#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
#           This file was automatically generated from src/transformers/models/phi4_multimodal/modular_phi4_multimodal.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_phi4_multimodal.py file directly. One of our CI enforces this.
#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2025 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
import warnings
from functools import wraps
from typing import Callable, List, Optional, Tuple, Union, Any

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.init import _calculate_fan_in_and_fan_out

from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPast,
    BaseModelOutputWithPooling,
    CausalLMOutputWithPast,
)
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
    torch_int,
)
from .configuration_phi4_multimodal import Phi4MultimodalAudioConfig, Phi4MultimodalConfig, Phi4MultimodalVisionConfig


logger = logging.get_logger(__name__)


def set_attribute_for_modules(module: "torch.nn.Module", key: str, value: Any):
    """
    Set a value to a module and all submodules.
    """
    setattr(module, key, value)
    for submodule in module.children():
        set_attribute_for_modules(submodule, key, value)


def del_attribute_from_modules(module: "torch.nn.Module", key: str):
    """
    Delete a value from a module and all submodules.
    """
    # because we might remove it previously in case it's a shared module, e.g. activation function
    if hasattr(module, key):
        delattr(module, key)

    for submodule in module.children():
        del_attribute_from_modules(submodule, key)


def can_return_tuple(func):
    """
    Decorator to wrap model method, to call output.to_tuple() if return_dict=False passed as a kwarg or
    use_return_dict=False is set in the config.

    Note:
        output.to_tuple() convert output to tuple skipping all `None` values.
    """

    @wraps(func)
    def wrapper(self, *args, **kwargs):
        is_requested_to_return_tuple = kwargs.pop("return_dict", True) is False
        is_configured_to_return_tuple = self.config.use_return_dict is False if hasattr(self, "config") else False

        # The following allows to convert output to tuple ONLY on top level forward call,
        # while internal modules of the model will return Output objects
        # to be able to use name-based attribute access in modeling code.

        # We will check if we are on top level module, if so, turn off to tuple conversion for all
        # underling calls.
        is_top_level_module = getattr(self, "_is_top_level_module", True)
        if is_configured_to_return_tuple and is_top_level_module:
            set_attribute_for_modules(self, "_is_top_level_module", False)

        try:
            output = func(self, *args, **kwargs)
            if is_requested_to_return_tuple or (is_configured_to_return_tuple and is_top_level_module):
                output = output.to_tuple()
        finally:
            # Remove the flag after the model forward call is finished.
            if is_configured_to_return_tuple and is_top_level_module:
                del_attribute_from_modules(self, "_is_top_level_module")

        return output

    return wrapper


def dynamic_rope_update(rope_forward):
    """
    Decorator function to update the RoPE parameters in the forward pass, if the model is using a dynamic RoPE
    (i.e. a RoPE implementation that may recompute its frequencies in the forward pass).

    Args:
        rope_forward (Callable):
            The forward pass of the RoPE implementation.

    Returns:
        The decorated forward pass.
    """

    def longrope_frequency_update(self, position_ids, device):
        """Longrope uses long factor if sequence is larger than original pretraining length, short otherwise."""
        seq_len = torch.max(position_ids) + 1
        if hasattr(self.config, "original_max_position_embeddings"):
            original_max_position_embeddings = self.config.original_max_position_embeddings
        else:
            original_max_position_embeddings = self.config.max_position_embeddings
        if seq_len > original_max_position_embeddings:
            if not hasattr(self, "long_inv_freq"):
                self.long_inv_freq, _ = self.rope_init_fn(
                    self.config, device, seq_len=original_max_position_embeddings + 1
                )
            self.register_buffer("inv_freq", self.long_inv_freq, persistent=False)
        else:
            # This .to() is needed if the model has been moved to a device after being initialized (because
            # the buffer is automatically moved, but not the original copy)
            self.original_inv_freq = self.original_inv_freq.to(device)
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)

    def dynamic_frequency_update(self, position_ids, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
            # This .to() is needed if the model has been moved to a device after being initialized (because
            # the buffer is automatically moved, but not the original copy)
            self.original_inv_freq = self.original_inv_freq.to(device)
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @wraps(rope_forward)
    def wrapper(self, x, position_ids):
        if "dynamic" in self.rope_type:
            dynamic_frequency_update(self, position_ids, device=x.device)
        elif self.rope_type == "longrope":
            longrope_frequency_update(self, position_ids, device=x.device)
        return rope_forward(self, x, position_ids)

    return wrapper


class Phi4MultimodalVisionMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.activation_fn = ACT2FN[config.hidden_act]
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states


def simple_eager_attention_forward(
    module: nn.Module,
    query_states: torch.Tensor,
    key_states: torch.Tensor,
    value_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.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


class Phi4MultimodalVisionAttention(nn.Module):
    def __init__(self, config: Phi4MultimodalVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        self.scaling = self.head_dim**-0.5
        self.is_causal = True
        self.attention_dropout = config.attention_dropout

        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size)
        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        """Input shape: Batch x Time x Channel"""
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

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

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

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

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


class Phi4MultimodalVisionEncoderLayer(nn.Module):
    def __init__(self, config: Phi4MultimodalVisionConfig):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.self_attn = Phi4MultimodalVisionAttention(config)
        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = Phi4MultimodalVisionMLP(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor]:
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                Input to the layer of shape `(batch, seq_len, embed_dim)`.
            attention_mask (`torch.FloatTensor`):
                Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
        )
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class Phi4MultimodalVisionEncoder(nn.Module):
    """
    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
    [`Phi4MultimodalVisionEncoderLayer`].

    Args:
        config: Phi4MultimodalVisionConfig
    """

    def __init__(self, config: Phi4MultimodalVisionConfig):
        super().__init__()
        self.config = config
        self.layers = nn.ModuleList(
            [Phi4MultimodalVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]
        )
        self.gradient_checkpointing = False

    # Ignore copy
    @can_return_tuple
    def forward(
        self,
        inputs_embeds,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
    ) -> BaseModelOutput:
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        hidden_states = inputs_embeds
        for encoder_layer in self.layers:
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    encoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    output_attentions,
                )
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    attention_mask,
                    output_attentions=output_attentions,
                )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=encoder_states,
            attentions=all_attentions,
        )


def _trunc_normal_(tensor, mean, std, a, b):
    # Cut & paste from PyTorch official master until it's in a few official releases - RW
    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    def norm_cdf(x):
        # Computes standard normal cumulative distribution function
        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        warnings.warn(
            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
            "The distribution of values may be incorrect.",
            stacklevel=2,
        )

    # Values are generated by using a truncated uniform distribution and
    # then using the inverse CDF for the normal distribution.
    # Get upper and lower cdf values
    l = norm_cdf((a - mean) / std)
    u = norm_cdf((b - mean) / std)

    # Uniformly fill tensor with values from [l, u], then translate to
    # [2l-1, 2u-1].
    tensor.uniform_(2 * l - 1, 2 * u - 1)

    # Use inverse cdf transform for normal distribution to get truncated
    # standard normal
    tensor.erfinv_()

    # Transform to proper mean, std
    tensor.mul_(std * math.sqrt(2.0))
    tensor.add_(mean)

    # Clamp to ensure it's in the proper range
    tensor.clamp_(min=a, max=b)


def trunc_normal_tf_(
    tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
) -> torch.Tensor:
    """Fills the input Tensor with values drawn from a truncated
    normal distribution. The values are effectively drawn from the
    normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
    with values outside :math:`[a, b]` redrawn until they are within
    the bounds. The method used for generating the random values works
    best when :math:`a \\leq \text{mean} \\leq b`.

