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# Copyright 2026 The PaddlePaddle Team 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 collections
import math
from dataclasses import dataclass

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

from ... import initialization as init
from ...activations import ACT2CLS, ACT2FN
from ...backbone_utils import filter_output_hidden_states
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, ModelOutput
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.generic import merge_with_config_defaults
from ...utils.output_capturing import capture_outputs
from .configuration_slanext import SLANeXtConfig, SLANeXtVisionConfig


class SLANeXtVisionAttention(nn.Module):
    """Multi-head Attention block with relative position embeddings."""

    def __init__(self, config, window_size):
        super().__init__()
        input_size = (
            (config.image_size // config.patch_size, config.image_size // config.patch_size)
            if window_size == 0
            else (window_size, window_size)
        )

        self.num_attention_heads = config.num_attention_heads
        head_dim = config.hidden_size // config.num_attention_heads
        self.scale = head_dim**-0.5
        self.dropout = config.attention_dropout

        self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias)
        self.proj = nn.Linear(config.hidden_size, config.hidden_size)

        self.use_rel_pos = config.use_rel_pos
        if self.use_rel_pos:
            if input_size is None:
                raise ValueError("Input size must be provided if using relative positional encoding.")

            # initialize relative positional embeddings
            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))

    def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
        """
        Get relative positional embeddings according to the relative positions of
            query and key sizes.

        Args:
            q_size (int):
                size of the query.
            k_size (int):
                size of key k.
            rel_pos (`torch.Tensor`):
                relative position embeddings (L, channel).

        Returns:
            Extracted positional embeddings according to relative positions.
        """
        max_rel_dist = int(2 * max(q_size, k_size) - 1)
        # Interpolate rel pos.
        rel_pos_resized = F.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
            size=max_rel_dist,
            mode="linear",
        )
        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)

        # Scale the coords with short length if shapes for q and k are different.
        q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
        k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
        relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

        return rel_pos_resized[relative_coords.long()]

    def get_decomposed_rel_pos(
        self,
        query: torch.Tensor,
        rel_pos_h: torch.Tensor,
        rel_pos_w: torch.Tensor,
        q_size: tuple[int, int],
        k_size: tuple[int, int],
    ) -> torch.Tensor:
        """
        Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
        https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py

        Args:
            query (`torch.Tensor`):
                query q in the attention layer with shape (batch_size, query_height * query_width, channel).
            rel_pos_h (`torch.Tensor`):
                relative position embeddings (Lh, channel) for height axis.
            rel_pos_w (`torch.Tensor`):
                relative position embeddings (Lw, channel) for width axis.
            q_size (tuple):
                spatial sequence size of query q with (query_height, query_width).
            k_size (tuple):
                spatial sequence size of key k with (key_height, key_width).

        Returns:
            decomposed_rel_pos (`torch.Tensor`):
                decomposed relative position embeddings.
        """
        query_height, query_width = q_size
        key_height, key_width = k_size
        relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
        relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w)

        batch_size, _, dim = query.shape
        reshaped_query = query.reshape(batch_size, query_height, query_width, dim)
        rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height)
        rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width)

        decomposed_rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]

        return decomposed_rel_pos

    def forward(self, hidden_states: torch.Tensor, output_attentions=None) -> tuple[torch.Tensor, torch.Tensor]:
        batch_size, height, width, _ = hidden_states.shape
        # qkv with shape (3, batch_size, nHead, height * width, channel)
        qkv = (
            self.qkv(hidden_states)
            .reshape(batch_size, height * width, 3, self.num_attention_heads, -1)
            .permute(2, 0, 3, 1, 4)
        )
        # q, k, v with shape (batch_size * nHead, height * width, channel)
        query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0)

        attn_weights = (query * self.scale) @ key.transpose(-2, -1)

        if self.use_rel_pos:
            decomposed_rel_pos = self.get_decomposed_rel_pos(
                query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
            )
            decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights)
            attn_weights = attn_weights + decomposed_rel_pos

