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# Copyright 2026 The LG AI Research and HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from collections.abc import Callable

import torch
from torch import nn

from ...activations import ACT2FN
from ...cache_utils import Cache
from ...generation import GenerationMixin
from ...integrations import use_kernel_forward_from_hub
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, CausalLMOutputWithPast
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check
from ...utils.generic import (
    accepts_precomputed_kwargs,
    get_max_seqlen,
    is_flash_attention_requested,
    merge_with_config_defaults,
)
from ...utils.output_capturing import capture_outputs
from ...vision_utils import get_vision_attention_seqlens, get_vision_position_ids, get_vision_window_index
from ..auto import AutoModel
from .configuration_exaone4_5 import Exaone4_5_Config, Exaone4_5_VisionConfig


class Exaone4_5_PatchEmbed(nn.Module):
    def __init__(
        self,
        patch_size: int | list[int] | tuple[int, int] = 14,
        temporal_patch_size: int | list[int] | tuple[int, int] = 2,
        in_channels: int = 3,
        embed_dim: int = 1152,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.in_channels = in_channels
        self.embed_dim = embed_dim

        kernel_size = [temporal_patch_size, patch_size, patch_size]
        self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        target_dtype = self.proj.weight.dtype
        hidden_states = hidden_states.view(
            -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size
        )
        hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim)
        return hidden_states


class Exaone4_5_VisionRotaryEmbedding(nn.Module):
    """
    Simple axial 2D rope with same freqs used for H and W grids. The freqs are
    pre-computed using `head-dim//4` which is later used to concat H and W positions.
    The final angles rotate over the whole head dim, no partial rotation involved.
    """

    def __init__(self, config: Exaone4_5_VisionConfig):
        super().__init__()
        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        rope_init_fn: Callable = self.compute_axial_rope_parameters
        if self.rope_type != "axial":
            raise ValueError(f"{self.__class__.__name__} supports only axial rope, but requested {self.rope_type}")
        inv_freq, self.attention_scaling = rope_init_fn(self.config)

        self.inv_freq = nn.Buffer(inv_freq, persistent=False)
        self.original_inv_freq = nn.Buffer(inv_freq.clone(), persistent=False)

    @staticmethod
    def compute_axial_rope_parameters(
        config: Exaone4_5_VisionConfig, device=None, **kwargs
    ) -> tuple[torch.Tensor, float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
        spatial_dim = dim // 2

        attention_factor = 1.0  # Unused in this type of RoPE
        inv_freq = 1.0 / (base ** (torch.arange(0, spatial_dim, 2, dtype=torch.float) / spatial_dim))
        return inv_freq.to(device), attention_factor

    @torch.no_grad()
    def forward(self, x, position_ids):
        # position_ids: (2, N) — row 0 = h coords, row 1 = w coords
        freqs = position_ids[..., None].float() * self.inv_freq.to(device=x.device, dtype=torch.float)
        cos = freqs.cos() * self.attention_scaling
        sin = freqs.sin() * self.attention_scaling

        cos = self.recomposition_frequencies(cos)
        sin = self.recomposition_frequencies(sin)
        return cos, sin

    def recomposition_frequencies(self, freq):
        """
        Recompose the frequencies into the final spatial layout used per each grid.
        """
        freq_h, freq_w = freq[:, 0], freq[:, 1]
        freq_hw = torch.cat([freq_h, freq_w], dim=-1)
        return torch.cat([freq_hw, freq_hw], dim=-1)


@use_kernel_forward_from_hub("RMSNorm")
class Exaone4_5_RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
        """
        Exaone4_5_RMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

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

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


class Exaone4_5_PatchMerger(nn.Module):
    def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
        super().__init__()
        self.hidden_size = context_dim * (spatial_merge_size**2)
        self.ln_q = Exaone4_5_RMSNorm(context_dim, eps=1e-6)
        self.mlp = nn.Sequential(
            nn.Linear(self.hidden_size, self.hidden_size),
            nn.GELU(),
            nn.Linear(self.hidden_size, dim),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.mlp(self.ln_q(x).view(-1, self.hidden_size))
        return x


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


def apply_rotary_pos_emb_vision(
    q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
    orig_q_dtype = q.dtype
    orig_k_dtype = k.dtype
    q, k = q.float(), k.float()
    cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    q_embed = q_embed.to(orig_q_dtype)
    k_embed = k_embed.to(orig_k_dtype)
    return q_embed, k_embed