    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
    and the result is subsequently scaled and shifted by the mean and std args.

    Args:
        tensor: an n-dimensional `torch.Tensor`
        mean: the mean of the normal distribution
        std: the standard deviation of the normal distribution
        a: the minimum cutoff value
        b: the maximum cutoff value
    """
    with torch.no_grad():
        _trunc_normal_(tensor, 0, 1.0, a, b)
        tensor.mul_(std).add_(mean)


def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
    if mode == "fan_in":
        denom = fan_in
    elif mode == "fan_out":
        denom = fan_out
    elif mode == "fan_avg":
        denom = (fan_in + fan_out) / 2

    variance = scale / denom

    if distribution == "truncated_normal":
        # constant is stddev of standard normal truncated to (-2, 2)
        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
    elif distribution == "normal":
        with torch.no_grad():
            tensor.normal_(std=math.sqrt(variance))
    elif distribution == "uniform":
        bound = math.sqrt(3 * variance)
        with torch.no_grad():
            tensor.uniform_(-bound, bound)
    else:
        raise ValueError(f"invalid distribution {distribution}")


def lecun_normal_(tensor):
    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")


def default_flax_embed_init(tensor):
    variance_scaling_(tensor, mode="fan_in", distribution="normal")


class Phi4MultimodalVisionPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = Phi4MultimodalVisionConfig
    base_model_prefix = "phi4_vision"
    supports_gradient_checkpointing = True

    _no_split_modules = ["Phi4MultimodalVisionEncoderLayer"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_flex_attn = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, Phi4MultimodalVisionEmbeddings):
            width = (
                self.config.hidden_size
                if isinstance(self.config, Phi4MultimodalVisionConfig)
                else self.config.hidden_size
            )
            nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
        elif isinstance(module, nn.Embedding):
            default_flax_embed_init(module.weight)
        elif isinstance(module, Phi4MultimodalVisionAttention):
            nn.init.normal_(module.q_proj.weight)
            nn.init.normal_(module.k_proj.weight)
            nn.init.normal_(module.v_proj.weight)
            nn.init.normal_(module.out_proj.weight)
            nn.init.zeros_(module.q_proj.bias)
            nn.init.zeros_(module.k_proj.bias)
            nn.init.zeros_(module.v_proj.bias)
            nn.init.zeros_(module.out_proj.bias)
        elif isinstance(module, Phi4MultimodalVisionMLP):
            nn.init.normal_(module.fc1.weight)
            nn.init.normal_(module.fc2.weight)
            nn.init.normal_(module.fc1.bias, std=1e-6)
            nn.init.normal_(module.fc2.bias, std=1e-6)
        elif isinstance(module, Phi4MultimodalVisionMultiheadAttentionPoolingHead):
            nn.init.normal_(module.probe.data)
            nn.init.normal_(module.attention.in_proj_weight.data)
            nn.init.zeros_(module.attention.in_proj_bias.data)
        elif isinstance(module, (nn.Linear, nn.Conv2d)):
            lecun_normal_(module.weight)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


class Phi4MultimodalVisionEmbeddings(nn.Module):
    def __init__(self, config: Phi4MultimodalVisionConfig):
        super().__init__()
        self.config = config
        self.patch_size = config.patch_size
        self.num_patches_per_side = config.image_size // self.patch_size

        self.patch_embedding = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=config.hidden_size,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            padding="valid",
        )
        self.position_embedding = nn.Embedding(self.num_patches_per_side**2, config.hidden_size)

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing and no class embeddings.

        Adapted from:
        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
        """

        num_patches = embeddings.shape[1]
        num_positions = self.position_embedding.weight.shape[0]

        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
            return self.position_embedding(self.position_ids)

        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)

        dim = embeddings.shape[-1]

        new_height = height // self.patch_size
        new_width = width // self.patch_size

        sqrt_num_positions = torch_int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(new_height, new_width),
            mode="bicubic",
            align_corners=False,
        )

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return patch_pos_embed

    def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor:
        batch_size = pixel_values.size(0)

        patch_embeds = self.patch_embedding(pixel_values)
        embeddings = patch_embeds.flatten(2).transpose(1, 2)

        max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3)
        max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
        boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
        position_ids = torch.full((batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0)

        for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
            nb_patches_h = p_attn_mask[:, 0].sum()
            nb_patches_w = p_attn_mask[0].sum()

            fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
            fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)

            bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
            bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)

            pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
            position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids

        position_ids = position_ids.to(self.position_embedding.weight.device)

        embeddings = embeddings + self.position_embedding(position_ids)
        return embeddings


class Phi4MultimodalVisionMultiheadAttentionPoolingHead(nn.Module):
    """Multihead Attention Pooling."""

    def __init__(self, config: Phi4MultimodalVisionConfig):
        super().__init__()

        self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
        self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True)
        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.mlp = Phi4MultimodalVisionMLP(config)

    def forward(self, hidden_state, attention_mask):
        batch_size = hidden_state.shape[0]
        probe = self.probe.repeat(batch_size, 1, 1)

        hidden_state = self.attention(
            query=probe, key=hidden_state, value=hidden_state, key_padding_mask=~attention_mask
        )[0]

        residual = hidden_state
        hidden_state = self.layernorm(hidden_state)
        hidden_state = residual + self.mlp(hidden_state)

        return hidden_state[:, 0]


class Phi4MultimodalVisionModel(Phi4MultimodalVisionPreTrainedModel):
    config_class = Phi4MultimodalVisionConfig
    main_input_name = "pixel_values"

    def __init__(self, config: Phi4MultimodalVisionConfig):
        super().__init__(config)
        self.config = config

        self.embeddings = Phi4MultimodalVisionEmbeddings(config)
        self.encoder = Phi4MultimodalVisionEncoder(config)
        self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.head = Phi4MultimodalVisionMultiheadAttentionPoolingHead(config)