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

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
        attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)

        attn_output = self.proj(attn_output)
        return attn_output, attn_weights


class SLANeXtAttentionGRUCell(nn.Module):
    def __init__(self, input_size, hidden_size, num_embeddings):
        super().__init__()

        self.input_to_hidden = nn.Linear(input_size, hidden_size, bias=False)
        self.hidden_to_hidden = nn.Linear(hidden_size, hidden_size)
        self.score = nn.Linear(hidden_size, 1, bias=False)

        self.rnn = nn.GRUCell(input_size + num_embeddings, hidden_size)

    def forward(
        self,
        prev_hidden: torch.FloatTensor,
        batch_hidden: torch.FloatTensor,
        char_onehots: torch.FloatTensor,
        **kwargs: Unpack[TransformersKwargs],
    ):
        batch_hidden_proj = self.input_to_hidden(batch_hidden)
        prev_hidden_proj = self.hidden_to_hidden(prev_hidden).unsqueeze(1)

        attention_scores = batch_hidden_proj + prev_hidden_proj
        attention_scores = torch.tanh(attention_scores)
        attention_scores = self.score(attention_scores)

        attn_weights = F.softmax(attention_scores, dim=1, dtype=torch.float32).to(attention_scores.dtype)
        attn_weights = attn_weights.transpose(1, 2)
        context = torch.matmul(attn_weights, batch_hidden).squeeze(1)
        concat_context = torch.cat([context, char_onehots], 1)
        hidden_states = self.rnn(concat_context, prev_hidden)

        return hidden_states, attn_weights


class SLANeXtMLP(nn.Module):
    def __init__(self, hidden_size, out_channels, activation=None):
        super().__init__()
        self.fc1 = nn.Linear(hidden_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, out_channels)
        self.act_fn = nn.Identity() if activation is None else ACT2CLS[activation]()

    def forward(self, hidden_states):
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.act_fn(hidden_states)
        return hidden_states


class SLANeXtPreTrainedModel(PreTrainedModel):
    config: SLANeXtConfig
    base_model_prefix = "backbone"
    main_input_name = "pixel_values"
    input_modalities = ("image",)
    supports_gradient_checkpointing = True
    _keep_in_fp32_modules_strict = ["structure_attention_cell", "structure_generator"]

    @torch.no_grad()
    def _init_weights(self, module):
        """Initialize the weights"""
        super()._init_weights(module)

        # Initialize positional embeddings to zero (SLANeXtVisionEncoder holds pos_embed)
        if isinstance(module, SLANeXtVisionEncoder):
            if module.pos_embed is not None:
                init.constant_(module.pos_embed, 0.0)

        # Initialize relative positional embeddings to zero (SLANeXtVisionAttention holds rel_pos_h/w)
        if isinstance(module, SLANeXtVisionAttention):
            if module.use_rel_pos:
                init.constant_(module.rel_pos_h, 0.0)
                init.constant_(module.rel_pos_w, 0.0)

        # Initialize GRUCell (replicates PyTorch default reset_parameters)
        if isinstance(module, nn.GRUCell):
            std = 1.0 / math.sqrt(module.hidden_size) if module.hidden_size > 0 else 0
            init.uniform_(module.weight_ih, -std, std)
            init.uniform_(module.weight_hh, -std, std)
            if module.bias_ih is not None:
                init.uniform_(module.bias_ih, -std, std)
            if module.bias_hh is not None:
                init.uniform_(module.bias_hh, -std, std)

        # Initialize SLAHead layers
        if isinstance(module, SLANeXtSLAHead):
            std = 1.0 / math.sqrt(self.config.hidden_size * 1.0)
            # Initialize structure_generator and loc_generator layers
            for generator in (module.structure_generator,):
                for layer in generator.children():
                    if isinstance(layer, nn.Linear):
                        init.uniform_(layer.weight, -std, std)
                        if layer.bias is not None:
                            init.uniform_(layer.bias, -std, std)


class SLANeXtMLPBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim)
        self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size)
        self.act = ACT2FN[config.hidden_act]

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.lin1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.lin2(hidden_states)
        return hidden_states


class SLANeXtVisionLayer(GradientCheckpointingLayer):
    def __init__(self, config, window_size):
        super().__init__()
        self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attn = SLANeXtVisionAttention(config, window_size)
        self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.mlp = SLANeXtMLPBlock(config)
        self.window_size = window_size

    def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> tuple[torch.Tensor, tuple[int, int]]:
        """
        Args:
        Partition into non-overlapping windows with padding if needed.
            hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window
            size.

        Returns:
            windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel].
            (pad_height, pad_width): padded height and width before partition
        """
        batch_size, height, width, channel = hidden_states.shape

        pad_h = (window_size - height % window_size) % window_size
        pad_w = (window_size - width % window_size) % window_size
        hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h))
        pad_height, pad_width = height + pad_h, width + pad_w

        hidden_states = hidden_states.reshape(
            batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel
        )
        windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel)
        return windows, (pad_height, pad_width)

    def window_unpartition(
        self, windows: torch.Tensor, window_size: int, padding_shape: tuple[int, int], original_shape: tuple[int, int]
    ) -> torch.Tensor:
        """
        Args:
        Window unpartition into original sequences and removing padding.
            hidden_states (tensor):
                input tokens with [batch_size * num_windows, window_size, window_size, channel].
            window_size (int):
                window size.
            padding_shape (Tuple):
                padded height and width (pad_height, pad_width).
            original_shape (Tuple): original height and width (height, width) before padding.

