# Copyright 2022 SHI Labs 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.
"""PyTorch Dilated Neighborhood Attention Transformer model."""

import math
from dataclasses import dataclass

import torch
from torch import nn

from ...activations import ACT2FN
from ...backbone_utils import BackboneMixin, filter_output_hidden_states
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import (
    ModelOutput,
    OptionalDependencyNotAvailable,
    auto_docstring,
    is_natten_available,
    logging,
    requires_backends,
)
from ...utils.generic import can_return_tuple
from .configuration_dinat import DinatConfig


if is_natten_available():
    from natten.functional import natten2dav, natten2dqkrpb
else:

    def natten2dqkrpb(*args, **kwargs):
        raise OptionalDependencyNotAvailable()

    def natten2dav(*args, **kwargs):
        raise OptionalDependencyNotAvailable()


logger = logging.get_logger(__name__)


# drop_path and DinatDropPath are from the timm library.


@dataclass
@auto_docstring(
    custom_intro="""
    Dinat encoder's outputs, with potential hidden states and attentions.
    """
)
class DinatEncoderOutput(ModelOutput):
    r"""
    reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
        shape `(batch_size, hidden_size, height, width)`.

        Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
        include the spatial dimensions.
    """

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


@dataclass
@auto_docstring(
    custom_intro="""
    Dinat model's outputs that also contains a pooling of the last hidden states.
    """
)
class DinatModelOutput(ModelOutput):
    r"""
    pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
        Average pooling of the last layer hidden-state.
    reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
        shape `(batch_size, hidden_size, height, width)`.

        Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
        include the spatial dimensions.
    """

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


@dataclass
@auto_docstring(
    custom_intro="""
    Dinat outputs for image classification.
    """
)
class DinatImageClassifierOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Classification (or regression if config.num_labels==1) loss.
    logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
        Classification (or regression if config.num_labels==1) scores (before SoftMax).
    reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
        shape `(batch_size, hidden_size, height, width)`.

        Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
        include the spatial dimensions.
    """

    loss: torch.FloatTensor | None = None
    logits: torch.FloatTensor | None = None
    hidden_states: tuple[torch.FloatTensor, ...] | None = None
    attentions: tuple[torch.FloatTensor, ...] | None = None
    reshaped_hidden_states: tuple[torch.FloatTensor, ...] | None = None


class DinatEmbeddings(nn.Module):
    """
    Construct the patch and position embeddings.
    """

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

        self.patch_embeddings = DinatPatchEmbeddings(config)

        self.norm = nn.LayerNorm(config.embed_dim)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, pixel_values: torch.FloatTensor | None) -> tuple[torch.Tensor]:
        embeddings = self.patch_embeddings(pixel_values)
        embeddings = self.norm(embeddings)

        embeddings = self.dropout(embeddings)

        return embeddings


class DinatPatchEmbeddings(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, height, width, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config):
        super().__init__()
        patch_size = config.patch_size
        num_channels, hidden_size = config.num_channels, config.embed_dim
        self.num_channels = num_channels

        if patch_size == 4:
            pass
        else:
            # TODO: Support arbitrary patch sizes.
            raise ValueError("Dinat only supports patch size of 4 at the moment.")

        self.projection = nn.Sequential(
            nn.Conv2d(self.num_channels, hidden_size // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
            nn.Conv2d(hidden_size // 2, hidden_size, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
        )

    def forward(self, pixel_values: torch.FloatTensor | None) -> torch.Tensor:
        _, 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."
            )
        embeddings = self.projection(pixel_values)
        embeddings = embeddings.permute(0, 2, 3, 1)

        return embeddings


class DinatDownsampler(nn.Module):
    """
    Convolutional Downsampling Layer.