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

    def get_input_embeddings(self) -> nn.Module:
        return self.embeddings.patch_embedding

    def forward(
        self,
        pixel_values,
        patch_attention_mask: Optional[torch.BoolTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
    ) -> BaseModelOutputWithPooling:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        batch_size = pixel_values.size(0)
        if patch_attention_mask is None:
            patch_attention_mask = torch.ones(
                size=(
                    batch_size,
                    pixel_values.size(2) // self.config.patch_size,
                    pixel_values.size(3) // self.config.patch_size,
                ),
                dtype=torch.bool,
                device=pixel_values.device,
            )

        hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask)

        patch_attention_mask = patch_attention_mask.view(batch_size, -1)
        # The call to `_upad_input` in `_flash_attention_forward` is expensive
        # So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
        # avoiding passing the attention_mask, which is equivalent to attending to the full sequence
        if not torch.any(~patch_attention_mask):
            attention_mask = None
        else:
            attention_mask = (
                _prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype)
                if not self.config._attn_implementation == "flash_attention_2"
                else patch_attention_mask
            )

        encoder_outputs: BaseModelOutput = self.encoder(
            inputs_embeds=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )

        last_hidden_state = encoder_outputs.last_hidden_state
        last_hidden_state = self.post_layernorm(last_hidden_state)

        pooled_output = self.head(
            hidden_state=last_hidden_state,
            attention_mask=patch_attention_mask,
        )

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class Phi4MultimodalImageEmbedding(nn.Module):
    """Image embedding."""

    def __init__(self, config: Phi4MultimodalConfig):
        super().__init__()
        self.config = config
        self.layer_idx = config.vision_config.feature_layer
        self.crop_size = config.vision_config.crop_size
        self.image_dim_out = config.vision_config.hidden_size

        n_patches = config.vision_config.image_size // config.vision_config.patch_size
        if n_patches % 2 != 0:
            self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1))
            n_patches += 1
        self.num_img_tokens = (n_patches // 2) ** 2

        self.drop = nn.Dropout(config.embd_pdrop)
        self.img_processor = Phi4MultimodalVisionModel._from_config(config.vision_config)
        self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2)
        self.img_projection_up = nn.Linear(self.image_dim_out, config.hidden_size)
        self.img_projection_down = nn.Linear(config.hidden_size, config.hidden_size)
        self.global_img_feature_extensor = nn.Parameter(torch.zeros([1, 1, self.image_dim_out]))
        self.sub_img_feature_extensor = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out]))

    def get_img_features(self, img_embeds: torch.FloatTensor, attention_mask=None) -> torch.FloatTensor:
        img_processor_output = self.img_processor(
            img_embeds, patch_attention_mask=attention_mask, output_hidden_states=True
        )
        img_feature = img_processor_output.hidden_states[self.layer_idx]

        patch_feature = img_feature
        # reshape to 2D tensor
        width = int(math.sqrt(patch_feature.size(1)))
        patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1))
        # convert to NCHW
        patch_feature = patch_feature.permute(0, 3, 1, 2)
        if getattr(self, "img_processor_padding", None) is not None:
            patch_feature = self.img_processor_padding(patch_feature)
        patch_feature = self.image_token_compression(patch_feature)
        # convert to NHWC
        patch_feature = patch_feature.permute(0, 2, 3, 1)
        patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1))
        return patch_feature

    def forward(
        self,
        input_ids: torch.LongTensor,
        inputs_embeds: torch.Tensor,
        image_pixel_values: torch.FloatTensor,
        image_sizes: Optional[torch.Tensor] = None,
        image_attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.FloatTensor:
        image_pixel_values = image_pixel_values.to(self.img_processor.embeddings.patch_embedding.weight.dtype)

        target_device = self.img_projection_up.bias.device
        target_dtype = self.img_projection_up.bias.dtype

        batch_size = image_pixel_values.shape[0]

        img_features = self.get_img_features(
            image_pixel_values.flatten(0, 1),
            attention_mask=image_attention_mask.flatten(0, 1).to(dtype=bool, device=target_device),
        )
        base_feat_size = int(np.sqrt(img_features.shape[1]))
        img_features = img_features.view(batch_size, -1, base_feat_size**2, self.image_dim_out)
        image_sizes = image_sizes.view(-1, 2)

        output_imgs = []
        for idx in range(batch_size):
            height, width = image_sizes[idx]
            height_ratio = height // self.crop_size
            width_ratio = width // self.crop_size
            area_ratio = height_ratio * width_ratio

            global_img = img_features[idx, :1]
            global_img = global_img.reshape(1, base_feat_size, base_feat_size, self.image_dim_out).contiguous()
            temporary_extensor = self.sub_img_feature_extensor.repeat(1, base_feat_size, 1, 1)
            global_img = torch.cat([global_img, temporary_extensor], dim=2).reshape(1, -1, self.image_dim_out)

            sub_img = img_features[idx, 1:]
            sub_img = sub_img[:area_ratio]
            sub_img = (
                sub_img.reshape(height_ratio, width_ratio, base_feat_size, base_feat_size, self.image_dim_out)
                .transpose(1, 2)
                .reshape(1, height_ratio * base_feat_size, width_ratio * base_feat_size, self.image_dim_out)
                .contiguous()
            )

            if image_attention_mask is not None:
                reshaped_image_attention_mask = (
                    image_attention_mask[idx, 1 : area_ratio + 1, 0::2, 0::2]
                    .reshape(height_ratio, width_ratio, base_feat_size, base_feat_size)
                    .transpose(1, 2)
                    .reshape(1, height_ratio * base_feat_size, width_ratio * base_feat_size)
                )
                useful_height = int(reshaped_image_attention_mask[0, :, 0].sum().item())
                useful_width = int(reshaped_image_attention_mask[0, 0, :].sum().item())
                sub_img = sub_img[:, :useful_height, :useful_width]
                temporary_extensor = self.sub_img_feature_extensor.repeat(1, useful_height, 1, 1)
            else:
                temporary_extensor = self.sub_img_feature_extensor.repeat(1, height_ratio * base_feat_size, 1, 1)

            sub_img = torch.cat([sub_img, temporary_extensor], dim=2).reshape(1, -1, self.image_dim_out)

            # Merge global and sub
            output_imgs.append(torch.cat([sub_img, self.global_img_feature_extensor, global_img], dim=1))

        img_set_tensor = []
        for output_img in output_imgs:
            output_img = output_img.to(device=target_device, dtype=target_dtype)
            img_feature_proj = self.img_projection_up(output_img)
            img_feature_proj = nn.functional.gelu(img_feature_proj)
            img_feature_proj = self.img_projection_down(img_feature_proj)
            img_set_tensor.append(img_feature_proj)

        merged_img_set_tensor = torch.cat(img_set_tensor, dim=1).squeeze(0)
        merged_img_set_tensor = merged_img_set_tensor.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)

        with torch.no_grad():
            positions_tuple = torch.nonzero(input_ids == self.config.vision_config.image_token_id, as_tuple=True)