        Returns:
            hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
        """
        pad_height, pad_width = padding_shape
        height, width = original_shape
        batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
        hidden_states = windows.reshape(
            batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1
        )
        hidden_states = (
            hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1)
        )

        hidden_states = hidden_states[:, :height, :width, :].contiguous()
        return hidden_states

    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.FloatTensor]:
        residual = hidden_states
        hidden_states = self.layer_norm1(hidden_states)
        # Window partition
        if self.window_size > 0:
            height, width = hidden_states.shape[1], hidden_states.shape[2]
            hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size)

        hidden_states, attn_weights = self.attn(
            hidden_states=hidden_states,
        )
        # Reverse window partition
        if self.window_size > 0:
            hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width))

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


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for slanext vision model's outputs that also contains image embeddings obtained by applying the projection
    layer to the pooler_output.
    """
)
class SLANeXtVisionEncoderOutput(ModelOutput):
    r"""
    image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
        The image embeddings obtained by applying the projection layer to the pooler_output.
    """

    image_embeds: torch.FloatTensor | None = None
    last_hidden_state: torch.FloatTensor | None = None
    hidden_states: tuple[torch.FloatTensor, ...] | None = None
    attentions: tuple[torch.FloatTensor, ...] | None = None


class SLANeXtPatchEmbeddings(nn.Module):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size
        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)

    def forward(self, pixel_values):
        batch_size, num_channels, height, width = pixel_values.shape
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        if height != self.image_size[0] or width != self.image_size[1]:
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
            )
        embeddings = self.projection(pixel_values).permute(0, 2, 3, 1)
        return embeddings


class SLANeXtLayerNorm(nn.LayerNorm):
    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
    """

    def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs):
        super().__init__(normalized_shape, eps=eps, **kwargs)
        if data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError(f"Unsupported data format: {data_format}")
        self.data_format = data_format

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        """
        Args:
            features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels)
        """
        if self.data_format == "channels_first":
            features = features.permute(0, 2, 3, 1)
            features = super().forward(features)
            features = features.permute(0, 3, 1, 2)
        else:
            features = super().forward(features)
        return features


class SLANeXtVisionNeck(nn.Module):
    def __init__(self, config: SLANeXtVisionConfig):
        super().__init__()
        self.config = config

        self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False)
        self.layer_norm1 = SLANeXtLayerNorm(config.output_channels, data_format="channels_first")
        self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False)
        self.layer_norm2 = SLANeXtLayerNorm(config.output_channels, data_format="channels_first")

    def forward(self, hidden_states):
        hidden_states = hidden_states.permute(0, 3, 1, 2)
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.layer_norm1(hidden_states)

        hidden_states = self.conv2(hidden_states)
        hidden_states = self.layer_norm2(hidden_states)
        return hidden_states


class SLANeXtVisionEncoder(SLANeXtPreTrainedModel):
    _can_record_outputs = {"hidden_states": SLANeXtVisionLayer, "attentions": SLANeXtVisionAttention}
    input_modalities = ("image",)

    def __init__(self, config: SLANeXtVisionConfig):
        super().__init__(config)
        self.config = config
        self.image_size = config.image_size
        self.patch_embed = SLANeXtPatchEmbeddings(config)

        self.pos_embed = None
        if config.use_abs_pos:
            # Initialize absolute positional embedding with pretrain image size.
            self.pos_embed = nn.Parameter(
                torch.zeros(
                    1,
                    config.image_size // config.patch_size,
                    config.image_size // config.patch_size,
                    config.hidden_size,
                )
            )

        self.layers = nn.ModuleList()
        for i in range(config.num_hidden_layers):
            layer = SLANeXtVisionLayer(
                config,
                window_size=config.window_size if i not in config.global_attn_indexes else 0,
            )
            self.layers.append(layer)

        self.neck = SLANeXtVisionNeck(config)

        self.gradient_checkpointing = False
        self.post_init()

    def get_input_embeddings(self):
        return self.patch_embed

    @merge_with_config_defaults
    @capture_outputs(tie_last_hidden_states=False)
    def forward(
        self, pixel_values: torch.FloatTensor | None = None, **kwargs: Unpack[TransformersKwargs]
    ) -> tuple | SLANeXtVisionEncoderOutput:
        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        hidden_states = self.patch_embed(pixel_values)
        if self.pos_embed is not None:
            hidden_states = hidden_states + self.pos_embed
        for layer_module in self.layers:
            hidden_states = layer_module(hidden_states)
        hidden_states = self.neck(hidden_states)
        return SLANeXtVisionEncoderOutput(
            last_hidden_state=hidden_states,
        )