    Args:
        dim (`int`):
            Number of input channels.
        norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
            Normalization layer class.
    """

    def __init__(self, dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
        super().__init__()
        self.dim = dim
        self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        self.norm = norm_layer(2 * dim)

    def forward(self, input_feature: torch.Tensor) -> torch.Tensor:
        input_feature = self.reduction(input_feature.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
        input_feature = self.norm(input_feature)
        return input_feature


# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    """
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()  # binarize
    output = input.div(keep_prob) * random_tensor
    return output


# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Dinat
class DinatDropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: float | None = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return drop_path(hidden_states, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return f"p={self.drop_prob}"


class NeighborhoodAttention(nn.Module):
    def __init__(self, config, dim, num_heads, kernel_size, dilation):
        super().__init__()
        if dim % num_heads != 0:
            raise ValueError(
                f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
            )

        self.num_attention_heads = num_heads
        self.attention_head_size = int(dim / num_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.kernel_size = kernel_size
        self.dilation = dilation

        # rpb is learnable relative positional biases; same concept is used Swin.
        self.rpb = nn.Parameter(torch.zeros(num_heads, (2 * self.kernel_size - 1), (2 * self.kernel_size - 1)))

        self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
        self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
        self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_attentions: bool | None = False,
    ) -> tuple[torch.Tensor]:
        batch_size, seq_length, _ = hidden_states.shape
        query_layer = (
            self.query(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )
        key_layer = (
            self.key(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )
        value_layer = (
            self.value(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )

        # Apply the scale factor before computing attention weights. It's usually more efficient because
        # attention weights are typically a bigger tensor compared to query.
        # It gives identical results because scalars are commutable in matrix multiplication.
        query_layer = query_layer / math.sqrt(self.attention_head_size)

        # Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
        attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.kernel_size, self.dilation)

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        context_layer = natten2dav(attention_probs, value_layer, self.kernel_size, self.dilation)
        context_layer = context_layer.permute(0, 2, 3, 1, 4).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        return outputs


class NeighborhoodAttentionOutput(nn.Module):
    def __init__(self, config, dim):
        super().__init__()
        self.dense = nn.Linear(dim, dim)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)

        return hidden_states


class NeighborhoodAttentionModule(nn.Module):
    def __init__(self, config, dim, num_heads, kernel_size, dilation):
        super().__init__()
        self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size, dilation)
        self.output = NeighborhoodAttentionOutput(config, dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_attentions: bool | None = False,
    ) -> tuple[torch.Tensor]:
        self_outputs = self.self(hidden_states, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


class DinatIntermediate(nn.Module):
    def __init__(self, config, dim):
        super().__init__()
        self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class DinatOutput(nn.Module):
    def __init__(self, config, dim):
        super().__init__()
        self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


class DinatLayer(nn.Module):
    def __init__(self, config, dim, num_heads, dilation, drop_path_rate=0.0):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.kernel_size = config.kernel_size
        self.dilation = dilation
        self.window_size = self.kernel_size * self.dilation
        self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
        self.attention = NeighborhoodAttentionModule(
            config, dim, num_heads, kernel_size=self.kernel_size, dilation=self.dilation
        )
        self.drop_path = DinatDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
        self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
        self.intermediate = DinatIntermediate(config, dim)
        self.output = DinatOutput(config, dim)
        self.layer_scale_parameters = (
            nn.Parameter(config.layer_scale_init_value * torch.ones((2, dim)), requires_grad=True)
            if config.layer_scale_init_value > 0
            else None
        )

    def maybe_pad(self, hidden_states, height, width):
        window_size = self.window_size
        pad_values = (0, 0, 0, 0, 0, 0)
        if height < window_size or width < window_size:
            pad_l = pad_t = 0
            pad_r = max(0, window_size - width)
            pad_b = max(0, window_size - height)
            pad_values = (0, 0, pad_l, pad_r, pad_t, pad_b)
            hidden_states = nn.functional.pad(hidden_states, pad_values)
        return hidden_states, pad_values

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_attentions: bool | None = False,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        batch_size, height, width, channels = hidden_states.size()
        shortcut = hidden_states

        hidden_states = self.layernorm_before(hidden_states)
        # pad hidden_states if they are smaller than kernel size x dilation
        hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)