        # Temporarily disable autocast to avoid issue on bf16 tensors
        # Ref: https://github.com/pytorch/pytorch/issues/132715
        with torch.autocast(device_type=inputs_embeds.device.type, enabled=False):
            image_embeds = inputs_embeds.index_put(
                indices=positions_tuple, values=merged_img_set_tensor, accumulate=False
            )

        image_embeds = self.drop(image_embeds)

        return image_embeds


########################################################## AUDIO #############################################


class Phi4MultimodalAudioMLP(nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__()
        self.layer_norm = nn.LayerNorm(config.hidden_size)
        self.act_fn = ACT2FN[config.activation]
        self.gate_up_proj = nn.Linear(config.hidden_size, config.intermediate_size * 2)
        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(self, hidden_states):
        hidden_states = self.layer_norm(hidden_states)
        up_states = self.gate_up_proj(hidden_states)
        up_states, gate = up_states.chunk(2, dim=-1)
        up_states = up_states * self.act_fn(gate)
        up_states = self.dropout(up_states)
        hidden_states = self.down_proj(up_states)
        out = self.dropout(hidden_states)

        return out


class Phi4MultimodalAudioAttention(nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__()
        self.config = config
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.dropout_rate
        self.is_causal = True

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        **kwargs,
    ):
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

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

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

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

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


class Phi4MultimodalAudioDepthWiseSeperableConv1d(nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig, padding: int = 0):
        super().__init__()
        self.dw_conv = nn.Conv1d(
            config.hidden_size,
            config.hidden_size * config.depthwise_multiplier,
            config.kernel_size,
            1,
            padding=padding,
            groups=config.hidden_size,
        )
        self.pw_conv = nn.Conv1d(
            config.hidden_size * config.depthwise_multiplier, config.depthwise_seperable_out_channel, 1, 1, 0
        )

    def forward(self, hidden_states):
        return self.pw_conv(self.dw_conv(hidden_states))


class Phi4MultimodalAudioGluPointWiseConv(nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__()
        self.config = config
        self.output_dim = config.ext_pw_out_channel

        self.ext_pw_conv_1d = nn.Conv1d(config.hidden_size, config.ext_pw_out_channel * 2, kernel_size=1, stride=1)
        self.glu_act = ACT2FN[config.conv_glu_type]
        self.b1 = nn.Parameter(torch.zeros(1, config.ext_pw_out_channel, 1))
        self.b2 = nn.Parameter(torch.zeros(1, config.ext_pw_out_channel, 1))

    def forward(self, hidden_states):
        # we assume the input always has the #channel (#dim) in the last dimension of the
        # tensor, so need to switch the dimension first for 1D-Conv case
        hidden_states = hidden_states.permute([0, 2, 1])
        hidden_states = self.ext_pw_conv_1d(hidden_states)
        out = hidden_states[:, 0 : self.output_dim, :] + self.b1
        out = out * self.glu_act(hidden_states[:, self.output_dim : self.output_dim * 2, :] + self.b2)
        return out.permute([0, 2, 1])


class Phi4MultimodalAudioConvModule(nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__()
        self.config = config
        self.kernel_size = config.kernel_size

        self.layer_norm = nn.LayerNorm(config.hidden_size)
        self.glu = Phi4MultimodalAudioGluPointWiseConv(config)
        self.dw_sep_conv_1d = Phi4MultimodalAudioDepthWiseSeperableConv1d(config, padding=config.kernel_size - 1)
        self.act = ACT2FN[config.conv_activation]
        self.ext_pw_conv_1d = nn.Conv1d(config.hidden_size, config.ext_pw_out_channel, kernel_size=1, stride=1)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(self, hidden_states: torch.Tensor):
        hidden_states = self.glu(self.layer_norm(hidden_states))
        hidden_states = self.dw_sep_conv_1d(hidden_states.permute([0, 2, 1]))

        if self.kernel_size > 1:
            hidden_states = hidden_states[:, :, : -(self.kernel_size - 1)]

        hidden_states = self.act(hidden_states)
        hidden_states = self.ext_pw_conv_1d(hidden_states)
        out = self.dropout(hidden_states.permute([0, 2, 1]))
        return out


class Phi4MultimodalAudioConformerEncoderLayer(nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__()

        self.feed_forward_in = Phi4MultimodalAudioMLP(config)
        self.self_attn = Phi4MultimodalAudioAttention(config)
        self.conv = Phi4MultimodalAudioConvModule(config)
        self.feed_forward_out = Phi4MultimodalAudioMLP(config)
        self.layer_norm_att = nn.LayerNorm(config.hidden_size)
        self.layer_norm = nn.LayerNorm(config.hidden_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
    ):
        residual = hidden_states + 0.5 * self.feed_forward_in(hidden_states)
        hidden_states = self.layer_norm_att(residual)

        hidden_states = residual + self.self_attn(hidden_states, attention_mask)
        hidden_states = hidden_states + self.conv(hidden_states)
        hidden_states = hidden_states + 0.5 * self.feed_forward_out(hidden_states)

        out = self.layer_norm(hidden_states)

        return out


class Phi4MultimodalAudioNemoConvSubsampling(torch.nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__()
        self.subsampling_factor = config.time_reduction
        self.sampling_num = int(math.log(self.subsampling_factor, 2))
        self.act_fn = ACT2FN[config.nemo_activation]
        conv_channels = config.nemo_conv_channels

        layers = [
            nn.Conv2d(1, conv_channels, kernel_size=3, stride=2, padding=1),
            self.act_fn,
        ]
        for _ in range(self.sampling_num - 1):
            layers.extend(
                [
                    nn.Conv2d(conv_channels, conv_channels, kernel_size=3, stride=2, padding=1, groups=conv_channels),
                    nn.Conv2d(conv_channels, conv_channels, kernel_size=1, stride=1, padding=0, groups=1),
                    self.act_fn,
                ]
            )

        # Aggregate the layers
        self.conv = torch.nn.Sequential(*layers)
        self.out = torch.nn.Linear(conv_channels * config.nemo_final_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor, mask: Optional[torch.Tensor]):
        # Unsqueeze Channel Axis
        hidden_states = hidden_states.unsqueeze(1)
        hidden_states = self.conv(hidden_states)

