per_layer_inputs,
            labels=labels,
            use_cache=use_cache,
            image_position_ids=image_position_ids,
            video_position_ids=video_position_ids,
            return_dict=True,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        # 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
        logits = self.lm_head(hidden_states[:, slice_indices, :])
        if (final_logit_softcapping := self.config.get_text_config().final_logit_softcapping) is not None:
            logits = logits / final_logit_softcapping
            logits = torch.tanh(logits)
            logits = logits * final_logit_softcapping

        loss = None
        if labels is not None:
            loss = self.loss_function(logits, labels, self.config.get_text_config().vocab_size, **kwargs)

        return Gemma4CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=outputs.image_hidden_states,
            audio_hidden_states=outputs.audio_hidden_states,
            shared_kv_states=outputs.shared_kv_states,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        position_ids=None,
        pixel_values=None,
        pixel_values_videos=None,
        input_features=None,
        attention_mask=None,
        input_features_mask=None,
        token_type_ids=None,
        use_cache=True,
        logits_to_keep=None,
        labels=None,
        is_first_iteration=False,
        **kwargs,
    ):
        # Overwritten -- custom `position_ids` and `pixel_values` handling
        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            position_ids=position_ids,
            use_cache=use_cache,
            logits_to_keep=logits_to_keep,
            token_type_ids=token_type_ids,
            is_first_iteration=is_first_iteration,
            **kwargs,
        )

        # If we're in cached decoding stage, multimodal inputs are already cached and can be dropped
        if is_first_iteration or not use_cache:
            model_inputs["pixel_values"] = pixel_values
            model_inputs["pixel_values_videos"] = pixel_values_videos
            model_inputs["input_features"] = input_features
            model_inputs["input_features_mask"] = input_features_mask
        else:
            # Don't pass to not apply bidirectional mask on top
            model_inputs["mm_token_type_ids"] = None

        # If `per_layer_inputs` was provided along with `inputs_embeds` for first forward, drop it for subsequent forwards
        if not is_first_iteration:
            _ = model_inputs.pop("per_layer_inputs", None)

        return model_inputs

    def get_per_layer_input_embeddings(self):
        return self.model.get_per_layer_input_embeddings()

    def set_per_layer_input_embeddings(self, value):
        self.model.set_per_layer_input_embeddings(value)

    @staticmethod
    def create_masks_for_generate(
        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,
        is_first_iteration: bool | None = False,
        **kwargs,
    ) -> dict:
        mask_kwargs = {
            "config": config.get_text_config(),
            "inputs_embeds": inputs_embeds,
            "attention_mask": attention_mask,
            "past_key_values": past_key_values,
            "position_ids": position_ids,
        }

        # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
        # Smaller Gemma models use a conventional casual attention mask
        if getattr(config.get_text_config(), "use_bidirectional_attention", None) == "vision":
            block_sequence_ids = torch.full([*inputs_embeds.size()[:-1]], -1, device=inputs_embeds.device)
            if mm_token_type_ids is not None:
                block_sequence_ids = get_block_sequence_ids_for_mask(mm_token_type_ids, device=inputs_embeds.device)

            mask_kwargs["block_sequence_ids"] = block_sequence_ids

        return create_masks_for_generate(**mask_kwargs)


__all__ = [
    "Gemma4AudioModel",
    "Gemma4ForCausalLM",
    "Gemma4ForConditionalGeneration",
    "Gemma4Model",
    "Gemma4PreTrainedModel",
    "Gemma4TextModel",
    "Gemma4VisionModel",
]