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


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    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:
        attn_weights = attn_weights + attention_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


class Exaone4_5_VisionAttention(nn.Module):
    def __init__(self, config: Exaone4_5_VisionConfig) -> None:
        super().__init__()
        self.num_key_value_heads = config.num_key_value_heads
        self.dim = config.hidden_size
        self.num_heads = config.num_heads
        self.head_dim = self.dim // self.num_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.proj = nn.Linear(self.dim, self.dim)
        self.scaling = self.head_dim**-0.5
        self.config = config
        self.attention_dropout = 0.0
        self.is_causal = False
        self.q_dim = self.num_heads * self.head_dim
        self.kv_dim = self.num_key_value_heads * self.head_dim
        self.qkv = nn.Linear(self.dim, self.q_dim + (self.kv_dim * 2), bias=True)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        max_seqlen: int | None = None,
        **kwargs,
    ) -> torch.Tensor:
        seq_length = hidden_states.shape[0]
        hidden_shape = (seq_length, -1, self.head_dim)

        query_states, key_states, value_states = self.qkv(hidden_states).split(
            [self.q_dim, self.kv_dim, self.kv_dim], dim=-1
        )

        query_states = query_states.view(hidden_shape)
        key_states = key_states.view(hidden_shape)
        value_states = value_states.view(hidden_shape)

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

        query_states = query_states.transpose(0, 1).unsqueeze(0)
        key_states = key_states.transpose(0, 1).unsqueeze(0)
        value_states = value_states.transpose(0, 1).unsqueeze(0)

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

        if is_flash_attention_requested(self.config):
            max_seqlen = get_max_seqlen(cu_seqlens, self.config, kwargs={"max_seqlen": max_seqlen})
            attn_output, _ = attention_interface(
                self,
                query_states,
                key_states,
                value_states,
                attention_mask=None,
                scaling=self.scaling,
                dropout=0.0 if not self.training else self.attention_dropout,
                cu_seq_lens_q=cu_seqlens,
                cu_seq_lens_k=cu_seqlens,
                max_length_q=max_seqlen,
                max_length_k=max_seqlen,
                is_causal=False,
                **kwargs,
            )
        else:
            lengths = cu_seqlens[1:] - cu_seqlens[:-1]
            splits = [
                torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
            ]
            attn_outputs = [
                attention_interface(
                    self,
                    q,
                    k,
                    v,
                    attention_mask=None,
                    scaling=self.scaling,
                    dropout=0.0 if not self.training else self.attention_dropout,
                    is_causal=False,
                    **kwargs,
                )[0]
                for q, k, v in zip(*splits)
            ]
            attn_output = torch.cat(attn_outputs, dim=1)

        attn_output = attn_output.reshape(seq_length, -1).contiguous()
        return self.proj(attn_output)


class Exaone4_5_MLP(nn.Module):
    def __init__(self, config, bias: bool = False):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias)
        self.act_fn = ACT2FN[config.hidden_act]

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


class Exaone4_5_VisionBlock(GradientCheckpointingLayer):
    def __init__(self, config, attn_implementation: str = "sdpa") -> None:
        super().__init__()
        self.norm1 = Exaone4_5_RMSNorm(config.hidden_size, eps=1e-6)
        self.norm2 = Exaone4_5_RMSNorm(config.hidden_size, eps=1e-6)
        self.attn = Exaone4_5_VisionAttention(config=config)
        self.mlp = Exaone4_5_MLP(config, bias=True)

    @auto_docstring
    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        **kwargs,
    ) -> torch.Tensor:
        r"""
        cu_seqlens (`torch.Tensor`):
            Cumulative sequence lengths used for packed variable-length attention in Flash Attention kernels.
        """
        hidden_states = hidden_states + self.attn(
            self.norm1(hidden_states),
            cu_seqlens=cu_seqlens,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
        return hidden_states


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

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


class Exaone4_5_Attention(nn.Module):
    def __init__(self, config: Exaone4_5_Config, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.num_attention_heads = config.num_attention_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.hidden_size = config.hidden_size
        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.attention_dropout = config.attention_dropout
        self.is_causal = True
        self.scaling = self.head_dim**-0.5
        self.sliding_window = config.sliding_window
        self.sliding_window_pattern = config.sliding_window_pattern
        layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
        self.is_sliding = layer_type == "sliding_attention"