        # Flatten Channel and Frequency Axes
        b, _, t, _ = hidden_states.size()
        hidden_states = self.out(hidden_states.transpose(1, 2).reshape(b, t, -1))

        if mask is None:
            return hidden_states, None

        max_audio_length = hidden_states.shape[1]
        feature_lens = mask.sum(1)
        padding_length = torch.ceil(feature_lens / self.subsampling_factor)
        arange_ = torch.arange(0, max_audio_length, device=hidden_states.device)
        pad_mask = arange_.expand(padding_length.size(0), -1) < padding_length.unsqueeze(1)
        return hidden_states, pad_mask.unsqueeze(1)


class Phi4MultimodalAudioRelativeAttentionBias(nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__()

        self.max_distance = config.bias_max_distance
        self.symmetric = config.bias_symmetric
        self.num_buckets = self.max_distance
        if not config.bias_symmetric:
            self.num_buckets *= 2
        self.bias_values = nn.Embedding(self.num_buckets, config.num_attention_heads)

    def forward(self, x):
        # instantiate bias compatible with shape of x
        max_pos = x.size(1)
        context_position = torch.arange(max_pos, device=x.device, dtype=torch.long)[:, None]
        memory_position = torch.arange(max_pos, device=x.device, dtype=torch.long)[None, :]
        relative_position = memory_position - context_position
        # clipping to a maximum distance using ops that play well with ONNX export
        relative_position = relative_position.masked_fill(relative_position < -self.max_distance, -self.max_distance)
        relative_position = relative_position.masked_fill(
            relative_position > self.max_distance - 1, self.max_distance - 1
        )

        # mapping from relative position to index in the bias parameter
        bias_idx = relative_position
        bias_idx = bias_idx.abs() if self.symmetric else bias_idx + self.num_buckets // 2

        att_bias = self.bias_values(bias_idx)
        att_bias = att_bias.permute(2, 0, 1).unsqueeze(0)

        return att_bias


class Phi4MultimodalAudioMeanVarianceNormLayer(nn.Module):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__()
        self.register_buffer("global_mean", torch.zeros(config.input_size))
        self.register_buffer("global_invstd", torch.ones(config.input_size))

    def forward(self, x):
        return (x - self.global_mean) * self.global_invstd


class Phi4MultimodalAudioPreTrainedModel(PreTrainedModel):
    config_class = Phi4MultimodalAudioConfig
    supports_gradient_checkpointing = True
    _no_split_modules = ["Phi4MultimodalAudioConformerEncoderLayer"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_flex_attn = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, Phi4MultimodalAudioGluPointWiseConv):
            module.b1.data.zero_()
            module.b2.data.zero_()


def unfold_tensor(tensor, max_seq_len):
    """
    For a given tensor with shape of (N, T, D), if sequence length T is longer than max_seq_len,
    this function unfold it to a (NT', max_seq_len, D) where T' is T // max_seq_len.
    Args:
        tensor: N, T, D
    """
    _, _, D = tensor.shape
    tensor = tensor.transpose(-1, -2)
    # N x D x 1 x T => N x (D x max_seq_len) x T'
    tensor = F.unfold(tensor[..., None, :], kernel_size=(1, max_seq_len), stride=(1, max_seq_len))

    new_bsz, _, slen = tensor.shape
    tensor = tensor.view(new_bsz, -1, max_seq_len, slen)
    tensor = tensor.permute(0, 3, 2, 1)
    tensor = tensor.view(-1, max_seq_len, D).contiguous()
    return tensor


def adaptive_enc_mask(x_len, chunk_start_idx, left_window=0, right_window=0):
    """
    The function is very important for Transformer Transducer Streaming mode
    Args:
        xs_len (int): sequence length
        chunk_start_idx (list): first idx of each chunk, such as [0,18,36,48]. It also supports adaptive chunk size [0,10,15,45]
        left_window (int): how many left chunks can be seen
        right_window (int): how many right chunks can be seen. It is used for chunk overlap model.
        Returns:
            mask (torch.Tensor): a mask tensor for streaming model
    """
    chunk_start_idx = torch.Tensor(chunk_start_idx).long()
    start_pad = torch.nn.functional.pad(
        chunk_start_idx, (1, 0)
    )  # append 0 to the beginning, so it becomes [0, 0, 18, 36, 48]
    end_pad = torch.nn.functional.pad(
        chunk_start_idx, (0, 1), value=x_len
    )  # append x_len to the end, so it becomes [0,18,36,48, x_len]
    seq_range = torch.arange(0, x_len).unsqueeze(-1)
    idx = ((seq_range < end_pad) & (seq_range >= start_pad)).nonzero()[:, 1]
    seq_range_expand = torch.arange(0, x_len).unsqueeze(0).expand(x_len, -1)
    idx_left = idx - left_window
    idx_left[idx_left < 0] = 0
    boundary_left = start_pad[idx_left]
    mask_left = seq_range_expand >= boundary_left.unsqueeze(-1)
    idx_right = idx + right_window
    idx_right[idx_right > len(chunk_start_idx)] = len(chunk_start_idx)
    boundary_right = end_pad[idx_right]
    mask_right = seq_range_expand < boundary_right.unsqueeze(-1)
    return mask_left & mask_right


class Phi4MultimodalAudioModel(Phi4MultimodalAudioPreTrainedModel):
    def __init__(self, config: Phi4MultimodalAudioConfig):
        super().__init__(config)
        self.config = config

        self.encoder_embedding = Phi4MultimodalAudioMeanVarianceNormLayer(config)
        self.embed = Phi4MultimodalAudioNemoConvSubsampling(config)
        self.relative_attention_bias_layer = Phi4MultimodalAudioRelativeAttentionBias(config)
        self.encoders = nn.ModuleList(
            [Phi4MultimodalAudioConformerEncoderLayer(config) for _ in range(config.num_blocks)]
        )
        self.gradient_checkpointing = False