class SLANeXtBackbone(SLANeXtPreTrainedModel):
    def __init__(
        self,
        config: dict | None = None,
        **kwargs,
    ):
        super().__init__(config)
        self.vision_tower = SLANeXtVisionEncoder(config.vision_config)
        self.post_conv = nn.Conv2d(
            config.post_conv_in_channels, config.post_conv_out_channels, kernel_size=3, stride=2, padding=1, bias=False
        )
        self.post_init()

    def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]):
        vision_output = self.vision_tower(hidden_states, **kwargs)
        hidden_states = self.post_conv(vision_output.last_hidden_state)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=vision_output.hidden_states,
            attentions=vision_output.attentions,
        )


class SLANeXtSLAHead(SLANeXtPreTrainedModel):
    _can_record_outputs = {
        "attentions": SLANeXtAttentionGRUCell,
    }

    def __init__(
        self,
        config: dict | None = None,
        **kwargs,
    ):
        super().__init__(config)

        self.structure_attention_cell = SLANeXtAttentionGRUCell(
            config.post_conv_out_channels, config.hidden_size, config.out_channels
        )
        self.structure_generator = SLANeXtMLP(config.hidden_size, config.out_channels)

        self.post_init()

    @merge_with_config_defaults
    @capture_outputs
    @filter_output_hidden_states
    def forward(
        self,
        hidden_states: torch.FloatTensor,
        targets: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        features = torch.zeros(
            (hidden_states.shape[0], self.config.hidden_size), dtype=torch.float32, device=hidden_states.device
        )
        predicted_chars = torch.zeros(size=[hidden_states.shape[0]], dtype=torch.long, device=hidden_states.device)

        structure_preds_list = []
        structure_ids_list = []
        for _ in range(self.config.max_text_length + 1):
            embedding_feature = F.one_hot(predicted_chars, self.config.out_channels).float()
            features, _ = self.structure_attention_cell(features, hidden_states.float(), embedding_feature)
            structure_step = self.structure_generator(features)
            predicted_chars = structure_step.argmax(dim=1)

            structure_preds_list.append(structure_step)
            structure_ids_list.append(predicted_chars)
            if torch.stack(structure_ids_list, dim=1).eq(self.config.out_channels - 1).any(-1).all():
                break
        structure_preds = F.softmax(torch.stack(structure_preds_list, dim=1), dim=-1, dtype=torch.float32).to(
            hidden_states.dtype
        )

        return BaseModelOutput(last_hidden_state=structure_preds, hidden_states=structure_preds_list)


@dataclass
@auto_docstring
class SLANeXtForTableRecognitionOutput(BaseModelOutput):
    r"""
    head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Hidden-states of the SLANeXtSLAHead at each prediction step, varies up to max `self.config.max_text_length` states (depending on early exits).
    head_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
        Attentions of the SLANeXtSLAHead at each prediction step, varies up to max `self.config.max_text_length` attentions (depending on early exits).
    """

    head_hidden_states: torch.FloatTensor | None = None
    head_attentions: torch.FloatTensor | None = None


@auto_docstring(
    custom_intro="""
    SLANeXt Table Recognition model for table recognition tasks. Wraps the core SLANeXtPreTrainedModel
    and returns outputs compatible with the Transformers table recognition API.
    """
)
class SLANeXtForTableRecognition(SLANeXtPreTrainedModel):
    def __init__(self, config: SLANeXtConfig):
        super().__init__(config)
        self.backbone = SLANeXtBackbone(config=config)
        self.head = SLANeXtSLAHead(config=config)
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]
    ) -> tuple[torch.FloatTensor] | SLANeXtForTableRecognitionOutput:
        backbone_outputs = self.backbone(pixel_values, **kwargs)
        head_outputs = self.head(backbone_outputs.last_hidden_state, **kwargs)
        return SLANeXtForTableRecognitionOutput(
            last_hidden_state=head_outputs.last_hidden_state,
            hidden_states=backbone_outputs.hidden_states,
            attentions=backbone_outputs.attentions,
            head_hidden_states=head_outputs.hidden_states,
            head_attentions=head_outputs.attentions,
        )


__all__ = ["SLANeXtSLAHead", "SLANeXtBackbone", "SLANeXtForTableRecognition", "SLANeXtPreTrainedModel"]