        _, height_pad, width_pad, _ = hidden_states.shape

        attention_outputs = self.attention(hidden_states, output_attentions=output_attentions)

        attention_output = attention_outputs[0]

        was_padded = pad_values[3] > 0 or pad_values[5] > 0
        if was_padded:
            attention_output = attention_output[:, :height, :width, :].contiguous()

        if self.layer_scale_parameters is not None:
            attention_output = self.layer_scale_parameters[0] * attention_output

        hidden_states = shortcut + self.drop_path(attention_output)

        layer_output = self.layernorm_after(hidden_states)
        layer_output = self.output(self.intermediate(layer_output))

        if self.layer_scale_parameters is not None:
            layer_output = self.layer_scale_parameters[1] * layer_output

        layer_output = hidden_states + self.drop_path(layer_output)

        layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
        return layer_outputs


class DinatStage(nn.Module):
    def __init__(self, config, dim, depth, num_heads, dilations, drop_path_rate, downsample):
        super().__init__()
        self.config = config
        self.dim = dim
        self.layers = nn.ModuleList(
            [
                DinatLayer(
                    config=config,
                    dim=dim,
                    num_heads=num_heads,
                    dilation=dilations[i],
                    drop_path_rate=drop_path_rate[i],
                )
                for i in range(depth)
            ]
        )

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(dim=dim, norm_layer=nn.LayerNorm)
        else:
            self.downsample = None

        self.pointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_attentions: bool | None = False,
    ) -> tuple[torch.Tensor]:
        _, height, width, _ = hidden_states.size()
        for i, layer_module in enumerate(self.layers):
            layer_outputs = layer_module(hidden_states, output_attentions)
            hidden_states = layer_outputs[0]

        hidden_states_before_downsampling = hidden_states
        if self.downsample is not None:
            hidden_states = self.downsample(hidden_states_before_downsampling)

        stage_outputs = (hidden_states, hidden_states_before_downsampling)

        if output_attentions:
            stage_outputs += layer_outputs[1:]
        return stage_outputs


class DinatEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.num_levels = len(config.depths)
        self.config = config
        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths), device="cpu")]
        self.levels = nn.ModuleList(
            [
                DinatStage(
                    config=config,
                    dim=int(config.embed_dim * 2**i_layer),
                    depth=config.depths[i_layer],
                    num_heads=config.num_heads[i_layer],
                    dilations=config.dilations[i_layer],
                    drop_path_rate=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
                    downsample=DinatDownsampler if (i_layer < self.num_levels - 1) else None,
                )
                for i_layer in range(self.num_levels)
            ]
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        output_attentions: bool | None = False,
        output_hidden_states: bool | None = False,
        output_hidden_states_before_downsampling: bool | None = False,
        return_dict: bool | None = True,
    ) -> tuple | DinatEncoderOutput:
        all_hidden_states = () if output_hidden_states else None
        all_reshaped_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if output_hidden_states:
            # rearrange b h w c -> b c h w
            reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
            all_hidden_states += (hidden_states,)
            all_reshaped_hidden_states += (reshaped_hidden_state,)

        for i, layer_module in enumerate(self.levels):
            layer_outputs = layer_module(hidden_states, output_attentions)

            hidden_states = layer_outputs[0]
            hidden_states_before_downsampling = layer_outputs[1]

            if output_hidden_states and output_hidden_states_before_downsampling:
                # rearrange b h w c -> b c h w
                reshaped_hidden_state = hidden_states_before_downsampling.permute(0, 3, 1, 2)
                all_hidden_states += (hidden_states_before_downsampling,)
                all_reshaped_hidden_states += (reshaped_hidden_state,)
            elif output_hidden_states and not output_hidden_states_before_downsampling:
                # rearrange b h w c -> b c h w
                reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
                all_hidden_states += (hidden_states,)
                all_reshaped_hidden_states += (reshaped_hidden_state,)

            if output_attentions:
                all_self_attentions += layer_outputs[2:]

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)

        return DinatEncoderOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            reshaped_hidden_states=all_reshaped_hidden_states,
        )


@auto_docstring
class DinatPreTrainedModel(PreTrainedModel):
    config: DinatConfig
    base_model_prefix = "dinat"
    main_input_name = "pixel_values"
    input_modalities = ("image",)


@auto_docstring
class DinatModel(DinatPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        r"""
        add_pooling_layer (bool, *optional*, defaults to `True`):
            Whether to add a pooling layer
        """
        super().__init__(config)

        requires_backends(self, ["natten"])

        self.config = config
        self.num_levels = len(config.depths)
        self.num_features = int(config.embed_dim * 2 ** (self.num_levels - 1))

        self.embeddings = DinatEmbeddings(config)
        self.encoder = DinatEncoder(config)

        self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
        self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None