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

    def _streaming_mask(self, seq_len, batch_size, chunk_size, left_chunk):
        # Create mask matrix for streaming
        # S stores start index. if chunksize is 18, s is [0,18,36,....]
        chunk_start_idx = np.arange(0, seq_len, chunk_size)
        # avoid randomness when run evaluation or decoding
        if self.training and np.random.rand() > 0.5:
            # Either first or last chunk is not complete.
            # If only the last one is not complete, EOS is not effective
            chunk_start_idx = seq_len - chunk_start_idx
            chunk_start_idx = chunk_start_idx[::-1]
            chunk_start_idx = chunk_start_idx[:-1]
            chunk_start_idx = np.insert(chunk_start_idx, 0, 0)

        enc_streaming_mask = (
            adaptive_enc_mask(seq_len, chunk_start_idx, left_window=left_chunk)
            .unsqueeze(0)
            .expand([batch_size, -1, -1])
        )
        return enc_streaming_mask

    def forward_embeddings(self, hidden_states, masks):
        """Forwarding the inputs through the top embedding layers"""
        seq_len = math.ceil(hidden_states.shape[1] / self.config.time_reduction)
        if seq_len <= 0:
            raise ValueError(
                f"The squence length after time reduction is invalid: {seq_len}. Your input feature is too short."
            )

        batch_size = hidden_states.shape[0]

        enc_streaming_mask = self._streaming_mask(seq_len, batch_size, self.config.chunk_size, self.config.left_chunk)
        enc_streaming_mask = enc_streaming_mask.to(hidden_states.device)

        hidden_states, masks = self.embed(hidden_states, masks)

        streaming_mask = enc_streaming_mask
        if streaming_mask is not None and masks is not None:
            hs_mask = masks & streaming_mask
        elif masks is not None:
            hs_mask = masks
        else:
            hs_mask = streaming_mask

        return hidden_states, hs_mask, masks

    def calculate_hs_mask(self, hidden_states, device, mask):
        max_audio_length = hidden_states.shape[1]
        batch_size = hidden_states.shape[0]
        enc_streaming_mask = self._streaming_mask(
            max_audio_length, batch_size, self.config.chunk_size, self.config.left_chunk
        )
        enc_streaming_mask = enc_streaming_mask.to(device)
        if mask is None:
            return enc_streaming_mask

        feature_lens = mask.sum(1)
        padding_length = feature_lens
        pad_mask = torch.arange(0, max_audio_length, device=device).expand(
            padding_length.size(0), -1
        ) < padding_length.unsqueeze(1)
        pad_mask = pad_mask.unsqueeze(1)
        pad_mask = pad_mask & enc_streaming_mask
        return pad_mask

    def forward(self, hidden_states: torch.Tensor, mask: Optional[torch.Tensor]):
        hidden_states = self.encoder_embedding(hidden_states)
        hidden_states, hs_mask, mask = self.forward_embeddings(hidden_states, mask)

        unfolded = False
        bs, seq_len, _ = hidden_states.shape
        max_seq_len = 500  # maxium position for absolute positional encoding
        if seq_len > max_seq_len:
            # audio sequence is longer than max_seq_len, unfold it into chunks of max_seq_len
            unfolded = True
            # the unfold op will drop residual frames, pad it to the multiple of max_seq_len
            if seq_len % max_seq_len > 0:
                chunk_pad_size = max_seq_len - (seq_len % max_seq_len)
            else:
                chunk_pad_size = 0
            if chunk_pad_size > 0:
                hidden_states_pad = F.pad(hidden_states, (0, 0, 0, chunk_pad_size), "constant", 0)
                hidden_states = hidden_states_pad.to(hidden_states.device)

            hidden_states = unfold_tensor(hidden_states, max_seq_len)
            masks_unfold = None
            if mask is not None:
                # revise hs_mask here because the previous calculated hs_mask did not consider extra pad
                subsampled_pad_mask = mask.squeeze(1)  # [bz, subsampled_unmask_seq_len]
                extra_padded_subsamlped_pad_mask = F.pad(
                    subsampled_pad_mask, (0, chunk_pad_size), "constant", False
                )  # extra padding to the pad mask
                extra_padded_subsamlped_pad_mask = extra_padded_subsamlped_pad_mask.unsqueeze(-1).float()
                masks_unfold = unfold_tensor(
                    extra_padded_subsamlped_pad_mask, max_seq_len
                )  # unfold the pad mask like we did to the input tensor
                masks_unfold = masks_unfold.squeeze(-1).bool()  # unfold op does not support bool tensor
            hs_mask = self.calculate_hs_mask(
                hidden_states, hidden_states.device, masks_unfold
            )  # calculate hs_mask based on the unfolded pad mask

        relative_attention_bias = self.relative_attention_bias_layer(hidden_states)
        attention_mask = hs_mask.unsqueeze(1) + relative_attention_bias

        for layer in self.encoders:
            if self.gradient_checkpointing and self.training:
                hidden_states = self._gradient_checkpointing_func(
                    layer.__call__,
                    hidden_states,
                    attention_mask,
                )
            else:
                hidden_states = layer(hidden_states, attention_mask)

        if unfolded:
            embed_dim = hidden_states.shape[-1]
            hidden_states = hidden_states.reshape(bs, -1, embed_dim)
            # if we ever padded before unfolding, we need to remove the padding
            if chunk_pad_size > 0:
                hidden_states = hidden_states[:, :-chunk_pad_size, :]

        return hidden_states


class Phi4MultimodalAudioEmbedding(nn.Module):
    def __init__(self, config: Phi4MultimodalConfig):
        super().__init__()
        self.config = config
        self.layer_idx = config.audio_config.feature_layer

        self.drop = nn.Dropout(config.embd_pdrop)
        self.encoder = Phi4MultimodalAudioModel._from_config(config.audio_config)
        self.up_proj_for_speech = nn.Linear(
            config.audio_config.hidden_size * config.audio_config.downsample_rate, config.hidden_size
        )
        self.down_proj_for_speech = nn.Linear(config.hidden_size, config.hidden_size)
        self.up_proj_for_vision_speech = nn.Linear(
            config.audio_config.hidden_size * config.audio_config.downsample_rate, config.hidden_size
        )
        self.down_proj_for_vision_speech = nn.Linear(config.hidden_size, config.hidden_size)

    def forward(
        self,
        input_ids: torch.LongTensor,
        inputs_embeds: torch.Tensor,
        audio_input_features: torch.FloatTensor,
        audio_embed_sizes=None,
        audio_attention_mask=None,
        audio_projection_mode="speech",
    ) -> torch.FloatTensor:
        with torch.no_grad():
            positions_tuple = torch.nonzero(input_ids == self.config.audio_config.audio_token_id, as_tuple=True)

        up_proj = self.up_proj_for_speech if audio_projection_mode == "speech" else self.up_proj_for_vision_speech
        down_proj = (
            self.down_proj_for_speech if audio_projection_mode == "speech" else self.down_proj_for_vision_speech
        )

        target_device = up_proj.bias.device
        target_dtype = up_proj.bias.dtype

        audio_input_features = audio_input_features.to(device=target_device, dtype=target_dtype)

        audio_encoder_hidden_states = self.encoder(audio_input_features, audio_attention_mask)
        audio_encoder_hidden_states = up_proj(audio_encoder_hidden_states)
        audio_encoder_hidden_states = nn.functional.gelu(audio_encoder_hidden_states)
        audio_embeds = down_proj(audio_encoder_hidden_states)

        merged_audio_embeds = torch.cat(
            [audio_embeds[i, : audio_embed_sizes[i], :] for i in range(len(audio_embed_sizes))], dim=0
        )
        merged_audio_embeds = merged_audio_embeds.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
        # Temporarily disable autocast to avoid issue on bf16 tensors
        # Ref: https://github.com/pytorch/pytorch/issues/132715
        with torch.autocast(device_type=inputs_embeds.device.type, enabled=False):
            audio_embeds = inputs_embeds.index_put(
                indices=positions_tuple, values=merged_audio_embeds, accumulate=False
            )

        audio_embeds = self.drop(audio_embeds)

        return audio_embeds


class Phi4MultimodalRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        Phi4MultimodalRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