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

        self.q_norm = Exaone4_5_RMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = Exaone4_5_RMSNorm(self.head_dim, eps=config.rms_norm_eps)

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

        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)

        # We use QK-norm
        query_states = self.q_norm(query_states)
        key_states = self.k_norm(key_states)

        cos, sin = position_embeddings
        # We use global NoPE for hybrid attention model
        if self.sliding_window is None or self.is_sliding:
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_values is not None:
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)

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

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

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


class Exaone4_5_DecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: Exaone4_5_Config, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = Exaone4_5_Attention(config=config, layer_idx=layer_idx)

        self.mlp = Exaone4_5_MLP(config)
        self.post_attention_layernorm = Exaone4_5_RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_feedforward_layernorm = Exaone4_5_RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

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


@auto_docstring
class Exaone4_5_PreTrainedModel(PreTrainedModel):
    config: Exaone4_5_Config
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Exaone4_5_VisionBlock", "Exaone4_5_DecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": Exaone4_5_DecoderLayer,
        "attentions": Exaone4_5_Attention,
    }
    config_class = Exaone4_5_Config
    _keys_to_ignore_on_load_unexpected = [r"mtp.*"]


class Exaone4_5_VisionModel(Exaone4_5_PreTrainedModel):
    config: Exaone4_5_VisionConfig
    _no_split_modules = ["Exaone4_5_VisionBlock"]
    _input_embed_layer = "patch_embed"
    _can_record_outputs = {
        "hidden_states": Exaone4_5_VisionBlock,
        "attentions": Exaone4_5_VisionAttention,
    }
    config_class = Exaone4_5_VisionConfig

    def __init__(self, config: Exaone4_5_VisionConfig, *inputs, **kwargs) -> None:
        super().__init__(config, *inputs, **kwargs)
        self.spatial_merge_size = config.spatial_merge_size
        self.patch_size = config.patch_size
        self.fullatt_block_indexes = config.fullatt_block_indexes
        self.window_size = config.window_size
        self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
        self.patch_embed = Exaone4_5_PatchEmbed(
            patch_size=config.patch_size,
            temporal_patch_size=config.temporal_patch_size,
            in_channels=config.in_channels,
            embed_dim=config.hidden_size,
        )
        self.rotary_pos_emb = Exaone4_5_VisionRotaryEmbedding(config)
        self.blocks = nn.ModuleList([Exaone4_5_VisionBlock(config) for _ in range(config.depth)])
        self.merger = Exaone4_5_PatchMerger(
            dim=config.out_hidden_size,
            context_dim=config.hidden_size,
            spatial_merge_size=config.spatial_merge_size,
        )
        self.gradient_checkpointing = False

        self.post_init()

    def permute_input_for_window_attn(self, input_tensor: torch.Tensor, window_index: torch.Tensor, seq_len: int):
        input_tensor = input_tensor.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
        input_tensor = input_tensor[window_index, :, :]
        input_tensor = input_tensor.reshape(seq_len, -1)
        return input_tensor

    @merge_with_config_defaults
    @capture_outputs
    def forward(
        self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
    ) -> tuple | BaseModelOutputWithPooling:
        """
        Args:
            hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`):
                The final hidden states of the model.
            grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
                The temporal, height and width of feature shape of each image in LLM.

        Returns:
            `torch.Tensor`: hidden_states.
        """
        position_ids = get_vision_position_ids(grid_thw, self.spatial_merge_size, kwargs=kwargs)
        cu_seqlens, max_seqlen = get_vision_attention_seqlens(grid_thw, self.config, kwargs=kwargs)
        window_index, cu_window_seqlens = get_vision_window_index(
            grid_thw,
            spatial_merge_size=self.spatial_merge_size,
            window_size=self.window_size,
            patch_size=self.patch_size,
            kwargs=kwargs,
        )
        max_window_seqlen = get_max_seqlen(
            cu_window_seqlens, self.config, kwargs=kwargs, kwarg_name="max_window_seqlen"
        )

        hidden_states = self.patch_embed(hidden_states)

        seq_len, _ = hidden_states.size()
        hidden_states = self.permute_input_for_window_attn(hidden_states, window_index, seq_len=seq_len)