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

    def get_input_embeddings(self):
        return self.embeddings.patch_embeddings

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | DinatModelOutput:
        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
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        embedding_output = self.embeddings(pixel_values)

        encoder_outputs = self.encoder(
            embedding_output,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)

        pooled_output = None
        if self.pooler is not None:
            pooled_output = self.pooler(sequence_output.flatten(1, 2).transpose(1, 2))
            pooled_output = torch.flatten(pooled_output, 1)

        if not return_dict:
            output = (sequence_output, pooled_output) + encoder_outputs[1:]

            return output

        return DinatModelOutput(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
        )


@auto_docstring(
    custom_intro="""
    Dinat Model transformer with an image classification head on top (a linear layer on top of the final hidden state
    of the [CLS] token) e.g. for ImageNet.
    """
)
class DinatForImageClassification(DinatPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        requires_backends(self, ["natten"])

        self.num_labels = config.num_labels
        self.dinat = DinatModel(config)

        # Classifier head
        self.classifier = (
            nn.Linear(self.dinat.num_features, config.num_labels) if config.num_labels > 0 else nn.Identity()
        )

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

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | DinatImageClassifierOutput:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.dinat(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        logits = self.classifier(pooled_output)

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

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return DinatImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            reshaped_hidden_states=outputs.reshaped_hidden_states,
        )


@auto_docstring(
    custom_intro="""
    NAT backbone, to be used with frameworks like DETR and MaskFormer.
    """
)
class DinatBackbone(BackboneMixin, DinatPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        requires_backends(self, ["natten"])

        self.embeddings = DinatEmbeddings(config)
        self.encoder = DinatEncoder(config)
        self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]

        # Add layer norms to hidden states of out_features
        hidden_states_norms = {}
        for stage, num_channels in zip(self.out_features, self.channels):
            hidden_states_norms[stage] = nn.LayerNorm(num_channels)
        self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)

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

    def get_input_embeddings(self):
        return self.embeddings.patch_embeddings

    @can_return_tuple
    @filter_output_hidden_states
    @auto_docstring
    def forward(
        self,
        pixel_values: torch.Tensor,
        output_hidden_states: bool | None = None,
        output_attentions: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> BackboneOutput:
        r"""
        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, AutoBackbone
        >>> import torch
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))

        >>> processor = AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224")
        >>> model = AutoBackbone.from_pretrained(
        ...     "shi-labs/nat-mini-in1k-224", out_features=["stage1", "stage2", "stage3", "stage4"]
        ... )

        >>> inputs = processor(image, return_tensors="pt")

        >>> outputs = model(**inputs)

        >>> feature_maps = outputs.feature_maps
        >>> list(feature_maps[-1].shape)
        [1, 512, 7, 7]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        embedding_output = self.embeddings(pixel_values)

        outputs = self.encoder(
            embedding_output,
            output_attentions=output_attentions,
            output_hidden_states=True,
            output_hidden_states_before_downsampling=True,
            return_dict=True,
        )

        hidden_states = outputs.reshaped_hidden_states

        feature_maps = ()
        for stage, hidden_state in zip(self.stage_names, hidden_states):
            if stage in self.out_features:
                batch_size, num_channels, height, width = hidden_state.shape
                hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
                hidden_state = hidden_state.view(batch_size, height * width, num_channels)
                hidden_state = self.hidden_states_norms[stage](hidden_state)
                hidden_state = hidden_state.view(batch_size, height, width, num_channels)
                hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
                feature_maps += (hidden_state,)

        if not return_dict:
            output = (feature_maps,)
            if output_hidden_states:
                output += (outputs.hidden_states,)
            return output

        return BackboneOutput(
            feature_maps=feature_maps,
            hidden_states=outputs.hidden_states if output_hidden_states else None,
            attentions=outputs.attentions,
        )


__all__ = ["DinatForImageClassification", "DinatModel", "DinatPreTrainedModel", "DinatBackbone"]