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

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


class Phi4MultimodalMLP(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.config = config
        self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
        self.activation_fn = ACT2FN[config.hidden_act]

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        up_states = self.gate_up_proj(hidden_states)

        gate, up_states = up_states.chunk(2, dim=-1)
        up_states = up_states * self.activation_fn(gate)

        return self.down_proj(up_states)


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


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

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

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

    return attn_output, attn_weights


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

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

    rotary_dim = cos.shape[-1]
    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]

    q_embed = torch.cat([(q_rot * cos) + (rotate_half(q_rot) * sin), q_pass], dim=-1)
    k_embed = torch.cat([(k_rot * cos) + (rotate_half(k_rot) * sin), k_pass], dim=-1)
    return q_embed, k_embed


class Phi4MultimodalAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Phi4MultimodalConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim)
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
        self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=False)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        qkv = self.qkv_proj(hidden_states)
        query_pos = self.config.num_attention_heads * self.head_dim
        query_states = qkv[..., :query_pos]
        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]

        query_states = query_states.view(hidden_shape).transpose(1, 2)
        key_states = key_states.view(hidden_shape).transpose(1, 2)
        value_states = value_states.view(hidden_shape).transpose(1, 2)

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

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

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
                logger.warning_once(
                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
                )
            else:
                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            sliding_window=getattr(self.config, "sliding_window", None),
            **kwargs,
        )

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


class Phi4MultimodalDecoderLayer(nn.Module):
    def __init__(self, config: Phi4MultimodalConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = Phi4MultimodalAttention(config=config, layer_idx=layer_idx)
        self.mlp = Phi4MultimodalMLP(config)
        self.input_layernorm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.config = config
        self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
        self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
                `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
            past_key_value (`Cache`, *optional*): cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence
            kwargs (`dict`, *optional*):
                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
                into the model
        """
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + self.resid_attn_dropout(hidden_states)  # main diff with Llama

        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + self.resid_mlp_dropout(hidden_states)  # main diff with Llama

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs


class Phi4MultimodalFeatureEmbedding(nn.Module):
    """Image-audio embedding."""

    def __init__(self, config: Phi4MultimodalConfig) -> None:
        super().__init__()
        self.config = config
        self.image_token_id = config.vision_config.image_token_id
        self.audio_token_id = config.audio_config.audio_token_id
        self.image_embed = Phi4MultimodalImageEmbedding(config)
        self.audio_embed = Phi4MultimodalAudioEmbedding(config)

    def forward(
        self,
        input_ids: torch.LongTensor,
        inputs_embeds: torch.Tensor,
        image_pixel_values: Optional[torch.FloatTensor] = None,
        audio_input_features: Optional[torch.FloatTensor] = None,
        image_sizes=None,
        image_attention_mask=None,
        audio_embed_sizes=None,
        audio_attention_mask=None,
    ) -> torch.FloatTensor:
        with torch.no_grad():
            image_position_mask = (input_ids == self.config.vision_config.image_token_id).unsqueeze(-1)
            non_image_position_mask = ~image_position_mask

        image_embeds = None
        audio_embeds = None
        if image_pixel_values is not None and (input_ids == self.image_token_id).any():
            image_embeds = self.image_embed(
                input_ids,
                inputs_embeds,
                image_pixel_values=image_pixel_values,
                image_sizes=image_sizes,
                image_attention_mask=image_attention_mask,
            )
        if audio_input_features is not None and (input_ids == self.audio_token_id).any():
            audio_projection_mode = "vision" if image_pixel_values is not None else "speech"
            audio_embeds = self.audio_embed(
                input_ids,
                inputs_embeds,
                audio_input_features=audio_input_features,
                audio_embed_sizes=audio_embed_sizes,
                audio_attention_mask=audio_attention_mask,
                audio_projection_mode=audio_projection_mode,
            )

        # merge image and audio
        if image_embeds is not None and audio_embeds is not None:
            inputs_embeds = image_embeds * image_position_mask + audio_embeds * non_image_position_mask
        elif image_embeds is not None:
            inputs_embeds = image_embeds
        elif audio_embeds is not None:
            inputs_embeds = audio_embeds

        return inputs_embeds


class Phi4MultimodalRotaryEmbedding(nn.Module):
    def __init__(self, config: Phi4MultimodalConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

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

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

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


PHI4_MULTIMODAL_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`Phi4MultimodalConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare Phi4Multimodal Model outputting raw hidden-states without any specific head on top.",
    PHI4_MULTIMODAL_START_DOCSTRING,
)
class Phi4MultimodalPreTrainedModel(PreTrainedModel):
    config_class = Phi4MultimodalConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Phi4MultimodalDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_flex_attn = True
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = True
    _supports_attention_backend = True
    _version = "0.0.5"

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, Phi4MultimodalRMSNorm):
            module.weight.data.fill_(1.0)
        elif isinstance(module, Phi4MultimodalImageEmbedding):
            module.global_img_feature_extensor.data.zero_()
            module.sub_img_feature_extensor.data.zero_()


PHI4_MULTIMODAL_MODEL_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

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

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding indices in `input_values`. Mask values selected in `[0, 1]`:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
            [What are attention masks?](../glossary#attention-mask)
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`Cache`)`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
            See our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);

            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
            of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        image_pixel_values (`torch.FloatTensor`, *optional*):
            If the input contains images, these correspond to the pixel values after transformations (as returned by
            the Processor)
        image_sizes (`torch.LongTensor`, *optional*):
            If the input contains images, these correspond to size of each image.
        image_attention_mask (`torch.LongTensor`, *optional*):
            Attention mask for the images.
        audio_input_features (`torch.FloatTensor`, *optional*):
            If the input contains audio samples, these correspond to the values after transformation (as returned by
            the Processor).
        audio_embed_sizes (`torch.Tensor`, *optional*):
            Size of the audio inputs.
        audio_attention_mask (`torch.Tensor, *optional*):
            Attention mask for the audio inputs.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
            the complete sequence length.
"""