        position_embeddings = self.rotary_pos_emb(hidden_states, position_ids)
        position_embeddings = tuple(
            self.permute_input_for_window_attn(freq, window_index, seq_len=seq_len) for freq in position_embeddings
        )

        for layer_num, blk in enumerate(self.blocks):
            if layer_num in self.fullatt_block_indexes:
                cu_seqlens_now = cu_seqlens
                max_seqlen_now = max_seqlen
            else:
                cu_seqlens_now = cu_window_seqlens
                max_seqlen_now = max_window_seqlen

            hidden_states = blk(
                hidden_states,
                cu_seqlens=cu_seqlens_now,
                max_seqlen=max_seqlen_now,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        reverse_indices = torch.argsort(window_index)
        merged_hidden_states = self.merger(hidden_states)
        merged_hidden_states = merged_hidden_states[reverse_indices, :]

        return BaseModelOutputWithPooling(
            last_hidden_state=hidden_states,
            pooler_output=merged_hidden_states,
        )


@auto_docstring
class Exaone4_5_Model(Exaone4_5_PreTrainedModel):
    base_model_prefix = "model"
    # Reference: fix gemma3 grad acc #37208
    accepts_loss_kwargs = False

    def __init__(self, config: Exaone4_5_Config):
        super().__init__(config)
        self.visual = Exaone4_5_VisionModel._from_config(config.vision_config)
        self.language_model = AutoModel.from_config(config.text_config)
        self.rope_deltas = None  # cache rope_deltas here

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

    @accepts_precomputed_kwargs(modality="video")
    @can_return_tuple
    @auto_docstring
    def get_video_features(
        self,
        pixel_values_videos: torch.FloatTensor,
        video_grid_thw: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
        vision_outputs = self.visual(pixel_values_videos, grid_thw=video_grid_thw, **kwargs)
        split_sizes = (video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist()
        vision_outputs.pooler_output = torch.split(vision_outputs.pooler_output, split_sizes)

        return vision_outputs

    @accepts_precomputed_kwargs(modality="image")
    @can_return_tuple
    @auto_docstring
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_grid_thw: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        pixel_values = pixel_values.type(self.visual.dtype)
        vision_outputs = self.visual(pixel_values, grid_thw=image_grid_thw, **kwargs)
        split_sizes = (image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist()
        vision_outputs.pooler_output = torch.split(vision_outputs.pooler_output, split_sizes)

        return vision_outputs

    def get_placeholder_mask(
        self,
        input_ids: torch.LongTensor,
        inputs_embeds: torch.FloatTensor,
        image_features: torch.FloatTensor | None = None,
        video_features: torch.FloatTensor | None = None,
    ):
        """
        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
        equal to the length of multimodal features. If the lengths are different, an error is raised.
        """
        if input_ids is None:
            special_image_mask = inputs_embeds == self.get_input_embeddings()(
                torch.full((), self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
            )
            special_image_mask = special_image_mask.all(-1)
            special_video_mask = inputs_embeds == self.get_input_embeddings()(
                torch.full((), self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
            )
            special_video_mask = special_video_mask.all(-1)
        else:
            special_image_mask = input_ids == self.config.image_token_id
            special_video_mask = input_ids == self.config.video_token_id

        n_image_tokens = special_image_mask.sum()
        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
        if image_features is not None:
            torch_compilable_check(
                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
            )

        n_video_tokens = special_video_mask.sum()
        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
        if video_features is not None:
            torch_compilable_check(
                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
            )
        return special_image_mask, special_video_mask

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        pixel_values: torch.Tensor | None = None,
        pixel_values_videos: torch.FloatTensor | None = None,
        image_grid_thw: torch.LongTensor | None = None,
        video_grid_thw: torch.LongTensor | None = None,
        mm_encoder_outputs: dict[str, BaseModelOutputWithPooling] | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if (pixel_values is not None or pixel_values_videos is not None) and mm_encoder_outputs is not None:
            raise ValueError(
                "You cannot specify both pixel_values/pixel_values_videos and mm_encoder_outputs at the same time"
            )