@add_start_docstrings(
    "The bare Phi4Multimodal Model outputting raw hidden-states without any specific head on top.",
    PHI4_MULTIMODAL_START_DOCSTRING,
)
class Phi4MultimodalModel(Phi4MultimodalPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi4MultimodalMMDecoderLayer`]
    Args:
        config: Phi4MultimodalMMConfig
    """

    def __init__(self, config: Phi4MultimodalConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)

        self.layers = nn.ModuleList(
            [Phi4MultimodalDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = Phi4MultimodalRotaryEmbedding(config=config)

        self.gradient_checkpointing = False
        self.embed_dropout = nn.Dropout(config.embd_pdrop)

        self.embed_tokens_extend = Phi4MultimodalFeatureEmbedding(config)

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

    def get_input_embeddings(self):
        return self.embed_tokens

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

    @can_return_tuple
    @add_start_docstrings_to_model_forward(PHI4_MULTIMODAL_MODEL_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        image_pixel_values: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[torch.LongTensor] = None,
        image_attention_mask=None,
        audio_input_features: Optional[torch.FloatTensor] = None,
        audio_embed_sizes=None,
        audio_attention_mask=None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> BaseModelOutputWithPast:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
            inputs_embeds = self.embed_tokens_extend(
                input_ids,
                inputs_embeds,
                image_pixel_values=image_pixel_values,
                audio_input_features=audio_input_features,
                image_sizes=image_sizes,
                image_attention_mask=image_attention_mask,
                audio_embed_sizes=audio_embed_sizes,
                audio_attention_mask=audio_attention_mask,
            )

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

        causal_mask = self._update_causal_mask(
            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )

        hidden_states = inputs_embeds

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

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                    position_embeddings=position_embeddings,
                    **kwargs,
                )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )

    def _update_causal_mask(
        self,
        attention_mask: Union[torch.Tensor, "BlockMask"],
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool = False,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and past_key_values is not None:
                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
                if is_padding_right:
                    raise ValueError(
                        "You are attempting to perform batched generation with padding_side='right'"
                        " this may lead to unexpected behaviour for Flash Attention version of Phi4Multimodal. Make sure to "
                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                    )
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None
        if self.config._attn_implementation == "flex_attention":
            if isinstance(attention_mask, torch.Tensor):
                attention_mask = make_flex_block_causal_mask(attention_mask)
            return attention_mask

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)
        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if (
            self.config._attn_implementation == "sdpa"
            and not (using_static_cache or using_sliding_window_cache)
            and not output_attentions
        ):
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                sliding_window=self.config.sliding_window,
                is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        # SlidingWindowCache or StaticCache
        if using_sliding_window_cache or using_static_cache:
            target_length = past_key_values.get_max_cache_shape()
        # DynamicCache or no cache
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            device=device,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
            config=self.config,
            past_key_values=past_key_values,
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type in ["cuda", "xpu", "npu"]
            and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        device: torch.device,
        cache_position: torch.Tensor,
        batch_size: int,
        config: Phi4MultimodalConfig,
        past_key_values: Cache,
    ):
        """
        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            device (`torch.device`):
                The device to place the 4D attention mask on.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
            config (`Phi4MultimodalConfig`):
                The model's configuration class
            past_key_values (`Cache`):
                The cache class that is being used currently to generate
        """
        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
            )
            diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
            if config.get_text_config().sliding_window is not None:
                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
                # the check is needed to verify is current checkpoint was trained with sliding window or not
                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
                    sliding_attend_mask = torch.arange(target_length, device=device) <= (
                        cache_position.reshape(-1, 1) - config.get_text_config().sliding_window
                    )
                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
            causal_mask *= diagonal_attend_mask
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                if attention_mask.shape[-1] > target_length:
                    attention_mask = attention_mask[:, :target_length]
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
                    causal_mask.device
                )
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )
        return causal_mask


class Phi4MultimodalForCausalLM(Phi4MultimodalPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = Phi4MultimodalModel(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()

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

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

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @can_return_tuple
    @add_start_docstrings_to_model_forward(PHI4_MULTIMODAL_MODEL_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=Phi4MultimodalConfig)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        image_pixel_values: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[torch.LongTensor] = None,
        image_attention_mask=None,
        audio_input_features: Optional[torch.FloatTensor] = None,
        audio_embed_sizes=None,
        audio_attention_mask=None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs,
    ) -> CausalLMOutputWithPast:
        r"""
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

            logits_to_keep (`int` or `torch.Tensor`, *optional*):
                If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
                This is useful when using packed tensor format (single dimension for batch and sequence length).
        Returns:

        Example:
        ```python
        >>> from transformers import AutoTokenizer, Phi4MultimodalForCausalLM
        >>> model = Phi4MultimodalForCausalLM.from_pretrained("TBA")
        >>> tokenizer = AutoTokenizer.from_pretrained("TBA")
        >>> prompt = "This is an example script ."
        >>> 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]
        'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum'
        ```"""

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs: BaseModelOutputWithPast = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            image_pixel_values=image_pixel_values,
            image_sizes=image_sizes,
            image_attention_mask=image_attention_mask,
            audio_input_features=audio_input_features,
            audio_embed_sizes=audio_embed_sizes,
            audio_attention_mask=audio_attention_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            cache_position=cache_position,
            **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, :])

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

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

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        image_pixel_values=None,
        image_sizes=None,
        image_attention_mask=None,
        audio_input_features=None,
        audio_embed_sizes=None,
        audio_attention_mask=None,
        cache_position=None,
        position_ids=None,
        use_cache=True,
        logits_to_keep=0,
        **kwargs,
    ):
        # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the
        # process

        # When the first time input length reached long and short factor switching point, enforce re-compute cache
        # It will cause downside of slower at this single token position, however, better than current failure.
        if (
            past_key_values
            and self.config.rope_scaling
            and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1
        ):
            past_length = cache_position[0]
            if past_length <= self.config.original_max_position_embeddings:
                past_key_values = None

        model_inputs = super().prepare_inputs_for_generation(
            input_ids=input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            image_pixel_values=image_pixel_values,
            image_sizes=image_sizes,
            image_attention_mask=image_attention_mask,
            audio_input_features=audio_input_features,
            audio_embed_sizes=audio_embed_sizes,
            audio_attention_mask=audio_attention_mask,
            cache_position=cache_position,
            position_ids=position_ids,
            use_cache=use_cache,
            logits_to_keep=logits_to_keep,
            **kwargs,
        )
        return model_inputs


__all__ = [
    "Phi4MultimodalAudioPreTrainedModel",
    "Phi4MultimodalAudioModel",
    "Phi4MultimodalVisionPreTrainedModel",
    "Phi4MultimodalVisionModel",
    "Phi4MultimodalPreTrainedModel",
    "Phi4MultimodalModel",
    "Phi4MultimodalForCausalLM",
]


Phi4MultimodalForCausalLM.register_for_auto_class("AutoModelForCausalLM")