        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)

        mm_encoder_outputs = mm_encoder_outputs if mm_encoder_outputs is not None else {}
        if mm_encoder_outputs.get("image") is None and pixel_values is not None:
            mm_encoder_outputs["image"] = self.get_image_features(
                pixel_values, image_grid_thw, return_dict=True, **kwargs
            )

        if mm_encoder_outputs.get("video") is None and pixel_values_videos is not None:
            mm_encoder_outputs["video"] = self.get_video_features(
                pixel_values_videos, video_grid_thw, return_dict=True, **kwargs
            )

        if mm_encoder_outputs.get("image") is not None:
            image_embeds = torch.cat(mm_encoder_outputs["image"].pooler_output, dim=0).to(
                inputs_embeds.device, inputs_embeds.dtype
            )
            image_mask, _ = self.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
            )
            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)

        if mm_encoder_outputs.get("video") is not None:
            video_embeds = torch.cat(mm_encoder_outputs["video"].pooler_output, dim=0).to(
                inputs_embeds.device, inputs_embeds.dtype
            )
            _, video_mask = self.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
            )
            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)

        # Differ from Qwen: vision encoder uses 2D rotary positional embeddings (2D-RoPE)
        if position_ids is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            position_ids = position_ids.unsqueeze(0)

        outputs = self.language_model(
            input_ids=None,
            position_ids=position_ids,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            **kwargs,
        )

        return BaseModelOutputWithPast(
            last_hidden_state=outputs.last_hidden_state,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class Exaone4_5_ForConditionalGeneration(Exaone4_5_PreTrainedModel, GenerationMixin):
    """
    Main EXAONE 4.5 conditional generation class.

    Note: Unlike Qwen2VL, the EXAONE 4.5 vision encoder uses 2D rotary positional embeddings (2D-RoPE)
    and adopts a Grouped Query Attention (GQA) structure throughout the multimodal stack.
    """

    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
    _tp_plan = {"lm_head": "colwise_gather_output"}
    # Reference: fix gemma3 grad acc #37208
    accepts_loss_kwargs = False

    def __init__(self, config):
        super().__init__(config)
        self.model = Exaone4_5_Model(config)
        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)

        self.post_init()

    @auto_docstring
    def get_video_features(
        self,
        pixel_values_videos: torch.FloatTensor,
        video_grid_thw: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
            The tensors corresponding to the input videos.
        """
        return self.model.get_video_features(pixel_values_videos, video_grid_thw, **kwargs)

    @auto_docstring
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        image_grid_thw: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
            The tensors corresponding to the input images.
        """
        return self.model.get_image_features(pixel_values, image_grid_thw, **kwargs)

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        pixel_values: torch.Tensor | None = None,
        pixel_values_videos: torch.FloatTensor | None = None,
        image_grid_thw: torch.LongTensor | None = None,
        video_grid_thw: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        mm_encoder_outputs: dict[str, BaseModelOutputWithPooling] | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | CausalLMOutputWithPast:
        r"""
        Example:

        ```python
        >>> from transformers import AutoProcessor, Exaone4_5_ForConditionalGeneration
        >>> import torch

        >>> model = Exaone4_5_ForConditionalGeneration.from_pretrained("LGAI-EXAONE/EXAONE-4.5-33B")
        >>> processor = AutoProcessor.from_pretrained("LGAI-EXAONE/EXAONE-4.5-33B")

        >>> messages = [
        ...     {
        ...         "role": "user",
        ...         "content": [
        ...             {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
        ...             {"type": "text", "text": "Describe the image."},
        ...         ],
        ...     }
        ... ]
        >>> inputs = processor.apply_chat_template(
        ...     messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
        ... )
        >>> inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
        >>> generated_ids = model.generate(**inputs, max_new_tokens=64)
        ```
        """
        outputs = self.model(
            input_ids=input_ids,
            pixel_values=pixel_values,
            pixel_values_videos=pixel_values_videos,
            image_grid_thw=image_grid_thw,
            video_grid_thw=video_grid_thw,
            position_ids=position_ids,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            mm_encoder_outputs=mm_encoder_outputs,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        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=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
            )

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

    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        model_inputs = super().prepare_inputs_for_generation(input_ids, **kwargs)
        # Force recomputation of 2D-RoPE and ignore rope_deltas
        model_inputs["position_ids"] = None
        return model_inputs


__all__ = [
    "Exaone4_5_ForConditionalGeneration",
    "Exaone4_5_Model",
    "Exaone4_5_PreTrainedModel",
    "Exaone4_5_VisionModel",
]